El Bechir Msaddek
email: elbechir.msaddek@gmail.com

• Maher Baili
email: maher.baili@enit.fr

• Zoubeir Bouaziz
email: zoubeir.bouaziz@enis.rnu.tn

Gilles Dessein
email: gilles.dessein@enit.fr

Monica Carvalho

Maher Baili

Zoubeir Bouaziz

Surface roughness perfection by CAM interpolations

Keywords: Roughness, CAM interpolations, Free-form, Simulation, HSM

The surface quality of the parts is one of the major objectives of the use of the high-speed milling (HSM). Tool path interpolations affect the machining operation of complex parts in HSM. In this paper, the influence of the different types of interpolation of the computer-aided manufacturing (CAM) on the surface roughness in the HSM machining of free forms is studied. To do this, a simulation tool of the surface texture for each type of interpolation is proposed. Then, experimental measurements expressing surfaces condition are carried out. To validate our approach, theoretical results with experimental ones are compared. As results, CAM interpolations of tool paths have demonstrated an important impact on the surface texture. In fact, a simulation prediction tool has confirmed the advantages of using polynomial interpolations in order to ameliorate the quality of the machined surface.

Introduction

The surface condition is an essential criterion for judging the quality of a mechanical part. The roughness of the surface is one of the most important requirements in the high-speed machining process. Indeed, it depends on four families of parameters: the cutting parameters (depth of cut, cutting speed …), the generated phenomena of cutting (vibration, shape and aspect of the chip …), the properties of the tool (materials, shape …), and the properties of the part (hardness, shape…) [START_REF] Slamani | Comparison of surface roughness quality obtained by high speed CNC trimming and high speed robotic trimming for CFRP laminate[END_REF]. High-speed machining (HSM) has very interesting characteristics in the production of mechanical parts with the best quality in automobiles and aeronautics [START_REF] Yih-Fong | Dimensional quality optimisation of high-speed CNC milling process with dynamic quality characteristic[END_REF]. Thus, with the HSM machining, it is possible to remove the maximum of material in a minimum of time [START_REF] Nicolas | Lobes de stabilité en UGV approche expérimentale en usinage de poches[END_REF]. The cost of the part is then reduced [START_REF] Bouaziz | Methodology of machining costs evaluation for die and mould manufacturing[END_REF], since the machining time represents an important factor of the cost price.

Several parameters influence the surface roughness in machining of free forms in HSM. So, we find numerous methods of prediction of the surfaces textures in order to assess and ameliorate the part quality. This work will focus in the impact of the interpolation type on the surface roughness in HSM.

Literature review

Prediction of the surface roughness

Recent studies have investigated several methods of prediction of surface roughness and parameters which affect the surface condition for the machining of complex shapes in HSM. Some authors have used traditional methods to treat this subject. Peigne et al. [START_REF] Peigne | Impact of the cutting dynamics of small radial immersion milling operations on machined surface roughness[END_REF] have focused their study on vibratory cutting phenomena and their impacts on the roughness of the machined surface. They have shown that forced vibrations (stable cutting) have only a very small impact on roughness. Ozcelik et al. [START_REF] Ozcelik | The statistical modeling of surface roughness in high-speed flat end milling[END_REF] have developed a statistical model for estimating Ra for high-speed milling, considering the influence of machining parameters such as spindle speed, feed rate, depth of cut, max. step-over and the tool wear. De Aguiar et al. [START_REF] Mendes | Correlating surface roughness, tool wear and tool vibration in the milling process of hardened steel using long slender tools[END_REF] have analyzed the surface roughness for the milling of flat inclined surfaces. This study has combined the influence of the orientation, the geometry and kinematic parameters in the roughness definition. They have shown that the passage frequency of the teeth used in the milling process has a significant impact on the surface roughness. Karagiannis et al. [START_REF] Stefanos | Prediction of surface roughness magnitude in computer numerical controlled end milling processes using neural networks, by considering a set of influence parameters: an aluminium alloy 5083 case study[END_REF] have focused on the prediction of Aluminum alloy 5083 component surface quality, achieved inside end milling. Process performance is estimated using the statistical surface texture parameters R α , R y and R z measured during three different passes on the side surface of the pockets. The results indicate that process parameters-such as the cutting speed, the peripheral second relief angle and the core diameter-mostly influence surface texture. Bernardos and Vosniakos [START_REF] Benardos | Predicting surface roughness in machining: a review[END_REF] have presented and classified the various methodologies and practices that are being employed for the prediction of surface roughness.

Other authors have used special methods to resolve this issue. Zain et al. [START_REF] Zain | Prediction of surface roughness in the end milling machining using Artificial Neural Network[END_REF] have applied the ANN (Artificial Neural Network) method to predict the measurement of the surface roughness in the machining process. The recommended combination of cutting conditions is used to obtain the best surface roughness value with a low feed rate and a radial draft angle. Arapoglu et al. [START_REF] Arapoğlu | An ANN-based method to predict surface roughness in turning operations[END_REF] have investigated a new alternative parameter selection method based on artificial neural networks ANN in order to predict the surface roughness in turning operations effectively. Zhang et al. [START_REF] Zhang | Prediction of surface roughness in end face milling based on Gaussian process regression and cause analysis considering tool vibration[END_REF] have proposed the Gaussian process regression (GPR) for modeling and predicting the surface roughness in end face milling. Gao et al. [START_REF] Gao | Prediction of Surface Roughness Using Regression and ANN Models in High-Speed Finish Milling Operation[END_REF] have developed multi-linear regression and artificial neural network (ANN) models to predict surface roughness in high-speed milling of 7050-T7451 aluminum alloy. Razfar et al. [START_REF] Razfar | Optimum surface roughness prediction in face milling by using neural network and harmony search algorithm[END_REF] have presented a new approach to determine the optimal cutting parameters leading to minimum surface roughness in face milling of X20Cr13 stainless steel by coupling artificial neural network (ANN) and harmony search algorithm (HS). Antoniadis et al. [START_REF] Antoniadis | Prediction of surface topomorphy and roughness in ball-end milling[END_REF] have presented a novel simulation model, the so-called MSN-Milling Software Needle program, which is able to determine the surface produced and the resulting surface roughness, for ball end milling. Suresh et al. [START_REF] Suresh | A genetic algorithmic approach for optimization of surface roughness prediction model[END_REF] have studied and developed a surface roughness prediction model for machining mild steel, using response surface methodology (RSM) and they have optimized the surface roughness prediction model using genetic algorithms (GA). Fuh and Wu [START_REF] Fuh | A proposed statistical model for surface quality prediction in end-milling of Al alloy[END_REF] have used RSM to study the influence of tool geometries and cutting parameters on surface roughness in end milling of Al alloy. The results have shown that the surface roughness is affected by the tool nose radius and feed rate. According to the previous research works, we conclude that several parameters influence the surface quality. Among these parameters we can quote; vibratory cutting, machining parameters, passage frequency of the teeth, tool geometry and orientation, and kinematic parameters. So, to predict the surface roughness, authors have used several methods such as artificial neural network (ANN), Gaussian process regression (GPR), MSN-Milling Software Needle, response surface methodology (RSM), and genetic algorithms (GA).

Surface roughness and tool paths

The perfection of the surface roughness requires the use of several machining strategies, because the tool paths generated by the CAM systems influence the mechanical and metallurgical properties of the machined surfaces. De Souza et al. [START_REF] De Fagalisouza | Evaluating the roughness according to the tool path strategy when milling free form surfaces for mold application[END_REF] have evaluated the roughness according to the tool path strategy when milling free-form surfaces for mold application. Lestari et al. [START_REF] Lestari | The influence of tool path strategies on surface roughness and machining time in the CNC milling UHMWPE[END_REF] have analyzed the influence of tool path strategies on surface roughness and machining time in the CNC milling UHMWPE. Shajari et al. [START_REF] Shajari | The influence of tool path strategies on cutting force and surface texture during ball end milling of low curvature convex surfaces[END_REF] have studied the influence of tool path strategies on cutting force and surface texture during ball end milling of low curvature convex surfaces (see Fig. 1).

In general, radial cutter path seems to deliver favorite results in terms of more uniform texture and lower cutting forces. In contrary, resultant cutting force is the highest when using spiral tool path because of the tool-chip contact area. As a result, poor surface texture is found.

Etyemez [START_REF] Etyemez | Optimization of effects of pocket tool path strategies and cutting parameters on surface quality[END_REF] has optimized effects of pocket tool path strategies and cutting parameters on surface quality. Mebrahitom et al. [START_REF] Mebrahitom | Effect of High-speed Milling tool path strategies on the surface roughness of Stavax ESR mold insert machining[END_REF] have investigated the effect of high-speed milling tool path strategies on the surface roughness of Stavax ESR mold insert machining. Systems used in evaluation of surface roughness are digital microscope, handheld rugosimeter and 3D profilometry [START_REF] Nanovea | Surface roughness statistical analysis using 3D profilometry[END_REF]. Magalhães and Ferreira [START_REF] Magalhães | Assessment of tool path strategies for milling complex surfaces in hardened H13 steel[END_REF] have evaluated tool path strategies impact for milling complex surfaces in hardened H13 steel on the surface roughness. They have found that the roughness of the complex surface depends significantly on the tool path used and is significantly impaired by the increase in the feed per tooth. Dadgari et al. [START_REF] Dadgari | The effect of machining toolpath on surface roughness and dimensional accuracy for highspeed micro milling[END_REF] have examined the effect of machining tool path on surface roughness and dimensional accuracy for high-speed micro milling. De Souza et al. [START_REF] De Souza | Study of tool paths calculated by different commercial CAM systems and influences on the real machining time and surface roughness for milling free-form geometries[END_REF] have studied tool paths calculated by different commercial CAM systems and influences on the real machining time and surface roughness for milling free-form geometries (see Fig. 2).

Figure 2 shows the roughness profile and the real feed rate for two areas for the extremes of the roughness results, CAM E (best) and CAM A (worst). The relation between the feed oscillation and the surface roughness can be observed. Authors have confirmed that it is probable that such unevenness on the distribution of segments caused feed oscillation, resulting in poor surface roughness. Scandiffio et al. [START_REF] Scandiffio | Evaluating surface roughness, tool life, and machining force when milling free-form shapes on hardened AISI D6 steel[END_REF] have evaluated the surface roughness, tool life, and machining force when milling free-form shapes on hardened AISI D6 steel. Kull Neto et al. [START_REF] Neto | Influence of tooth passing frequency, feed direction, and tool overhang on the surface roughness of curved surfaces of hardened steel[END_REF] have estimated the influence of tooth passing frequency, feed direction, and tool overhang on the surface roughness of curved surfaces of hardened steel. They have concluded that feed direction was the input variable that most affected surface roughness. Hioki et al. [START_REF] Hioki | Influence of HSM cutting parameters on the surface integrity characteristics of hardened AISI H13 steel[END_REF] have shown the influence of HSM cutting parameters (depth of cut a p , working engagement a e and feed per tooth f z ) on the surface integrity characteristics of hardened AISI H13 steel. They have confirmed that the working engagement (a e ) was the cutting parameter that exerted the strongest effect on most of the 3D roughness parameters (see Fig. 3).

Da Costa et al. [START_REF] Da Costacastanhera | Effects of tool path strategies and thermochemical treatments on the surface roughness of hardened punches for hot stamping[END_REF] have presented effects of tool path strategies and thermochemical treatments on the surface roughness of hardened punches for hot stamping. The used milling strategies were circular (upward and downward), radial (upward and downward), parallel contours and spiral (upward). Figure 4 presents an example of the influence Fig. 2 Roughness profiles obtained by several CAM systems tool paths [START_REF] De Souza | Study of tool paths calculated by different commercial CAM systems and influences on the real machining time and surface roughness for milling free-form geometries[END_REF] of strategies on roughness before the thermochemical treatments.

Most authors have not enough studied the influence of the type of interpolation on the surface condition. However, experience has shown that programming methods affect surface texture. Among these methods, the different types of interpolations which affect the parameters and the phenomena of cutting (machining step-over, tool orientation, feed rate, vibration …). So, it is interesting to introduce the surface texture criterion in the choice of the machining strategy and the type of interpolation to be used.

In previous research works, authors have investigated the influence of the interpolation type on the surface roughness only by experimental works [START_REF] El Bechir Msaddek | Influence of interpolation type in high-speed machining (HSM)[END_REF]. After that, they have examined the influence of the compensation method of machining errors in Bspline and Cspline on the surface condition [START_REF] Msaddek | Compensation of machining errors of Bspline and Cspline[END_REF]. The aim of this article is to simulate the influence of the different types of interpolation on the surface condition in addition with experimental works done. To do this, a simulation tool is developed and checked by experimental measurements. (Table 1)

Types of interpolations

The CAM software provides several types of interpolation in the machining. Indeed, in order to improve the roughness of the machining surfaces in HSM, we have chosen different types of programming for our study [START_REF] Siemens | Manual of programming-complementary notions[END_REF][START_REF] Siemens | Polynomial functions and Nurbs[END_REF]. The CAM interpolations used are summarized in the following table:

Methodology and characterization of the workpiece

The designed workpiece (see Fig. 5) contains most of shapes which can be found in an industrial part. In order to accurately evaluate the surface condition after the HSM machining of the workpiece, diversified interpolations will be used.

The workpiece is divided into 8 bands corresponding to 8 types of interpolations: linear, Aspline, Bspline, Cspline, Nurbs, Compon, Compcad, and Compcurv. The Bspline (Bezier Spline) is a polynomial of degree 3 or higher. The programmed positions are not points of the curve, but only "control points" of the spline, so the curve does not go directly through these points, but that it "tends" towards them. A "control polygon" of the spline is created connecting the control points.

Cspline

The Cspline (Cubic Spline) is an interpolation by a third-degree polynomial. The latter passes exactly through the intermediate points (P1 to P7). However, Cspline has a strong tendency to oscillations between these points. The modification of the Cspline is with global effect (is not local) that is to say that the modification of a point influences many blocks (with a decreasing intensity of variation, from the closest point to the less close point).

Nurbs

Nurbs (non-uniform rational Bspline) is a polynomial interpolation of the Bspline type (Bezier model). It is an approximation curve: does not pass through the control points. It is stable.

The modification of the Nurbs is with local effect. The distribution of the Nurbs nodes is not homogeneous (Non Uniform). Thus, these points have a weight (Rational) that contributes in the calculation of the entire curve.

Linear + compaction

Compon

This function groups together a series of blocks (number limited to 10) and realizes their approximation by a third degree polynomial with a tolerance to specify. At the transition between blocks, the speed is continuous and the acceleration is in echelon form (oscillations on the machine).

-- --

Compcad

Modeling and simulation of surface texture with different types of interpolation

The simulation of the surface texture for each interpolation is done in two steps:

-The first step is to simulate the tool path with the interpolation studied on longitudinal direction for many continuous passes (zigzag strategy) (see Fig. 6).

-The second step is to simulate the profile on transversal direction defining the CAM discretization (points of each interpolation) in the sense of the advance (see Fig. 7).

So, the surface texture according to the combination of the two trajectories for each interpolation is determined. To avoid condensing the article, only surface simulations for linear, Bspline, and Cspline interpolations will be presented.

Figure 8 shows the surface texture of the linear interpolation.

Figure 9 shows the surface texture of the polynomial interpolation "Bspline".

Figure 10 shows the surface texture of the polynomial interpolation "Cspline".

Figure 11 shows zooms of surface textures of interpolations: "linear," "Bspline" and "Cspline".

Previous figures show the variation of surface texture according to the type of interpolation. The discretization of

Experimental validation

The realization of the workpiece consists in machining of 8 bands (10 mm of width for each band) in the HSM machine After the part machining, experimental verifications of the influence of each type of interpolation on the surface condition are followed. In order to see and measure the surface roughness, the following apparatus were used: Huron KX10 (Figs. 12 and13).

Table 2 shows the parameters of the finishing operation of the test part.

• Digital microscope (Dino-Lite) max. zoom = 90.

• Handheld rugosimeter (Mahr), Rz = 9.72 μm.

To assess the roughness, a similar area to measure in 8 bands has been chosen. Then, the roughness has been measured by the rugosimeter using as indicator "Ra." So, in order to avoid measurement errors, operations have been repeated several times until having sure measurements.

Figure 13 shows the workpiece machined in finishing operation by different types of interpolation and the capture Fig. 11 Zooms of Simulated surface textures of interpolations: "linear," "Bspline" and "Cspline" Fig. 12 HSM machine HURON KX10 and tool "Ball end mill" in high-speed steel (HSS) and measurement area selected to distinguish the influence of each interpolation on the surface condition. Almost flat area that can be measured by the handheld rugosimeter is chosen.

Figure 14 shows surface conditions, captured in zoom by the digital microscope, for each type of interpolation.

According to Fig. 14, the interpolation type has an immense impact on the surface quality. The surface protrusions generated in the linear interpolation are larger than those generated in polynomial interpolations: Nurbs, Aspline, Bspline and Cspline. Therefore, polynomial interpolations generate best surface conditions, compared with the linear interpolation. The Nurbs interpolation has the minimum value of roughness Ra. On the contrary, the surface condition of the Bspline interpolation is the bad one, because its trajectory does not pass through knots (control points). So, errors and vibrations increase during machining, which negatively influence the surface condition. Thus, compaction functions generate surfaces with poor quality, since they are derived from the linear interpolation.

Figure 15 shows the roughness histogram for different types of interpolation on the chosen area.

Nurbs and Cspline interpolations have the lowest roughness values (Ra ≈ 0.6 μm). In general, the machining based on the polynomial interpolation generates a better surface condition than the linear interpolation. The use of polynomial interpolations leads to a gain in the machining time as well as the surface quality. However, these types of interpolation generate more machining errors.

Figure 16 shows simulated and measured surface textures of interpolations: "linear," "Bspline" and "Cspline" on the chosen area.

According to previous results, the surface texture of the linear interpolation is the least congested, compared with the Bspline and the Cspline interpolations. The simulation tool proves that the surface condition of polynomial interpolations is better than that of the linear interpolation. The main origin of this difference is the numerical discretization of each interpolation. The precision of the trajectories increases the number of knots, and consequently, it increases the surface protrusions and the machining time.

Figure 17 shows simulated and measured surface texture details of "linear" interpolation:

During machining, the tool does not follow exactly the theoretical path. Tool deflections are generated by the cutting forces and other parameters [START_REF] Smaoui | Simulation of the deflected cutting tool trajectory in complex surface milling[END_REF]. They present the main cause of the difference between simulated and obtained surface texture. Besides, steps along the axis X are the displacements between points of discretization. So, they have not any other impact on results. However, max. step-over along the axis Y have an impact on the surface roughness [START_REF] Hioki | Influence of HSM cutting parameters on the surface integrity characteristics of hardened AISI H13 steel[END_REF] but we will have not any modification in comparison between interpolations and their impacts on the surface roughness, because the most important parameter is the discretization relied to the manner of interpolations writing and implementation.

Conclusion

The surface condition constraint is essential for defining an optimal machining strategy in finishing process. In this article, we have focused on modeling and simulation of the surface texture obtained after machining of a complex shape by different types of CAM interpolations. The objective is to obtain a good prediction of a given trajectory and its generated surface texture. In the beginning, a global program of the simulation process has been specified. Then, simulation results of the surface texture for linear, Bspline and Cspline interpolations have been presented. Finally, experimental measurements have been realized in order to show the influence of the interpolation on the surface condition and in order to validate our prediction model of surface textures.

The results have proven the benefits of using polynomial interpolations on the surface condition. Indeed, polynomial interpolations of different types have given good surface quality, unlike the linear interpolation and its derivatives (compaction functions).

This paper opens diverse perspectives about the relationship between the machining quality and the interpolation type. Several mathematical and geometric approaches could be developed in order to satisfy this criterion.

Fig. 1

 1 Fig. 1 Texture obtained for strategies: a raster, b 3D-offset, c radial, and d spiral[START_REF] Shajari | The influence of tool path strategies on cutting force and surface texture during ball end milling of low curvature convex surfaces[END_REF]

Fig. 3 Fig. 4

 34 Fig.3Milled surfaces with several conditions[START_REF] Hioki | Influence of HSM cutting parameters on the surface integrity characteristics of hardened AISI H13 steel[END_REF]

Fig. 5 CADFig. 6

 56 Fig. 5 CAD Workpiece divided into eight bands

Fig. 7 Fig. 8

 78 Fig. 7 Simulated path of the CAM discretization of the linear interpolation

Fig. 9 Fig. 10

 910 Fig. 9 Simulated surface texture of the "Bspline" interpolation

Fig. 13

 13 Fig. 13 Workpiece machined in finishing operation by different types of interpolation and Area of capture and measurement of the surface condition

Fig. 14

 14 Fig. 14 Surface roughness of each type of interpolation

Fig. 15 Fig. 16 Fig. 17

 151617 Fig. 15 Roughness (Ra) for different types of interpolation

  Fig. 17Simulated and measured surface texture details

Table 1

 1 CAM interpolations

	intermediate points. Moreover, the
	displacement of a point does not modify
	more than 6 neighboring points.
	Bspline

Table 2

 2 Cutting parameters in finishing operation

						Ra=1.647 µm	Ra=0.610 µm
						Ra=0,791 µm	Ra=0,845 µm
	Programmed	Spindle	Workpiece	Tool	CAM	Ra=0,647 µm	Ra=1,254 µm
	feed rate	speed	material		tolerance
	4.8 m/min	24,000 rpm Aluminum	Ball end	10 µm
				mill Ø6
						Ra=1,558 µm	Ra=1,406 µm

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V Wagner

M Harzallah

M Baili

G Dessein

D Lallement

Experimental and numerical investigations of the heating influence on the Ti5553 titanium alloy machinability

Keywords:

The use of titanium alloys has become more widespread in recent years. Regardless, although research work is enhancing, productivity levels remain low. Several alternatives have been considered and one of the techniques is envisaged is heating assistance. The objective is then to increase the temperature locally in order to benefit from thermal softening. However, the research is based on power-controlled laser assistance where the temperature levels are not considered. For this purpose, this article suggests a study based on a new experimental device which allows to control the temperature of the machined part. After presenting the device and the material used (Ti5553), the study first shows the effect of temperature on cutting forces. Three temperature ranges and three trends are shown. A first where temperature has no effect, a second when there is a substantial gain and a third where temperature has a real impact. In order to understand the existing links, two methods were used. A finite element model integrating a new law of behaviour was developed and shows the very high temperature levels reached in the cutting areas. Then, to better understand the phenomena governing cutting, we looked at chip formation and the link between chip formation, temperature and its effect on the microstructure. After all, an economic analysis completes this article and highlights the small gains of this assistance.

Introduction

Ti5553 titanium alloy is increasingly used and its machining stays always a problematic. In fact, its low thermal conductivity combined with its microstructure and its high mechanical properties make it a low machinability alloy [START_REF] Arrazola | Machinability of titanium alloys (Ti6Al4V and Ti555. 3)[END_REF]. Recently, several studies have been conducted to investigate the Ti5553 machinability and thus, to enhance the productivity, the tool life and the surface integrity. Since the limitations of Ti5553 conventional machining (without assist), several assistance techniques have been applied to achieve these goals. These techniques can be sorted into two groups. The first group intended to cool the workpiece to decrease the temperature at the tool/workpiece interface [START_REF] Sun | Thermally enhanced machining of hard-to-machine materials-a review[END_REF][START_REF] Machado | Tool performance and chip control when machining Ti6A14V and INCONEL 901 using high pressure coolant supply[END_REF]. employed the high-pressure water jet assistance during Ti-6Al-4 V titanium alloy machining. They found an increase of about 300 % in tool life compared to dry machining. Using the same technologie [START_REF] Braham-Bouchnak | Influence of high-pressure coolant assistance on the machinability of the titanium alloy Ti555-3[END_REF], registered an increase of 185 % on the tool life when machining the Ti555-3 alloy. The cryogenic assistance is also widely used in machining and, especially for titanium alloy. Most cryogenic machining studies have shown improved machinability when freezing the workpiece or cooling the tool using a cryogenic coolant [START_REF] Sun | Thermally enhanced machining of hard-to-machine materials-a review[END_REF]. The second group focused on heating of the workpiece to decrease its mechanical properties. Hot machining has received the attention of researchers in the metal cutting domain and a few research was carried out during the recent years [START_REF] Ayed | Experimental and numerical study of laser-assisted machining of Ti6Al4V titanium alloy[END_REF]. employed the Laser-assisted technique (LAM) during machining of Ti-6Al-4 V titanium alloy. The authors noted a high reduction of cutting force which can exceed the 50 %. An economic analysis is carried out by [START_REF] Anderson | Laser-assisted machining of Inconel 718 with an economic analysis[END_REF] during the Inconel 718 machining. They demonstrated that the LAM technique can provide a strong reduction in cutting forces, a strong improvement in surface roughness (2 at 3 times) and a 200-300 % increase of tool life of ceramic tools over conventional machining. However, to make thermally assisted machining more attractive some attempts use a localised heat sources such as plasma and laser. Although laser assistance could increase the machinability of hard-to-cut materials, but it presents some disadvantages like the efficiency, the high-power consumption, the human security and the high cost. Indeed, many researchers used conventional heat sources such as furnaces, torches or induction technique for the workpiece heating [START_REF] Komanduri | Highlights of the DARPA advanced machining research program[END_REF] and more recently [START_REF] Kaynak | A comparison of flood cooling, minimum quantity lubrication and high pressure coolant on machining and surface integrity of titanium Ti-5553 alloy[END_REF]. By means of gas flame heat machining technique [START_REF] Thandra | Effect of cutting parameters on cutting force, surface finish and tool wear in hot machining[END_REF] analyse the effect of the machining parameters on the surface integrity, the cutting forces and the tool life. They show an improvement on the surface finish and a decrease on the cutting forces.

It was observed a decrease of 33.95 % flank wear compared to mm turning operation is performed.

Material of study

All tests were performed on titanium alloys Ti5553. The chemical composition is Aluminum 5 %, Molybdenum 5 %, Vanadium 5 % and Chromium 3 %. This alloys is a beta-metastable structure and it is a part of the near-beta-alloys family of titanium. The titanium used in this work is firstly heat-treated and after forged. The heat treatment is divided into three parts :

1 heating to 800 °C during 4 h, 2 free cooling, 3 aging at 600 °C for 8 h

The resulting microstructure is shown Fig. 3. The difference between the two phases of titanium can be observed in using a spectral analysis technic. The lowest concentration of aluminium is found in the α phase. During the heat treatment, a combination of the α and β phases results in some nodules α p, which coalesce during the tempering. This last treatment also generates fine α s precipitates in the β matrix. As shown in this figure, the size of the beta phase is large ( = µm 500

). EBSD (Electron Backscatters Diffraction) analysis correlates with observations made by [START_REF] Martin | Numerical multiscale simulation of the mechanical behavior of betametastable titanium alloys Ti5553 and Ti17[END_REF].

Cutting conditions

The cutting speed (Vc = 35 m/min), the feed is (0,1 m/rev), the radial depth (ap = 3 mm) are steady. This cutting speed was chosen according to the Tool Material Pair methodology but made at room temperature [START_REF] Iso | ISO8688-1 -tool life testing in milling, part 1 : face milling[END_REF]. The insert used is CNMG 160612 -QM1105 (rake angle = 13°, backing edge of 0.12 mm at 13°combined with a honing of 0.02 mm).

Experimental results

Cutting forces

Fig. 4 exhibits the evolution of the cutting forces according to the temperature of the workpiece. Four values occur : Fc, Ft, Fa and R which are respectively the component co-linear to the cutting speed, to the feed and to the cutting edge. After all, R is the cutting force resultant. The decrease in the percentage according to the initial value is shown on the graph with the blue dot line. The evolution of cutting forces describes three tendencies according to temperature ranges. The first one is between the room temperature and 100 °C. No noticeable effect on the heating of the measured forces were detected. All Fig. 1. Experimental set-up description. conventional machining. [START_REF] Macy | Heating device for hot machning apparatus[END_REF] employed a dedicated induction system for hot turning. The system is based on an improved induction coil which is adapted to heat a localised portion of the machined part. Using the same technique [START_REF] Ginta | Improved tool life in end milling Ti6Al4V through workpiece preheating[END_REF] investigated the tool life improvement in milling of Titanium Alloy Ti-6Al-4 V through workpiece preheating. The authors concluded that with preheating at 650 °C it is possible to increase the tool life by 2.7 times compared to the experiment tests made at room temperature.

The aim of this study is to improve the machinability of the Ti5553 titanium alloy with the assistance of high frequency induction heating. Experimental and numerical investigations is carried out at various preheating temperatures. The paper presents firstly, the developed experimental set-up that used for workpiece preheating. Then, the experimental tests are described and the influence of the hot machining on the cutting forces as well as the chip morphology is discussed. By means of 3D FE orthogonal cutting model, the chip formation process is simulated and compared to experimental observations. Moreover, a discussion is made regarding the hot machining influence on the chip morphology and the material microstructure. After all, the economic benefits and limits of this assistance is discussed.

Experimental set-up

For the workpiece heating, a high frequency induction system has been used and adapted to the turning operation. The heating device is defined by a semicircular inductor which envelops the piece.

The part is divided in two with a first mounted in the machine mandrill and a second to be machined. This is fixed in a specific rotating component which is designed to integrate thermocouples. The temperature of the workpiece is controlled by a thermal camera and 4 thermocouples positioned as described in Fig. 1. It should be noticed, the signal recoveries from thermocouples are carried through a rotating collector which is fixed on the chuck of the lathe.

Fig. 2 exhibits the temperature evolution during a test in regarding the thermocouples T2 and T3 positioned differently. T2 is close to the radial depth and to the part extremity contrary to T3 which is deeper radially and axially. The temperature shown in the next section is an average between the thermocouple's and the IR camera's measures.

The preheating cycle is based in two phases. The first consists of the workpiece heating during its rotation until achieving the preselected temperature. During this phase, the part turns to get a homogeneous temperature. At the end of the first phase where the temperature desired is partially obtained, the part rotation is stopped for moving the tool and prepare the measure device. The inductor is also shut down to avoid the heating of only one section of the part. Although the experimental set-up is operational, the rotation and the heating start until to get the desired temperature. Once the temperature is reached, a 15 components are consistent. The second tendency is between 100 °C and 500 °C. A slight decrease of the forces is observed. The last section is defined for the temperatures higher than 500 °C where a considerable drop of forces was observed. Concerning each component, their evolution are identical. A larger decrease occurs for the Ft components when the temperature is 200 °C. An increase of 750 °C allows reducing cutting forces by 35 %. This evolution can be, partially, explained by the thermal softening got when the temperature increase. The steps described correspond to the evolution of mechanical properties as temperatures increasing. This evolution follows that of the material breaking limit and partially explains this trend [START_REF] Wagner | The relationship between the cutting speed, tool wear, and chip formation during Ti-5553 dry cutting[END_REF].

Tool wear

Since the elevation of temperature during the hot machining process, it is important to explore the state of the insert. As shown Fig. 5, the tool wear mechanisms are different as a function of the heat. For the test made at room temperature, a chipping occur near to the radial depth limit. This deterioration results from sever abrasion on rake face due to high pressures and titanium inclusions. These conclusions agree with the work of [START_REF] Wagner | The relationship between the cutting speed, tool wear, and chip formation during Ti-5553 dry cutting[END_REF]. When the temperature increases, the tool wear mechanisms change. The new titanium alloys behaviour and the tribological aspects of the tool chip interface explain this evolution. Indeed, the increase of temperature modifies the titanium behaviour. When the temperature raises the Ti5553 is more ductile and therefore stickier. As we will see later, it is possible to correlate degradation modes and chip formation.

Chip formation

The workpiece temperature also modifies the chip morphology for several reasons. The first is the modification of material behaviour which induces a new chip formation. The second is the modification of the titanium microstructure. The initial temperature combined with the thermal energy generated by the cutting process and consequently the titanium cooling generate a new microstructure. Table 1exposes the chip microstructure for different cutting temperature. Firstly, it appears that regardless of the cutting temperature, the chips are always sawtooth types. The shear angle is unchanged and the primary band frequency is never constant. Indeed, the chip analysis shows a stochastic distribution of the distance between each primary band. There is also the evolution of the primary shear bands thickness. The increase in temperature generates some largest shear bands. It should be noticed that for highest temperature the shear band thickness is not constant. As we will see later, very wide shear bands and extremely thin bands may appear. Consequently, the quantity of sheared titanium is strongly different.

Chip formation simulation

To investigate the titanium alloy chip formation mechanism, a 3D thermomechanical finite element model was developed. However, the simulation of such complex process requires the mastery of many parameters: (i) materials behavior and chip formation criterion (ii) parts geometries, (iii) boundary conditions …

Initial geometry, meshes and boundary conditions

3D continuum elements under reduced integration (C3D8RT) were used for thermomechanical fields calculations. In particular, a mesh refinement was applied in the region of interest in order to improve the accuracy at the tool tip. Based on a mesh sensitivity analysis, the region of interest mesh size is set to 25 μm while the coarsest is between (200-500 μm). The workpiece is a parallelepiped part with a 1.8 mm in length, 0.35 mm in width and 0.75 mm in height. It is modelled by a single partition of favours the realistic material separation.

As Summarized in Fig. 6, the tool is fixed and the workpiece moves only along the X axis with a constant velocity (here 35 m.min-1).

Tool and workpiece behaviors

A classical thermo-elastic law was used to model the carbide tool behavior. Regarding the workpiece, a Johnson-Cook behavior model is adopted [START_REF] Ludwik | Elemente der Technologischen Mechanik Elemente der Technologischen Mechanik[END_REF] (Eq. 1).

= + + [A B( ) ] 1 C Log 1 T T T T m p n p p0 r m r (1)
Where , p p p0 are respectively the plastic strain, the strain rate and reference plastic strain rate and T, T ,T r m are the temperature, the room temperature, the melting temperature of the workpiece material. A, B, C, m, n are material parameters. The Johnson-Cook parameter for the Ti-555-3 titanium alloy are specified in Table 2, whereas the physical properties of both material are mentioned in Table 3.

Damage criterion

Since the material failure mechanism in titanium alloy machining based on the ductile fracture, a specific damage model is developed through the coupling between the Johnson Cook flow behavior and the max shear stress criterion. The Max Shear (or Tresca) damage criterion can be expressed in terms of principal stresses by (Eq. 2):

= 2 f 1 3 (2)
However, the MS criteria can described and implemented though the equivalent strain at failure . More details about the model

Table 2

The Johnson-Cook behavior model parameters [START_REF] Bouchnak | Etude du comportement en sollicitations extrêmes et de l'usinabilité d'un nouvel alliage de titane aéronautique le TI[END_REF]. transformation are described in [START_REF] Harzallah | A coupled in-situ measurement of temperature and kinematic fields in Ti-6Al-4V serrated chip formation at micro-scale[END_REF]. Therefore, the MS damage criterion provides an expression of the equivalent strain at failure as a function of θ, f , and the Johnson cook thermal and strain rate dependency terms:

= + ( ) ( ) ( ) B C Log A B ¯3 . cos 1 1 f f T T T T m n 6 1 p p r m r 0 (9)
The MS damage model requires only one parameter to be identified. This latter named maximum shear stress at failure f . It is set to 1903 MPa [START_REF] Bouchnak | Etude du comportement en sollicitations extrêmes et de l'usinabilité d'un nouvel alliage de titane aéronautique le TI[END_REF]. The numerical chip morphology with temperature and the damage fields were respectively described in Figs. 7 and8. Serrated chips generated from quasi-periodic cracks were observed for all simulations. In addition, a high level damage is identified between chips segments. It describes the thermo-mechanical loadings level and cracks propagations history provoked in machining.

Regarding the crack path, it starts at the tool tip and propagates within the material until achieves the material upper part. Such effect is observed by [START_REF] Pottier | Sub-millimeter measurement of finite strains at cutting tool tip vicinity[END_REF][START_REF] Harzallah | In-situ characterization and modelling of mechanisms and thermomechanical couplings in machining : application to Ti-6AL-4V titanium alloy[END_REF], during TA6V machining. In fact, this latter phenomenon is highly influenced by the heating temperature. More temperature is important and more crack propagation within the material decreases. Such effect resulting from the attenuation of strain rate effect by the heating of the part which induces the suppression of the competition between the thermal softening and the strain hardening. Consequently, the strain localisation becomes less important which leads to a reduced crack propagation.

Numerical cutting forces under different heating temperatures are depicted and compared to the experimental ones in Fig. 9. It should be mentioned that the cutting forces are highly influenced by the heating temperature. It decreases when the temperature increases. The numerical observations were validated by the experimental ones. However, the experimental forces present a small error for the simulation at 200 and 400 °C. Conversely, an error of 9,7 % is observed for the simulation at 750 °C. This error can be related to the friction behaviour model which not taken into account the temperature effect. After all, it can be said that the model predicts well the cutting forces and the chip morphology.

Discussion

Based on the simulation results, the chip observations and the microstructure, it is possible to explain the chip formation. For the room temperature, the mechanisms have been studied by [START_REF] Wagner | The relationship between the cutting speed, tool wear, and chip formation during Ti-5553 dry cutting[END_REF]. This study exposes that the chip formation follows the mechanisms described by [START_REF] Komanduri | New observations on the mechanism of chip formation when machining titanium alloys[END_REF]. The chips collected are similar and consequently not studied in this article. However, when the temperature is modified, the alloy changes and new mechanisms occur.

When the temperature is 200 °C, the primary shear bands are very clear. They can be observed on focusing on α p phases deformations. All of them are elongated in one orientation, the primary shear band, where a strong deformation of the grains is observed. The comparison between the initial form (circular) and the deformed one informs about the strain's value. Moreover, it should be noticed that the shear bands are very localised. The α p nodules close to the shear bands stay totally circular and consequently not mechanically modified. The same observation can be made inside the bands where between α nodules deformed, the slate stay at their initial state. The β matrix is also affected because its size is bigger than the undeformed chip thickness (Fig. 13). In regarding the primary shear band, it occurs two behaviours. On the β matrix, concerning the α s slate, the deformations are smallest compared to the α p nodules. This difference can be explained by different points. Firstly, α p is the softest phase above all compared to the β trans phase [START_REF] Huang | High cycle fatigue behavior of Ti-5Al-5Mo-5V-3Cr-1Zr titanium alloy with bimodal microstructure[END_REF]. After, the nature of the microstructure allows getting some dislocations. The dislocation from α phases to the β phases are facilitated by the Burger's relation. Contrary to them in the other way which is more difficult. The FEM results show that the temperatures are highest to β transus temperature. However, the EDX analysis doesn't reveal a real change of the titanium nature contrary to [START_REF] Wagner | The relationship between the cutting speed, tool wear, and chip formation during Ti-5553 dry cutting[END_REF]. As with chips got at room temperature, it is possible to imagine that the shear strips are the result of an accumulation of high thermal stress and hardening. However, it can be assumed that their spread is facilitated by the presence of the α p phases. Concerning the secondary shear zone, the results are quite different. Indeed, there are two areas. The first area near to the chip limit where occurs a microstructure totally homogeneous. As they move up towards the centre of the chip, alpha grains reappear. This microstructure is mainly due to the high temperature at the tool chip interface.

The formation of chips at 200 °C seems to correspond to the phenomena described by [START_REF] Komanduri | On thermoplastic shear instability in the machining of a titanium alloy (Ti-6Al-4V)[END_REF] and exposed in Fig. 13. When the tool penetrates into the material, a catastrophic shear failure occurs (Fig. 13b point ( 1)). At first, this phenomenon is parallel to the cutting speed orientation (Fig. 13 line 2) and then rises to the free surface of the chip (Fig. 13 line (3)), the connection between these two events is made by a radius of curvature. There is an intense deformation zone and a fracture zone at the tip of the chips indicating a crack. In the case of Ti5553, the intense deformation zone causes a very strong elongation of the nodular phases. A deformation pattern appears, facilitated by these elements. This difficult propagation is explained earlier in the paragraph. It

Table 3

Material and contact condition [START_REF] Wagner | The relationship between the cutting speed, tool wear, and chip formation during Ti-5553 dry cutting[END_REF][START_REF] Bouchnak | Etude du comportement en sollicitations extrêmes et de l'usinabilité d'un nouvel alliage de titane aéronautique le TI[END_REF].

should be noticed that the temperature increases rapidly as explained by [START_REF] Wagner | Comparison of the chip formations during turning of Ti64 β and Ti64 α+β[END_REF], however, the low thermal conductivity limits the effect on the microstructure and consequently in its evolution (Fig. 13b). The two close shear bands observed in some areas can be explained in two ways. The first is, as points out by [START_REF] Komanduri | On thermoplastic shear instability in the machining of a titanium alloy (Ti-6Al-4V)[END_REF], a deformation from the cutting edge through the free surface and a second in the opposite direction. The second explanation is a simultaneous propagation of two shear bands facilitated by an optimal α phase alignment from a dislocation point of view. Once this shear band is established, the material between the shear bands does not appear to be damaged. However, the ratio between the size of the ex-beta grains and the thickness of the chip suggests that it distorts the material. As temperature levels remain lower, the microstructure cannot be affected. This phenomenon has been studied by [START_REF] Wagner | Etude de la formation des copeaux en tournage à sec du Ti5553[END_REF] where some EBSD analysis have been performed. When a saw-tooth chip is generated, intense pressure and temperature occur at the tool chip interface which induce high strain and large microstructure evolution (Fig. 13c). A new shear bands can be generated (Fig. 10).

The temperature increasing generates a new material where all the phases described in the previous sections are not visible. Fig. 11shows the microstructure in primary and secondary zone of a chip when T = 400 °C. As when T = 20 °C, the α grains are deformed along the same orientation. Between the primary and the secondary shear bands, the structure seems not changed. However, there is a crucial evolution. Indeed, the α s slates disappear in the primary shear bands. Although the phenomena occurring in the shear zones suggest that dynamic recrystallisation phenomena may occur. These first analysis shows a fraction of phase-free material whose nature depends greatly on the temperature generated. This evolution is due to the temperature [START_REF] Setterfati | Experimental study and phase field modelling of alpha formation in near beta titanium alloys[END_REF]. The thickness of the primary shear bands is continuous compared to the previous test in despite of the softest material machined. It is explained by the homogenisation of the material where the behaviour difference between the phases is reduced. On the FEM model, the temperatures in these zones are higher than T βtransus and suggest the microstructure evolution. For the secondary zone, its thickness increases, reflecting higher temperatures and pressures. α grains are also more elongated compared to the primary zone. The direction of deformation is unique and parallel to the sliding speed of the chip on the cutting face.

For this temperature, the chip formation is conditioned by different factors which explained both the microstructure and the slight reduction in cutting forces. The phenomena involved during the initiation of the crack remain identical, as the initial microstructure does not seem to be modified (Fig. 13e). The difference is made when during the shear bands formation. Studies show that there is competition between hardening phenomena and thermal phenomena and that the shear band only appears when thermal effects become predominant. The latter are very localised and generate a very strong thermal softening. At this temperature, an additional phenomenon is present. The increase in temperature causes a microstructural modification where the α slates are completely dissolved. This phenomenon, as wide as the shear bands, favours the creation of these bands. The more homogeneous material promotes dislocations. This change in the nature of the alloy seems to be happening quickly (Fig. 13f). The cutting force reduction can also be explained by this chip formation. Indeed, this new microstructure facilitates the creation of shear bands (high thermal softening and initiation of dislocations facilitated) and therefore limits shear forces. As for the secondary zone, it is also totally devoid of α phase.

The chip formation and microstructure evolution are more significant when T = 750 °C. In general point of view, the morphology of the chips collected is not constant. For these conditions, there are two possibilities. The first is shown Fig. 1 where a sawtooth chip is generated. The main difference with the lower temperatures is some big primary shear bands. This chip formation follows the mechanisms exhibit previously. However, the thermal softening of the workpiece material requires a large volume of material to get the shear bands. The second possibility is exposed on Fig. 12. The slates observed in the β matrix have largely disappeared both in shear zones and in undeformed zones (Fig. 12). This evolution can also explain partially the cutting forces decrease. Indeed, the initial variable of the α phase, the alphagrain boundary, the alphaWidmanstätten grain boundary and the morphology of the β phase can explain the low machinability of this titanium alloy [START_REF] Arrazola | Machinability of titanium alloys (Ti6Al4V and Ti555. 3)[END_REF]. Moreover [START_REF] Warchomicka | Study of the hot deformation behaviour in Ti-5Al-5Mo-5V-3Cr-1Zr[END_REF] reveal the large decrease of the mechanical properties of the titanium when the temperature increase. Concerning the chip formation, the distance between shear strips appears to be less uniform as the chip and increase drastically.

At 750 °C, the phenomena are different because the microstructure is totally modified (Fig. 13g) before the chip formation. The absence of shear bands suggests that the phenomena involved are totally different. It would seem, given the thickness of the secondary zone, that the material is consequently cut. Indeed, there appears to be a very thick secondary. When the chip is separated from the part, a deformation propagates to the free surface of the chip. The latter is of low intensity because the α nodules are lightly deformed.

Limits of temperature increase

To define the advantage provide by the temperature increase, a power approach is used. Indeed, the definition of a sustainable assistance can be : the power used to help, in our case to increase the temperature, is lower than the power earning. Without considering the thermal dispersion (radius and convective effect), the power supplied to raise the temperature is defined (equation 6).

where C p is the heat capacity, M the sample weight, ∂T the temperature and ∂t the time to get the temperature. Fig. 14reveals the gain of power when the temperatures increase. Each blue line corresponds to the difference between the cutting power at the considered temperature and the cutting power when T = 20 °C. The orange lines are the power supplies to increase the temperature of the part. As seen on the Fig. 14, the power provides is always the lowest. Consequently and although the cutting force reduction is low, this help seems sustainable. However, the quantity of metal to heat is small or limited and the time is very long. If the part height is three times higher (yellow line), this assistance becomes not sustainable and the same conclusion is made when the time to increase the temperature is five times reduced.

The same approach has been applied on a turning operation. The thermal power corresponds to the power needs to increase the temperature of a tube which correspond to the volume of the material removed by the tool in a defined time. Contrary to the previous study, the heat source moves with the tool to heat a localized surface. The aim is to avoid to heat the entire part but only the a limited volume which can be removed in limiting the losses. For this study, the length is limited to L = 0,01 m. This power is defined equation 7:

where R is the part radius (150 mm), ap is the radial depth, L the length of the heated tube (0,01 m) 750 °C.

As defined previously (Fig. 15), if the radial depth increases the power provided is larger (highest volume of material) and when the heating time increases the power reduces. To each radial depth, the dot lines correspond to the cutting power earning when T = 750 °C. When the time increases, the power needed becomes very large. Based on these figures, it occurs an interesting point. Indeed, the limit of a sustainable process is always defined when t = 180 s. This limits move automatically when L is modified. For example, it moves to 20 s when L = 1 mm. In comparing the tool feed and the time to heat this limited length of material (without considering the convection and the radiation phenomenon), it seems very difficult to use this assistance only by focusing on the economic aspects during rough operations. Indeed, it is necessary to use more efficient heating means (to reduce the time to heat), therefore to the detriment of the economic gain and to combine these results with potentially significant gains in tool wear, for example.

Conclusion

Due to the difficulty of machining the Ti5553, many assistance systems have been tested. The hot machining technique seems to be a relevant solution. The principle is to heat the cutting zone and advantage from thermal softening. The objective of this work is to verify the relevance of this assistance on the machinability of Ti5553 and understand the phenomena involved. For this reason, an experimental device has been developed that allows temperature control over the whole depth of cut. The analysis shows that an increase in temperature does indeed lead to a reduction in cutting forces. This reduction seems to correlate with two aspects. The first is the reduction of mechanical properties with increasing temperature. The second is a different cutting process. From a wear point of view, although wear tests have not been carried out, totally different facies appear. Abrasive phenomena give way to an adherent chip. The increase in temperature leads to a different chip formation. At low temperatures (below 200 °C), shear bands appear as described in many works. Above 400 °C, a new phenomenon appears. The previously unchanged microstructure is now affected by the cutting process. This modification only occurs in the shear zones. For high temperatures, the cutting process is completely modified. The chip literally seems to have been cut. Lastly, a feasibility analysis shows that a purely economic consideration of this assistance shows a certain limit. The heating powers to be used for roughing operations become very important.

Declaration of Competing Interest

Fig. 2 .

 2 Fig. 2. Experimental heating of workpiece material.

Fig. 3 .

 3 Fig. 3. Ti5553 original microstructure.

Fig. 4 .

 4 Fig. 4. Cutting forces evolution with temperature.

Fig. 5 .

 5 Fig. 5. Tool wear according to temperature.

Fig. 6 .

 6 Fig. 6. 3D Orthogonal cutting FE model: Boundary condition and geometry.

Fig. 7 .

 7 Fig. 7. 3D orthogonal cutting FE model -Damage.

Fig. 8 .

 8 Fig. 8. 3D orthogonal cutting FE model -Temperature.

Fig. 9 .

 9 Fig. 9. Comparison between experimental and numerical cutting forces.

Fig. 10 .

 10 Fig. 10. Ti5553 microstructure when T = 200 °C.

Fig. 11 .

 11 Fig. 11. Ti5553 miscrostrcture when T = 400 °C.

Fig. 12 .

 12 Fig. 12. Ti5553 microstructure when T = 750 °C.

Fig. 13 .

 13 Fig. 13. Steps of the chip formation according to temperatures.

Fig. 14 .

 14 Fig. 14. Power gain and power provides to increase temperature.

Fig. 15 .

 15 Fig. 15. Power gain and power provides to increase temperature.

Table 1

 1 Chip morphology evolution.

The authors report no declarations of interest.


Véronique Boulenger
email: boulenger@isc.cnrs.fr

Alice C Roy

Yves Paulignan

Viviane Deprez

Marc Jeannerod

Tatjana A Nazir

Cross-talk between language processes and overt motor behavior in the first 200 msec of processing

come   L'archive ouverte pluridisciplinaire

Introduction

An increasingly popular view implies that in addition to "classical" cortical language areas, sensory and motor regions are involved in the retrieval of word meaning. Two models have been put forward to support this proposal. The first model refers to Hebbian correlation learning [START_REF] Pulvermüller | Hebb's concept of cell assemblies and the psychophysiology of word Processing[END_REF](Pulvermüller, , 1999a[START_REF] Pulvermüller | Brain reflections of words and their meaning[END_REF] and proposes that meaning-related information about words could be laid down in temporal-visual and motor areas because some words frequently co-occur in the context of visual perception (e.g. concrete nouns), while others frequently co-occur in the context of action execution (e.g. action verbs). The second model, on the other hand, sees language understanding as a form of mental simulation that involves a sensory-motor matching system, the so-called "mirror neuron system" [START_REF] Gallese | The brain's concepts: The role of the sensory-motor system in conceptual knowledge[END_REF]Rizzolatti, Fadiga, Gallese & Fogassi, 1996;[START_REF] Rizzolatti | Language within our grasp[END_REF]Rizzolatti, Fogassi, & Gallese, 2001;Tettamanti et al., 2005). The mirror neuron system, initially discovered in monkeys but which also exists in humans [START_REF] Buccino | Action observation activates the premotor and parietal areas in a somatotopic manner: an fMRI study[END_REF][START_REF] Buccino | Neural circuits involved in the recognition of actions performed by nonconspecifics: an FMRI study[END_REF][START_REF] Fadiga | Motor facilitation during action observation: a magnetic stimulation study[END_REF][START_REF] Mason | Thinking about actions: the neural substrates of person knowledge[END_REF]) consists of visuo-motor neurons that fire both when a monkey executes a given action and observes the same action being performed by others (di Pellegrino, [START_REF] Di Pellegrino | Understanding motor events: a neurophysiological study[END_REF]Gallese, Fadiga, Fogassi & Rizzolatti, 1996;[START_REF] Rizzolatti | Premotor cortex and the recognition of motor actions[END_REF]. By matching observed movement onto her/his own motor repertoire, the observer uses her/his motor knowledge to recognize the action [START_REF] Rizzolatti | Neurophysiological mechanisms underlying the understanding and imitation of action[END_REF]. The recent discovery of acoustic mirror neurons (responding to the execution and to the noise typically evoked by an action; [START_REF] Kohler | Hearing sounds, understanding actions: action representation in mirror neurons[END_REF] and of oro-facial communicative mirror neurons (responding to the execution of a mouth action and to the observation of oral affiliative gestures, i.e lip smacking; [START_REF] Ferrari | Mirror neurons responding to the observation of ingestive and communicative mouth actions in the monkey ventral premotor cortex[END_REF] have been taken to suggest that the human mirror neuron system may code action at an abstract level, accessible by language [START_REF] Gallese | The brain's concepts: The role of the sensory-motor system in conceptual knowledge[END_REF]Tettamanti et al., 2005). Hence, the perception of an action word and not the perception of the action per se would be sufficient to trigger the mirror neuron system.

Though based on different assumptions, both models see language and motor systems as sharing overlapping neuronal representations and a range of recent data seems to support this view [START_REF] Buccino | Listening to action-related sentences modulates the activity of the motor system: a combined TMS and behavioral study[END_REF][START_REF] Oliveri | All talk and no action: a transcranial magnetic stimulation study of motor cortex activation during action word production[END_REF]Pulvermüller, 2005a;Pulvermüller, Hauk, Nikulin & Ilmoniemi, 2005b;Pulvermüller, Shtyrov & Ilmoniemi, 2005c).

Neuropsychological studies, for instance, have shown selective deficits for processing action words after left premotor lesions (e.g. [START_REF] Bak | Selective impairment of verb processing associated with pathological changes in Brodmann areas 44 and 45 in the motor neurone disease-dementia-aphasia syndrome[END_REF][START_REF] Damasio | Nouns and verbs are retrieved with differently neural systems[END_REF]. Neuroimaging studies have further revealed that motor and premotor cortices are activated during processing of action words or sentences (Pulvermüller, 2005a). More importantly, words designating actions performed with different body parts elicit somatotopically-organized activation, just as movement execution does (Hauk, Johnsrude & Pulvermüller, 2004a;Tettamanti et al., 2005). Studies using transcranial magnetic stimulation (TMS) have completed this picture by revealing that processing actionrelated words or sentences modulates the excitability of the left but not the right motor cortex [START_REF] Meister | Motor cortex hand area and speech: implications for the development of language[END_REF]Pullvermuller et al., 2005b). [START_REF] Buccino | Listening to action-related sentences modulates the activity of the motor system: a combined TMS and behavioral study[END_REF], for instance, demonstrated that listening to hand and leg action-related sentences reduced the amplitude of motor evoked potentials (MEPs) recorded from hand and leg muscles, respectively. Furthermore, behavioral measures in this study revealed an increase of reaction time when the motor response and the words called the same effector. Related but slightly different results were observed by [START_REF] Oliveri | All talk and no action: a transcranial magnetic stimulation study of motor cortex activation during action word production[END_REF] and Pulvermüller et al. (2005b), who reported higher MEPs and shorter reaction times to action words (we will discuss these seemingly divergent results later).

In spite of the hitherto stimulating results, however, many questions remain unsolved.

Firstly, the reported results do not provide clear-cut evidence as to whether motor regions truly participate in the process of action word retrieval. In fact, as recognition of action words could prompt mental motor imagery -a phenomenon known to activate cortical motor regions (Alkadhi et al., 2005;[START_REF] Jeannerod | The representing brain: Neural correlates of motor intention and imagery[END_REF][START_REF] Jeannerod | Mental imaging of motor activity in humans[END_REF]) -language-related cortical motor activity could result from after-effects of linguistic processes. The likelihood that this is the case would diminish though, if cortical motor activity could be shown to arise early following word onset. That is, within the first 100-200 ms within which lexico-semantic processes are typically observed (e.g., word frequency effects or effects of word category; [START_REF] Pulvermüller | Nouns and verbs in the intact brain: Evidence from event-related potentials and high-frequency cortical responses[END_REF][START_REF] Pulvermüller | Brain reflections of words and their meaning[END_REF][START_REF] Preissl | Evoked potentials distinguish between nouns and verbs[END_REF][START_REF] Sauseng | When does the brain register deviances from standard word spellings? An ERP study[END_REF][START_REF] Sereno | Establishing a time-line of word recognition: evidence from eye movements and event-related potentials[END_REF][START_REF] Sereno | Measuring word recognition in reading: eye movements and event-related potentials[END_REF]. Pulvermüller et al. (2005c), who used magnetoencephalography (MEG) to describe the time course of cortical activity during action word processing, provided first data in this line. Their results showed that cortical activity in the perisylvian language areas, which appeared at about 130-150 ms after action word onset, was followed by a short-lived activity in frontocentral and superior central areas at about 170-200 ms post-stimulus. The present study is aimed at further substantiating this finding using behavioral measures.

Secondly, despite increasing evidence that action word processing engages cortical motor regions, it is unclear whether these same structures are also involved in controlling motor behavior or whether they remain "language-specific" despite their location in cortical motor regions. In other terms, would there be cross-talk between processing of actions words and overt motor behavior if the two tasks were performed concurrently?

To address these issues, we designed two experiments that allowed fine-grained analyses of the kinematics of an arm reaching movement performed in relation to a language task. In Experiment 1, the reaching movement was performed concurrently to a visual lexical decision task with action verbs or concrete nouns and pseudo-words. In Experiment 2, lexical decision was made prior to movement onset. As we will show, relative to concrete nouns, encoding of action verbs interferes with the concurrent execution of the reaching movement early following word/movement onset. By contrast, processing action verbs prior to movement onset assists subsequent motor performance.

Method

Participants

Nine French native volunteers (22 to 27 years old) participated in each of the two experiments. All were right-handed (scores between 0.58 to 0.90; [START_REF] Oldfield | The assessment and analysis of handedness: the Edinburgh Inventory[END_REF], and had normal or corrected-to-normal vision. None of the volunteers participated in both experiments.

Stimuli

Eighty-four words (42 verbs and 42 nouns) were selected from the French lexical database "Lexique" [START_REF] New | Une base de données lexicales du français contemporain sur internet : LEXIQUE[END_REF]. Verbs, all in the infinitive form, denoted actions performed either with the hand/arm, leg, or mouth/face (e.g., paint, jump, cry). Nouns, all in singular form, referred to imageable, concrete entities that cannot be manipulated (e.g., star, cliff, meadow). Words that could be used as both nouns and verbs were excluded from the selection. Stimuli were matched for relevant lexical variables including word frequency, length in letters, number of syllables, bi-and trigram frequency, and number and cumulative frequency of orthographic neighbors (Appendix 1). Word age of acquisition was also controlled using empirical ratings performed by 20 volunteers on a seven-point scale (1 = [0-2 years] and 7 = [older than 13 years]; [START_REF] Gilhooly | Methods and designs: Age of acquisition, imagery, concreteness, familiarity, and ambiguity measures for 1,944 words[END_REF]. Word imageability was estimated following the same procedure by another 18 volunteers (with 0 = impossible and 6 = very easy to generate a mental image of the word). To prevent participants from focusing on word-class discrimination, they were asked to perform a lexical decision task (deciding whether a letter string is a word or not). Eighty-four pseudo-words, constructed by changing one letter from real nouns or real verbs, were thus added as fillers to perform the lexical decision task. Pseudo-words were either "pseudo-nouns" (42 items) or "pseudo-verbs" (42 items) and were all pronounceable. Pseudo-words were matched to words for relevant lexical variables (Appendix 1). Verbs and pseudo-verbs were also carefully matched for endings, such that as many verbs as pseudo-verbs (32 out of 42) ended with "er", which is a frequent ending for verbs in French. All items were presented in lower case.

Procedure

Experiment 1: Participants were asked to touch a home-pad (10cm from their chest) with their right thumb and index finger held in a pinch grip position, while fixating a monitor (95cm from their chest). On appearance of a white cross at the center of the monitor for 500 ms (go-signal), they had to leave the home-pad to reach and grasp a cylindrical object (height: 30mm, diameter: 15mm) placed in front of them (40cm from the home-pad). The onset of the movement (i.e. leaving the home-pad) triggered the presentation of a letter string on the monitor. If the string was a word, the movement had to be carried on, if it was a pseudo-word, the movement had to be interrupted to move back to the home-pad. Stimulus remained on the screen until participants grasped the object (in the word condition) or turned back to the home-pad (in the pseudo-word condition). The next trial was triggered by the experimenter once participants were in the starting position. Video recording assured that participants maintained their gaze on the cylindrical object during final movement execution (word condition only). Each stimulus was proposed once and presentation was randomized. Twenty training trials familiarized participants with the task.

Experiment 2: Material was identical to Experiment 1. Procedure differed in that the gosignal was no longer a fixation cross but the letter string. As in Experiment 1, if the stimulus was a word, participants had to reach and grasp the cylindrical object. If it was a pseudoword, they had only to lift their hand from the home-pad.

Movement recordings

An Optotrak 3020 (Northern Digital) was used to record the spatial positions of four markers (infrared light-emitting diodes), at a frequency of 200 Hz and with a spatial resolution of 0.1 mm. One marker was taped on the wrist. The three remaining markers were fixed on the experimental set-up to define a space in which all recorded movements were systematically placed from participant to participant.

Data analysis

A second-order Butterworth dual pass filter (cutoff frequency, 10 Hz) was used for raw data processing. Movements were then visualized and analyzed using Optodisp software 2001). Kinematic parameters for the word condition were assessed for each individual movement. We analyzed movement time (i.e. time elapsed between the onset of the movement and the grasping of the target object), and latency and amplitude of wrist acceleration/deceleration peaks. Movement onset was determined as the first value of a sequence of at least eleven increasing points on the basis of wrist velocity profile. End of movements were determined similarly, starting from the end and going backwards. Peak latencies were defined as the time elapsed between movement onset and peak. In Experiment 2, we additionally measure reaction time defined as the time elapsed between word onset and movement onset. All kinematic parameters were determined for each individual trial and were averaged for each participant as a function of word category. Trials in which participants made errors or anticipated movement execution were excluded from the analysis.

(Optodisp: copyright INSERM-CNRS-UCBL, M. Thevenet, Y. Paulignan & C. Prablanc,

Results

Experiment 1

A total of 11.64% of trials were excluded from the analysis (5.56% for nouns vs. 6.08% for verbs). Analyses of movement parameters revealed that individual wrist acceleration peaks appeared later and were smaller during displays of action verbs than during displays of concrete nouns in 8 out of 9 participants (Table 1a). An analysis of variance (ANOVA) with repeated measures confirmed that word group had a significant influence on these two variables (for peak latency: 160 ms for nouns vs. 177 ms for verbs, [F (1, 8) = 6.12; p = .0380], for peak amplitude: 3994 mm/s 2 for nouns vs. 3871 mm/s 2 for verbs, [F (1, 8) = 8.77; p = .0180]). None of the other movement parameters showed significant differences between the two word categories. As wrist acceleration peak is indicative of initial muscular contractions, longer latency and smaller amplitude suggest that perceiving action verbs interferes with the execution of the movement.

Table 1 (about here)

For better illustration of the phenomenon, in Figure 1a, we plot wrist acceleration profiles, normalized between 0% and 100% of time, averaged over items and participants. Note that by averaging entire movement profiles, slightly different values for latencies and amplitudes of wrist acceleration/deceleration peaks are obtained when compared to the values presented in Table 1 (see Appendix 2 for details about differences between the two analyses).

Figure 1 (about here) Experiment 2 A total of 7.93% of trials were excluded from the analysis (4.76% for nouns vs. 3.17% for verbs). Reaction time analysis showed no significant difference between action verbs (386 ms; sd = 56; including items referring to actions performed with arm (385 ms) and other body parts (386 ms)) and concrete nouns (400 ms; sd = 73; [F (1, 8) = 2.5674; p = .1478]).

In contrast to Experiment 1, individual wrist acceleration peaks appeared earlier for action verbs than for concrete nouns in 6 out of 9 participants (two participants had identical latencies for the two word categories; Table 1b). The ANOVA with repeated measures confirmed the effect of word category (182 ms for nouns vs. 169 ms for verbs; [F (1, 8) = 5.8276; p = .0422]), thus indicating that processing action words prior to movement execution assists the reaching movement. In contrast to Experiment 1, however, the difference in amplitude of wrist acceleration peaks between the two groups did not reached significance.

None of the other kinematic landmarks showed significant effects of word group. Figure 1b plots wrist acceleration profiles, normalized between 0% and 100% of time and averaged over items and participants.

A comparison of the two experiments revealed that movement time was significantly shorter in Experiment 2 (see Table1ab, [F (1, 16) = 6.9750; p = .0178]), which suggests that the motor task was easier when lexical decision was made prior to movement execution. The percentage of errors did not differ between the two experiments.

Figure 2 plots latency of wrist acceleration peak for nouns and verbs in the two experiments. Performance for action verbs is also shown split by items referring to actions performed with the arm and those performed with other body parts. These data show that interference (Experiment 1) and facilitation (Experiment 2) were more pronounced for verbs designating actions performed with the arm, which was the effector used in the motor task.

However, given that relevant word parameters were controlled only over the entire set of words, these latter results have to be interpreted with caution.

Figure 2 (about here)

Discussion

Cross-talk between language and motor tasks

Our results thus show that processing action words can interfere with or facilitate overt motor behavior, depending on the temporal relation between language and motor tasks. When performed in parallel (Experiment 1), processing action verbs seems to transiently hinder the execution of a reaching movement, but when performed prior to movement onset (Experiment 2), it seems to assist motor behavior. Interference between the two tasks occurred as early as 160-180 ms following word onset (cf. latency of wrist acceleration peak in Table 1a), while priming became evident at about 550-580 ms following word onset (cf. latency of wrist acceleration peak in Table 1b plus reaction time (i.e., the time elapsed between word onset and movement onset)). The reversal of the pattern of interaction from interference to priming as a function of the temporal relation between tasks seems to display a systematic feature because it parallels the results reported in TMS studies. Hence, [START_REF] Buccino | Listening to action-related sentences modulates the activity of the motor system: a combined TMS and behavioral study[END_REF], who demonstrated a decrease of the amplitude of MEPs and slower motor reaction time to action words, applied TMS during word encoding (similar to the present Experiment 1), while [START_REF] Oliveri | All talk and no action: a transcranial magnetic stimulation study of motor cortex activation during action word production[END_REF] and Pulvermüller et al. (2005b), who reported an increase of the amplitude of MEPs and faster motor reaction times to action words, applied TMS 500 or 150 ms after word onset (similar to the present Experiment 2).

Given that priming effects occurred relatively late following word onset, these effects could result from side-or after-effects of linguistic processes. As a matter of fact, our priming effect is reminiscent of facilitatory effects of action observation on the kinematics of subsequent movement execution [START_REF] Brass | Compatibility between observed and executed finger movements: Comparing symbolic, spatial and imitative cues[END_REF][START_REF] Brass | Movement observation affects movement execution in a simple response task[END_REF][START_REF] Castiello | Observing a human or a robotic hand grasping an object: Differential motor priming effects[END_REF][START_REF] Edwards | Motor facilitation following action observation: A behavioural study in prehensile action[END_REF], and could thus result from mental motor imagery. By contrast, the early time window within which interference between language and motor tasks was observed is suggestive of the assumption that action word processing/retrieval does indeed engage cortical structures involved in the programming of motor action. Competition for common resources, for instance, could give rise to such interference. In line with TMS and brain imaging studies [START_REF] Buccino | Listening to action-related sentences modulates the activity of the motor system: a combined TMS and behavioral study[END_REF]Hauk et al., 2004a;Pulvermüller et al., 2005b;Pulvermüller, 2005c;Tettamanti et al., 2005), the present data further show that the interaction between language and motor tasks was strongest when words and action called the same effector (see Figure 2). This aspect of our results indicates that language-related activity in cortical motor regions does not simply result from an automatic spreading of activation throughout the entire motor system, but rather comforts the hypothesis of a straight link between action word content and motor activity.

Motor regions: a necessity for the understanding of action words?

Together with evidences from TMS and brain imaging studies [START_REF] Buccino | Listening to action-related sentences modulates the activity of the motor system: a combined TMS and behavioral study[END_REF]Hauk et al., 2004a;Pulvermüller et al., 2005b;Pulvermüller, 2005c;Tettamanti et al., 2005), our finding is thus suggestive of the assumption that language-related activity in cortical motor regions is part of action word processing, and cannot be solely attributed to processes that occur after the word had been identified (i.e., motor imagery). In other terms, languagerelated activity in cortical motor regions might contribute to the understanding of action words that refer to parts of the human body. Still, such assertion has to be taken with caution as the present study measures motor behavior and not word understanding (note that the same caveat applies to the cited TMS studies, which also assessed MEPs or motor reaction times).

Hence, although language-related activity in motor areas is strong enough to interfere with overt motor behavior, our data do not allow inferring whether these regions are truly essential for word understanding. By reviewing neuropsychological evidence from apraxic patients, [START_REF] Mahon | The orchestration of the sensory-motor systems: Clues from neuropsychology[END_REF] emphasized, for instance, that motor processes are not required to support conceptual knowledge about actions, because action recognition can be dissociated from imitation and execution [START_REF] Halsband | Recognition and imitation of pantomimed motor acts after unilateral parietal and premotor lesions: A perspective on apraxia[END_REF][START_REF] Rumiati | A form of ideational apraxia as a selective deficit of contention scheduling[END_REF]. Intact conceptual knowledge about graspable objects can also persist together with impairment in using these objects [START_REF] Rosci | Apraxia is not associated to a disproportionate naming impairment for manipulable objects[END_REF]. Similarly, lesions over left motor cortex do not predictably lead to impairment in processing action words [START_REF] De Renzi | Sparing of verbs and preserved, but ineffectual reading in a patient with impaired word production[END_REF][START_REF] Saygin | Action comprehension in aphasia: linguistic and non linguistic deficits and their lesion correlates[END_REF], suggesting that motor processes alone are not sufficient to represent all that we know about action concepts. However, though language-related activity in motor and premotor cortex might not be necessary for understanding, it might potentially help action word recognition. Braindamaged patients with left motor lesion but no obvious deficit in action word (verb) processing might thus turn out to perform poorer than healthy participants when tested with more subtle measures.

Concluding remarks

The present behavioral study supports previous findings, which showed that processing of action words involves cortical motor regions. We highlight that perceiving action verbs can interfere with or prime a reaching movement depending on the temporal relation between the two tasks. The arguments that we shortly developed above favor the view that languagerelated activity in cortical motor regions contributes to the understanding of action words, though we point out limits of this hypothesis given the currently available data. For a better understanding of the nature of the link between action word processing and motor activity, we need to better capture the rule that governs the switch from interference to priming between language and motor tasks, and to measure in parallel motor performance and action word understanding.

Authors' note

We would like to thank Alfonso Caramazza and one anonymous reviewer for helpful comments on an earlier version of this article. Tettamanti M., Buccino, G., Saccuman, M. C., Gallese, V., Danna, M., Scifo, P., Fazio, F., Rizzolatti, G., Cappa, S. F. & Perani, D. (2005). Listening to action-related sentences activates fronto-parietal motor circuits. Journal of Cognitive Neuroscience,17,[273][274][275][276][277][278][279][280][281] Table 1 Mean latency and amplitude for the wrist acceleration and deceleration peaks, and total movement time for each participant while processing nouns and verbs, for a) Experiment 1 and b) Experiment 2. For verbs, data are given averaged over all items (total) as well as for items that describe arm actions (arm) and non-arm actions (others). These latter data should be interpreted with caution though, as lexical parameters for nouns and verbs were equated over the entire set of verbs but not for the subsets. Last three rows: Mean over all participants, standard deviation and analyses of variance with repeated measures. Gray fields indicate performance that deviated from the tendency suggested by the mean.

  TAN is a member of the Marie Curie Research and Training Network: Language and Brain (RTN: LAB) funded by the European Commission (MRTN-CT-2004-512141) as part of its "Sixth Framework Program".

FIGURE 1 :

 1 FIGURE 1: Averaged wrist acceleration/deceleration profiles of all participants (normalized between 0% and 100% of time) during processing of nouns (in black) and verbs (in grey) for a) Experiment 1 and b) Experiment 2. Note that by normalizing the data, real time information is lost. One time unit in the graph corresponds approximately to 10ms. The inset magnifies the wrist acceleration peak profile. The grey bar indicates the time window within which paired t-tests (per time unit) revealed a significant difference between the two conditions.

FIGURE 2 :

 2 FIGURE 2: Time to wrist acceleration peak (in milliseconds) for the different word categories for a) Experiment 1 and b) Experiment 2. "*" indicates a significant difference in the latencies of the wrist acceleration peak between concrete nouns and action verbs. Black arrows notice the values of peak latency for verbs denoting arm-related actions (compared to verbs referring to actions performed with leg or mouth = other verbs).

  FIGURE 1

0422 ns. Wrist acceleration peak Wrist deceleration peak Total Movement time (ms) LATENCY AMPLITUDE LATENCY AMPLITUDE (ms) (mm/s 2 ) (ms)

	a) Experiment 1
				Wrist acceleration peak				Wrist deceleration peak
			LATENCY			AMPLITUDE		LATENCY	AMPLITUDE	Total Movement
			(ms)				(mm/s 2 )			(ms)	(mm/s 2 )	time (ms)
		NOUNS		VERBS		NOUNS	VERBS		NOUNS	VERBS	NOUNS		VERBS	NOUNS	VERBS
	Participa nts	total	total	arm	other s	total	total	arm	others	total	total	total		total	total	total
	P1	128	126	124	128	5341	5027	5125	4938	522	523	-4231		-4004	852	842
	P2	155	158	159	156	6251	6240	5740	6672	457	455	-4512		-4515	870	912
	P3	213	237	237	237	4405	4415	4288	4525	565	562	-3920		-3903	1025	1006
	P4	181	187	182	191	2366	2066	2051	2080	495	497	-1569		-1678	1348	1392
	P5	137	139	136	141	3883	3814	3842	3788	458	502	-2304		-2467	1036	1041
	P6	138	151	170	134	3575	3506	3441	3567	493	486	-3123		-2837	1205	1173
	P7	173	187	192	181	4262	4148	4255	4090	601	615	-5848		-2674	1060	1106
	P8	174	200	205	195	3282	3069	2869	3280	669	653	-3597		-3784	1332	1038
	P9	146	210	223	196	2576	2556	2540	2573	470	491	-1943		-1696	1214	1211
	MEAN	16 0	17 7	18 1	173	3994	3871	3795 3946	526	532	-345 0		-3062	1105	1080
	SD	27	36	38	36	1250	1279	1205	1358	73	66	1367		1035	182	165
	ANOVA	F(1,8) = 6.12; p = .038			F(1,8) = 8.77; p = .018			ns.		ns.			ns.
	b) Experiment 2
												(mm/s	)
		NOUNS	VERBS		NOUNS	VERBS		NOUNS	VERBS	NOUNS	V E RBS	NOUNS	VERBS
	Participants	total	total	arm	others	total	total	arm	others	total	total	total		total	total	total
	P1	161	141	127	154	8508	8905	9114	8705	443	424	-8201		-7893	625	632
	P2	178	143	146	139	4018	4126	4178	4082	658	641	-3259		-3397	1007	1000
	P3	287	245	208	282	3769	3572	3521	3623	654	662	-4695		-4586	847	866
	P4	140	129	127	131	7839	8192	8158	8220	432	431	-9380		-9348	661	677
	P5	135	132	135	130	5378	5321	5315	5326	498	493	-3855		-4023	1071	1078
	P6	205	208	209	207	5001	4866	4912	4824	496	498	-5815		-5591	774	788
	P7	165	156	165	147	4117	4104	3917	4272	568	563	-3787		-3662	866	871
	P8	193	193	198	189	4337	4297	4306	4289	677	689	-3013		-2755	1114	1174
	P9	171	171	171	172	4632	4624	4586	4657	553	561	-4029		-4113	928	882
	MEAN	182	169	165	172	5289	5334	5334 5333	553	551	-5145		-5041	877	885
	SD	45	39	34	49	1717	1897	1959	1841	93	98	2356		2286	171	177
	ANOVA	F(1,8) = 5.8276; p = .							ns.		ns.			ns.

Appendix 1

Mean values of word frequency (FQ), length in letters (LETT), number of syllabic groups (SYLL), bigram frequency (BIGR), trigram frequency (TRIG), number and cumulative frequency of orthographic neighbours (NB NEIGH and FQ NEIGH respectively), age of acquisition (AoA) and imageability (IMAG) are reported for nouns, verbs and pseudo-words. Word age of acquisition was controlled with empirical ratings of 20 subjects on a seven-point scale (according to [START_REF] Gilhooly | Methods and designs: Age of acquisition, imagery, concreteness, familiarity, and ambiguity measures for 1,944 words[END_REF]; 1 = [0-2] and 7 = [older than 13 years old]). Word imageability was evaluated in the same way by 18 other subjects (with 0 = impossible and 6 = very easy to generate a mental image of the word). Analyses of variance are reported for nouns and verbs, and words and pseudo-words. Verbs and pseudo-verbs were also carefully matched for endings, such that as many verbs as pseudo-verbs (32 out of 42) ended with "er", which is a frequent ending for verbs in French.

NOUNS

Appendix 2

To obtain the results presented Table 1, latencies and amplitudes of wrist acceleration/deceleration peaks were determined for each individual trial as illustrated below (left panel), and means were calculated only for peak values. The kinematic profiles in Figure 1, by contrast, were obtained by averaging profiles of normalized individual trials (between 0% and 100%) such that across trials, peak-values were averaged with non peak-values (red curve; right panel). Note that this latter procedure weakens the observed effects.

Analyses underlying the results in Table 1 Analyses underlying data in Figure 1 -8000