Jouilel Naima
email: naima.jouilel@gmail.com

EL Radouani Mohammed
email: m.radouani@ensam-umi.ac.ma

Fahime Benaissa

Geometric Modeling and Contribution to the choice of a Blade profile for a Wind Turbine

Keywords: geometric modeling, NACA profile, simulation, rotor, blade element momentum

The work involves a sensitive part of the wind turbine which is the rotor, specifically selecting the appropriate blade profile for a given wind distribution. A geometric modeling is performed based on a combination of the theories of the axial flow and the theory of the blade member. Then, a simulation of the turbine for different profiles was performed to establish the choice of the profile of the blades in an iterative manner to provide aerodynamic performance by introducing data for the wind distribution of the Essaouira region.

I.

INTRODUCTION The development of renewable energy sources is vital to meet the growing demand for electricity. Having a better use of renewable energy is a challenge that causes continuous development in methods and means used.

Renewable energy in Morocco represented 0.4% of the national energy balance (excluding biomass) and nearly 10% of electricity production in 2007. Renewable energy is supported by strong hydropower sources and the newly installed wind energy parks (147 MW installed and 975 MW under deployment). Morocco plans a $13 billion expansion of wind, solar and hydroelectric power generation capacity and associated infrastructure that should see the country get 42% of its electricity from renewable sources by 2020.

The increase in wind generation remains limited. In fact, the maximum power that can be extracted is 60% (Betz limit) [START_REF] Betz | Das Maximum der theoretisch möglichen Ausnutzung des Windes durch Windmotoren[END_REF]. Several studies have been conducted to develop wind turbines with performance that greatly approximate this limit [START_REF] Yang | Large-eddy simulation of turbulent flow past wind turbines/farms: the Virtual Wind Simulator (VWiS)[END_REF] [3] [START_REF] Wood | Small wind turbines[END_REF].

The work involves a sensitive part of the wind turbine which is the rotor, specifically selecting the appropriate blade profile for a given wind distribution.

The dimensioning of the rotor of a wind turbine is essentially based on aerodynamic theory to define the parameters affecting its overall performance [START_REF] Gourières | Énergie éolienne : Théorie, conception et calcul pratique des installations[END_REF] [START_REF] Nelson | WIND ENERGY: Renewable Energy and the Environment[END_REF].

A geometric modeling is performed based on a combination of the theories of the axial flow and the theory of the blade member. In fact the theory of single axial flow does not yield results for calculating the geometrical characteristics of the rotor.

A simulation of the turbine for different profiles is performed to establish the choice of the profile of the blades and in an iterative manner to provide aerodynamic performance by introducing data for the wind distribution of the Essaouira region.

The expression of the extracted power is given by the equation:

P = 1 / 2.ρ.V 0 3 .S.C P (1)
The purpose of this paper is to illustrate the impact of the profile on the transmitted power from the rotor to the multiplier. Cp is the power coefficient whose maximum value is 16/27 (limit BETZ) [START_REF] Betz | Das Maximum der theoretisch möglichen Ausnutzung des Windes durch Windmotoren[END_REF] it depends on the profile of the blades used.

II. AERODYNAMIC THEORIES FOR CALCULATING THE AERODYNAMIC POWER:

A. Froude Rankine Theorem:

In Rankine-Froude theory [START_REF] Burton | Wind Energy Handbook[END_REF], the rotor is considered such an apparatus modifying the kinetic energy of the fluid passing through it. This theory assumes a one-dimensional flow and perfect and incompressible fluid through the rotor. The surface swept by the blades of the rotor is replaced by a disc permeable factor of a pressure discontinuity.

B. Theorem of blade element :

This theory is based on cutting the blade in installments using cylindrical surfaces and on a study of the flow slice slice [START_REF] Wang | Nonlinear aeroelastic modelling for wind turbine blades based on blade element momentum theory and geometrically exact beam theory[END_REF]. It is assumed that the flow in a ring is independent of the other rings. Consequently, if we get the drag and lift forces for each slice, we can evaluate the integral characteristics of the rotor. For the elemental forces, each slice of the blade is presented as a cylindrical wing subjected to the relative wind created by the speed of at the infinity and by the rotation.

C. Blade Element momentum:

This approach is based on the theory of the blade element, but the relative velocity profile is corrected by the induced speeds. Here, the axial induced velocity is calculated for each elementary ring by applying the theorem of momentum in the axial direction [START_REF] Manwell | Wind energy explained: theory, design and application[END_REF] and [START_REF] Liu | Optimized linearization of chord and twist angle profiles for fixed-pitch fixed-speed wind turbine blades[END_REF]. The tangential induced velocity is calculated using the angular momentum theorem. It thus takes account of the velocity field disturbed by the rotor.

Unlike the one dimensional theory of Froude Rankine, induced speeds vary along the blade. By cons, they are averaged in the azimuthal direction. This theory is now improved by using deferent corrections taking into account the finite number of blades.

III. GEOMETRIC MODELING OF ROTOR:

A. blade parameters:

The dimensioning of a blade must achieve a compromise between lift and drag: have the maximum lift while taking into consideration the strength to prevent delamination of the blades. That is to maximize the ratio: f = Cz / Cx where Cz is the coefficient of lift and Cx drag coefficient A rotor is characterized by the number of blade that we take equal to 3. The calculation of the chord along the blade is made according to the following equation: The shape of the blade profiles determines their aerodynamic performance but also their resistance. The compromise between performance and mechanical strength is at the heart of the problem of the definition and optimization of the geometry of the blade. From aerodynamic or hydrodynamic point of view, the more the profile is thin the less it generates resistance to progress. The profile shape is an essential characteristic of a blade and it greatly influences the aerodynamic characteristics and the performance of an air turbine.

In this study we focus on the NACA profiles. Those are the most used in the aerodynamic field. The shape of the NACA airfoils is described using a series of digits following the word "NACA". The parameters in the numerical code can be entered into equations to precisely generate the cross-section of the airfoil and calculate its properties.

A. Simulation parameters:

The simulation of the turbine first passes by introducing the different parameters including Weibull factors. Based on studies in Morocco, the area that represents a peak wind speed is that of the region of Essaouira. For this reason, we will work with data extracted from this city for geometric modeling and aerodynamics of the turbine [START_REF]Metrological data of ESSAOUIRA region[END_REF].

The wind distribution follows a Weibull distribution. The figure 6 shows the law of the wind in the region of Essaouira at a height of 100m: The BEM model (blad element momentum) is used to test and develop different rotor designs and compare them by introducing small changes. This iterative approach allows the two-dimensional analysis of the blades. For the simulation, we will based on the range of the wind [1m / s, 22m / s] equivalent to that recorded in the region of Essaouira and this for a wind turbine rotor with a horizontal axis. The simulation results include:

 Cp: power coefficient  Ct: thrust coefficient  TSR: Tip Speed Ratio According to the graph of Cp as a function of TSR, the profiles 4306 and 4412 reach maximum values of Cp which implies a maximum value of the power (up to 50% of the kinetic power of the wind) with a difference of 10 % compared to the two other profiles.

From the results found above, we can clearly see the influence of the profile on the overall performance of the wind turbine. The profiles of symmetrical shape such as the NACA 0012 profile has a high confidence level and a poor power factor while non-symmetric profiles such as the NACA 4412 exhibit a high drag coefficient.

CONCLUSION

By introducing the meteorological data of the region of Essaouira for a horizontal axis wind turbine and for different profiles of the NACA four-digit family, the system response totally change from one profile to another. The one giving the maximum drag is the NACA 4412. It should be noted that there is another factor influencing the choice of the profile is that of cost: the manufacture of NACA 00XX profiles is cheaper than the NACA 44XX profiles.

Figure 1 :

 1 Figure 1: Substitution of the area swept by the rotor blades by a disc permeableTo express the power P of the rotor based on the coefficient of induced axial velocity, we use the momentum theory and Bernoulli's equation. With this method it is possible to express the power of the rotor based on the coefficient of induced axial velocity. It is also possible to show that the maximum power extracted by the rotor does not exceed 16/27 of the available power (Betz law) for an induced speed equal to one third of the speed infinity upstream[START_REF] Betz | Das Maximum der theoretisch möglichen Ausnutzung des Windes durch Windmotoren[END_REF].

Figure 2 :

 2 Figure 2: Efforts suffered by a blade member of a wind rotor subjected to wind

Figure 3 :

 3 Figure 3: Ring scanned by a blade member

C = 8 Figure 4 :

 84 Figure 4: Introduction of geometric parameters of the rotor in Q-Blade B.Profile choice:

Figure 5 :

 5 Figure 5: Characteristics of a NACA profile To illustrate the influence of the choice of the profile on the performance of a horizontal axis wind turbine for a given wind distribution, a simulation was performed for different profiles of the NACA four-digit family:  NACA 0012  NACA 2206  NACA 4306  NACA 4412

Figure 6 :

 6 Figure 6: Wind distribution for ESSAOUIRA region Where: K Weibull shape factor. It gives the shape of the distribution and has a value between 1 and 3. C is the Weibull scale factor expressed in m / s. It can express the chronology of a characteristic speed. In the case of Essaouira  The form factor: K = 2.01  The scale factor: C = 9.06 m / sThe BEM model (blad element momentum) is used to test and develop different rotor designs and compare them by introducing small changes. This iterative approach allows the two-dimensional analysis of the blades. For the simulation, we will based on the range of the wind [1m / s, 22m / s] equivalent to that recorded in the region of Essaouira and this for a wind turbine rotor with a horizontal axis. The simulation results include: Cp: power coefficient  Ct: thrust coefficient  TSR: Tip Speed Ratio

Figure 7 :

 7 Figure 7: The shape of the four NACA profiles

Figure 8 :Figure 9 :

 89 Figure 8: Cm and Cl/Cd evolution


Dahbi Samya

Moussami El
email: hajelmoussami@yahoo.com

Haj

Ezzine Latifa
email: latifae@yahoo.com

Optimization of turning parameters for surface roughness

Keywords: Optimization, Taguchi Design of Experiments, Regression model, surface roughness, turning. I

In this paper, Taguchi Design is used to identify the optimal combination of turning parameters to minimize the surface roughness. Turning experiments are carried out according to Taguchi orthogonal array L 9 for various combinations of four parameters: cutting speed, feed rate, depth of cut and nose radius. For each experiment run, the surface roughness Ra is measured, recorded and analyzed using Taguchi S/N ratios. To confirm the effectiveness of the Taguchi optimization, confirmation test and regression model are used.

INTRODUCTION

Turning is one of the widely used machining processes in industries. In turning, a single-point cutting tool removes material from the surface of a rotating cylindrical work piece. The cutting tool is fed linearly in a direction parallel to the axis of rotation. Turning is carried out on a lathe that provides the power to turn the work piece at a given rotational speed and to feed the cutting tool at a specified rate and depth of cut. Therefore, three cutting parameters, i.e. cutting speed, feed rate and depth of cut need to be determined in a turning operation. Fig. 1 shows a basic turning operation.

In turning, the important task is to select cutting parameters in order to achieve the highest cutting performance such as surface roughness, material removal rate, tool wear and power consumption. Habitually, conducting experiments or using a handbook can determine the required cutting parameters. Unfortunately, these means don't lead to optimal cutting performance for a particular machine tool and environment. Surface roughness remains the main indicator of machined component quality. A low surface roughness improves the tribological properties, fatigue strength, corrosion resistance and esthetic appeal of the product [START_REF] Groover | Fundamentals of Modern Manufacturing[END_REF].

Literature is very rich in terms of turning operation owing to its importance in metal cutting. The three important process parameters in this research are speed, feed rate and depth of cut. Surface roughness of a turned work piece is dependent on these process parameters and also on tool geometry: nose radius, rake angle, side cutting edge angle and cutting edge. It also depends on the several other exogenous factors such as: work piece and tool material combination and their mechanical properties, quality and type of the machine tool used, auxiliary tooling, lubricant used and vibrations between the work piece, machine tool and cutting tool [START_REF] Shaw | Metal Cutting Principles[END_REF][START_REF] Fang | A new algorithm for developing a reference model for predicting surface roughness in finish machining of steels[END_REF][START_REF] Thiele | Effect of cutting edge geometry and work-piece hardness on surface generation in the finish hard turning of AISI 52100 steel[END_REF][START_REF] Chen | Cutting forces and surface finish when machining medium hardness steel using CBN tools[END_REF][START_REF] Darwish | The impact of the tool material and the cutting parameters on surface roughness of supermet 718 nickel superalloy[END_REF][START_REF] Feng | Development of empirical models for surface roughness prediction in finish turning[END_REF][START_REF] Kopac | Optimal machining parameters for achieving the desired surface roughness in fine turning of cold preformed steel work pieces[END_REF].

Several studies proposed optimization of cutting parameters for surface roughness and other cutting performance [START_REF] Sahoo | Modeling and optimization of Al/SiCp MMC machining using Taguchi approach[END_REF][START_REF] Rao | Application of Taguchi methods and ANOVA in optimization of process parameters for metal removal rate in electrochemical machining of Al/5%SiC composites[END_REF][START_REF] Motorcu | The evaluation of the effects of control factors on surface roughness in the drilling of Waspaloy superalloy[END_REF].

The influence of process parameters like cutting speed, feed rate and depth of cut on flank wear and surface roughness in turning was studied in [START_REF] Sahoo | Modeling and optimization of Al/SiCp MMC machining using Taguchi approach[END_REF]. The experiments were carried out based on Taguchi orthogonal array. The research provided optimized parameter solution with the utilization of uncoated tungsten carbide inserts during machining Al/SiCp metal matrix composite under dry cutting conditions.

The study conducted in [START_REF] Rao | Application of Taguchi methods and ANOVA in optimization of process parameters for metal removal rate in electrochemical machining of Al/5%SiC composites[END_REF] discussed an application of the Taguchi design to investigate the effects of process parameters on the metal removal rate value, in the electrochemical machining of LM6 Al/5%SiC composites. It also provided the optimal values of voltage, feed rate and electrolyte concentration that lead to the maximum metal removal rate.

The effects of cutting tool, cutting speed, feed rate and drill bit angle on the surface roughness were investigated by [START_REF] Motorcu | The evaluation of the effects of control factors on surface roughness in the drilling of Waspaloy superalloy[END_REF]. This study concerned the drilling of Waspaloy superalloy. The experiments were carried out according to Taguchi design. The optimum levels of control factors for minimum surface roughness were defined by using S/N ratios.

Unfortunately, the previous studies included just the three major parameters (cutting speed, feed rate and depth of cut) and neglect an important one: tool nose radius, which has a considerable effect on surface roughness. Moreover, a large number of turning experiments is required to develop mathematical models. So, this mean is very costly in terms of time and materials. In this study, an alternative optimization approach, based on the Taguchi Design of Experiments (DoE), is used to determine the optimal combination of parameters.

Taguchi DoE is a powerful tool for the design of high quality systems [START_REF] Taguchi | Introduction to quality engineering[END_REF]. It provides a simple, efficient and systematic approach to optimize designs for performance, quality, and cost. Taguchi proposed a set of orthogonal arrays to investigate the effect of parameters on specific quality characteristics and determine the optimum parameters combination. These arrays use a small number of experiments which save time and resources. However, these arrays don't test all variables combinations and interactions which could make their risk of error very large. Then, these designs should not be used when all relationships between all variables are needed.

In our study, Taguchi DoE is adopted to identify the optimal combination of turning parameters (cutting speed, feed rate, depth of cut and nose radius) to optimize surface roughness. First, we present the Taguchi Design. Next, we describe the experimental system and results of optimization. Finally, the paper concludes with conclusion and future work.

II. METHODS

In our optimization of surface roughness, we included four cutting parameters:

 Cutting speed V;  Feed rate f;  Depth of cut a;  Nose radius r.

A. Modeling of surface roughness

The surface roughness average Ra was taken as a parameter, defined on the basis of the ISO 4287 Norm [START_REF]Geometrical Product Specifications (GPS)-Surface Texture: Profile Method-Terms, Definitions and Surface Texture Parameters[END_REF] as the arithmetical mean of the deviations of the roughness profile from the central line l m along the measurement. This definition is set out in [START_REF] Groover | Fundamentals of Modern Manufacturing[END_REF], where y(x) is the profile value of the roughness profile and L is the evaluation length. Characteristics of roughness profile are shown in Fig. 2: Equation ( 2) represents an exponential empirical model for surface roughness that was suggested in [START_REF] Fang | A new algorithm for developing a reference model for predicting surface roughness in finish machining of steels[END_REF].

Ra = 1/L ∫│y(x) │dx (1)
Ra = C.V n .f m .a p (2)
Where C, n, m and p are indexes which describe the empirical model.

A widely known empirical model to estimate the average surface roughness is given in (3) [START_REF] Boothroyd | Fundamentals of Machining and Machine Tool[END_REF][START_REF] Whitehouse | Handbook of Surface Metrology[END_REF]:

Ra = f 2 /32r (3)

B. Description of Taguchi Design

The Full Factorial Design with four factors and three levels needs 81 experiments to carry out. This high number takes time and requires a very high cost. Therefore, Taguchi proposed a unique design of orthogonal array to investigate the full parameter with a small number of experimental trials. This design reduces the experimental process, minimizes the effects of factors that cannot be controlled and identifies significant factors quickly [START_REF] Taguchi | Introduction to quality engineering[END_REF]. The Taguchi's quality loss function approach was performed to calculate the deviation between the experimental values and the optimal values. This function is further regenerated into a Signal-to-Noise (S/N) ratio and the parameters are evaluated based on the S/N ratio.

The S/N ratio indications can be separated into three groups, called performance characteristics:

 The Smaller-the-Better,  The Larger-the-Better,  The Nominal-the-Better.

Regardless of the type of the performance characteristic, a large S/N ratio means it is close to good quality. Then, a higher value of the S/N ratio is desirable.

Generally, the steps applied for Taguchi optimization are as following:

 Select parameters and their levels;  Select Taguchi orthogonal array;  Conduct experiments and measure the response to optimize;  Analyze results by using Signal-to-Noise ratio;  Identify optimum levels of parameters;  Confirm the effectiveness of optimization with a confirmation test.

In our study, four parameters with three levels each were considered which correspond to Taguchi L 9 orthogonal array.

C. Full Factorial Design and Multiple Regression Model

Full Factorial Design is an experiment whose design consists of two or more factors, each with discrete possible levels, and whose experimental units take on all possible combinations of these levels across all such factors. This factorial design allows his user to study the effect of each factor on the response variable, as well as the effects of interactions of factors on the response variable [START_REF] Montgomery | Design and Analysis of Experiments[END_REF].

Most of factorial experiments use only two levels for each factor. The number of combinations is denoted by 2 n , where n is the number of independent variables. If the number of combinations in a full factorial design is too high to be logistically executed, a fractional factorial design may be done, in which some of the possible combinations (usually at least half) are omitted.

Multiple Regression is a statistical method that allows us to determine the correlation between a continuous dependent variable and two or more continuous or discrete independent variables. It can be used for a variety of purposes such as analyzing of experimental, ordinal, or categorical data [START_REF] Saporta | Probabilités, analyse des données et Statistiques[END_REF].

In our study, the purpose of using the Full Factorial Design and Regression Model was to confirm the effectiveness of Taguchi optimization. We used a design of four factors with two levels each and we developed a regression model with interactions between factors.

III. EXPERIMENTAL SYSTEM

A. Material of work piece and cutting tool

For the work pieces, AISI 1042 Steel (58.55 HRC) in the form of round bars with 50 mm diameter and 40 mm of cutting length are used for turning operations. This type of steel is chosen due to its high machinability and commercial availability. The chemical composition of this material is as following:

 Carbon C: 0.40 -0.47 %,  Iron Fe: 98.5 -99.0 %,  Manganese Mn: 0.6 -0.9 %,  Phosphorus P: 0 -0.04 %,  Sulfur S: 0 -0.05 %. We used the same material for all work pieces to guaranty the same experimental conditions for all experiments. Indeed, from a type of steel to another, the physico-chemistry composition changes and can induce variability in the experimental process.

For the cutting tool, standard carbide tool inserts CNMG120402, CNMG120404 and CNMG120408 were used for turning experiments with nose radius of 0.2 mm, 0.4 mm and 0.8 mm, respectively.

B. Cutting conditions and measurement

Turning experiments were conducted on a Computer Numerical Controlled (CNC) Lathe, in the Laboratory of Mechanical Manufacturing of ENSAM, Moulay Ismail University, Meknes, Morocco. Photographs of CNC Lathe and experimental system are shown in Fig. 3 and Fig. 4, respectively. For each parameter, three levels were selected and their low-medium-high levels are shown in Table I. The selection of levels of machining parameters took into consideration the material of both work piece and cutting tool. The experiments were conducted without lubricant.

The work surface was characterized with roughness parameters as the arithmetic surface roughness average Ra. After each turning operation, Ra was measured by a Roughness Tester. The measurement is shown in Fig. 5.

C. Experimental data

In order to optimize the surface roughness, we used the L 9 Taguchi orthogonal array to conduct the experiments. Experimental combinations of the turning parameters and the measurement of surface roughness Ra are reported in Table II.

To confirm the effectiveness of Taguchi optimization, we used a full factorial design of the same parameters used in Taguchi optimization, but we were restricted to two levels (low and high). Then, 16 experiments were carried out to develop the regression model. This model will be used later to confirm the Taguchi optimization.

IV. RESULTS AND DISCUSSIONS

Experimental data was analyzed according to Taguchi Design by using Minitab 17, statistical analysis software widely exploited in many engineering optimizations.

In the Taguchi method, the term 'signal' represents the desirable value (mean) for the output characteristic and the 'noise' represents the undesirable value for the output characteristic. Taguchi uses the S/N ratio to measure the quality characteristic deviating from the desired value. First, Signal to Noise S/N ratios were determined by ( 4) with consideration of Lower-the-Better performance characteristic in terms of dB (decibel) [START_REF] Taguchi | Introduction to quality engineering[END_REF]. We chose this characteristic because Ra value is requested to be low. The experimental values and their corresponding S/N ratios are listed in Table III.

S/N LB = -10*log (1/n ∑ Ra i ² ) (4)
Where Ra is the value of surface roughness for the i th test in a run. In our case, n=1.

Next, the Mean S/N ratio at each level of cutting parameters was obtained by averaging at corresponding levels. For example: Mean S/N ratio for cutting speed V at the low level was calculated by averaging the S/N ratios for the experimental runs 1, 2 and 3, respectively. Similarly, the Mean S/N ratio for each level of other parameters can be computed.

Mean S/N ratios are presented in Table IV and their main effects plot are shown in Fig. 6.

Regardless of the type of the performance characteristic, a large S/N ratio means it is close to good quality. Then, a higher value of the S/N ratio is desirable.

From the Table IV and Fig. 6, optimal turning parameters values for surface roughness are highlighted and are as following:

 Cutting Speed V: 200 m/min;  Feed rate f: 0.15 mm/rev;  Depth of cut a: 2 mm;  Nose radius r: 0.4 mm. The confirmation test is the final step in verifying the optimal levels of parameters identified by Taguchi design. It is a crucial step and is highly recommended by Taguchi to verify the effectiveness of optimization.

In this study, a confirmation test was conducted by using the levels of the optimal parameters and we obtained: Ra exp = 2.15 μm. Fig. 6. Main Effects Plot for S/N ratios.

Compared to the nine previous experiments in the L 9 array, this value is not minimum, but it can be used for industrial applications which require this level of surface roughness. This result is due to the orthogonality of Taguchi array which doesn't include interactions between parameters.

The empirical and widely used model [START_REF] Boothroyd | Fundamentals of Machining and Machine Tool[END_REF][START_REF] Whitehouse | Handbook of Surface Metrology[END_REF] given in [START_REF] Chen | Cutting forces and surface finish when machining medium hardness steel using CBN tools[END_REF] indicates that the effect of interaction between feed rate f and nose radius r is very important and cannot be neglected. A small value of f and a high value of r decrease significantly the surface roughness. Ra = f 2 /32r

(5)

In addition, we developed a regression model of the third order by using the full factorial design. 16 experiments were carried out and the developed model with interactions between parameters is given in (6): Ra=10.34-56.1f-4.77a-18.35r-0.1643V.f+32.66f.a +129.2f.r+8.61a.r+0.03054V.a.r-67.22f.a.r [START_REF] Darwish | The impact of the tool material and the cutting parameters on surface roughness of supermet 718 nickel superalloy[END_REF] This model has a great coefficient of determination of 99.55% and an error of 0.07. Errors between estimated and experimental values of surface roughness are represented in Fig. 7. These performances support the choice of this regression model to confirm the effects of interactions between parameters on surface roughness. Indeed, it reveals that the second order interaction (f.r) and the third order interaction (f.a.r) have the main effects on surface roughness.

V. CONCLUSION Is this paper, Taguchi Design of Experiments was applied for turning parameters to obtain the optimal surface roughness.

For our optimization, we selected four turning parameters: Cutting speed, feed rate, depth of cut and nose radius. For each parameter, we selected three levels: low, medium and high.

Experiments were conducted using L 9 orthogonal array. For each experiment, surface roughness was measured, recorded and analyzed using Taguchi S/N ratios. These ratios were calculated with consideration of performance characteristic: Lower-the-Better, as surface roughness is requested to be low.

The optimum levels of parameters for minimizing the surface roughness were determined from the response table for Signal-to-Noise ratios. The best combination was obtained with:

 Cutting speed of 200 m/min,  Feed rate of 0.15 mm/rev,  Depth of cut of 2 mm,  Nose radius of 0.4 mm.

To confirm the effectiveness of our optimization, we followed two ways:  Confirmation experiment,  Development of regression model with interactions between parameters.

Confirmation experiment revealed that Taguchi design cannot identify effectively the optimal parameters as the optimal turning parameters didn't lead to the minimal surface roughness. This result is due to the L 9 Taguchi orthogonal array, which doesn't include interactions between parameters.

To illustrate the effects of interactions between parameters, we developed a regression model of the third order. It had a great coefficient of determination and a low error, which allowed us to confirm that interactions between turning parameters affect significantly the surface roughness. Indeed, we found that the interaction between feed rate f and nose radius r had the major effect on surface roughness.

From these results, we concluded that it is crucial to consider the effects of interactions between turning parameters in optimization of surface roughness. This will be the objective of our future study, where optimization will be performed with other methods that include interactions between parameters.

Fig. 1 .

 1 Fig. 1. Basic turning operation.

Fig. 2 .

 2 Fig. 2. Roughness profile characteristics.

Fig. 3 .

 3 Fig. 3. Photograph of the CNC Lathe.

Fig. 4 .

 4 Fig. 4. Experimental system.

Fig. 5 .

 5 Fig. 5. Measurement of surface roughness by Roughness Tester. The measurement was repeated thrice at different locations for each work piece and average value is reported. Details of setting parameters of measure are as following:  Cut-off length = 0.8 mm; Cut-off number = 5;  Standard: ISO 4287; Speed = 1 mm/s.

Fig. 7 .

 7 Fig. 7. Error of the developed regression model.

TABLE I .

 I CUTTING PARAMETERS AND THEIR LEVELS.

				Levels
		Parameters	Low	Medium	High
	V	Cutting speed (m/min)	100	150	200
	f	Feed rate (mm/rev)	0.1	0.15	0.2
	a	Depth of cut (mm)	1	1.5	2
	r	Nose radius (mm)	0.2	0.4	0.8

TABLE II .

 II EXPERIMENTAL DATA.

	Run	V	f	a	r	Ra
	Order	(m/min)	(mm/rev)	(mm)	(mm)	(μm)
	1	100	0.10	1.0	0.2	3.575
	2	100	0.15	1.5	0.4	1.212
	3	100	0.20	2.0	0.8	1.301
	4	150	0.10	1.5	0.8	1.894
	5	150	0.15	2.0	0.2	0.993
	6	150	0.20	1.0	0.4	1.654
	7	200	0.10	2.0	0.4	0.618
	8	200	0.15	1.0	0.8	0.713
	9	200	0.20	1.5	0.2	1.483

TABLE III

 III

	.	EXPERIMENTAL RESULTS AND CORRESPONDING
			S/N RATIOS.
	Run Order	Ra (μm)	S/N ratio (dB)
	1		3.575	-11.0647
	2		1.212	-1.6701
	3		1.301	-2.2855
	4		1.894	-5.5461
	5		0.993	0.0581
	6		1.654	-4.3690
	7		0.618	4.1755
	8		0.713	2.9382
	9		1.483	-3.4209

TABLE IV .

 IV MEAN S/N RATIOS FOR SURFACE ROUGHNESS.

	Levels	V	f	a	r
	Low	-5.0068	-4.1451	-4.1652	-4.8092
	Medium	-3.2856	0.4421	-3.5457	-0.6212
	High	1.2310	-3.3585	0.6494	-1.6311
	Delta	6.2377	4.5872	4.8145	4.1880
	Rank	1	3	2	4

Xème Conférence Internationale : Conception et Production Intégrées, CPI 2015, 2-4 Décembre 2015, Tanger -Maroc. Xth International Conference on Integrated Design and Production, CPI 2015, December 2-4, 2015, Tangier -Morocco. Identify applicable sponsor/s here. If no sponsors, delete this text box (sponsors).


Amal Gouiaa-Mtibaa

Sofiene Dellagi
email: sofiene.dellagi@univ-lorraine.fr

Zied Achour
email: zied.achour@univ-lorraine.fr

Joint optimization for maintenance planning and quality deterioration

Keywords: Integrated maintenance strategy, Quality deterioration, Optimization

This paper investigates an integrated strategy joining non quality effects and preventive maintenance (PM) policy. We consider a single machine subject to random failure rate and producing a progressive deteriorating products. A preventive maintenance (PM) strategy with minimal repair is applied with non negligible durations of maintenance tasks. This study consists on developing an analytical model in order to determine an optimal integrated maintenance plan taking into account the quality deterioration of products. This strategy consist on selling products at a discount price due to the loss of quality caused by the machine degradation. The objective is to determine the optimal number of batches produced N* before each preventive maintenance action which maximize the total profit (PT) per time unit for a full cycle T. A numerical example is presented in order to illustrate the proposed model. A sensitivity study is used to evaluate the influence of model parameters.

I. INTRODUCTION

The increased global competition calls farms to manage several functional areas successfully such as maintenance, quality, production and marketing. Consequently, managers and decision makers have to consider new management approaches integrating the interaction between parts or all of those functions. Quality and Maintenance are important aspects in any industrial process. They have been widely studied separately during decades. In fact, the earliest work on maintenance policies was elaborated by Barlow [4] and since that researchers has allocated considerable efforts on developing and optimizing preventive maintenance models which contributed to a good understanding of the properties and effectiveness of preventive maintenance policies under various conditions. During the last decade, practitioners and academicians recognized that there is a strong relationship between product quality and equipment maintenance. In fact, strategies dissociating maintenance and quality were imperfect and ineffective and it is commonly argued that preventive maintenance (PM) policies can contribute significantly in increasing equipment reliability as well as product quality. In addition, maintenance is considered among the three main reasons of quality deterioration [START_REF] Ollila | Maintenance has a role in quality[END_REF]. Then, an effective integration of these two components represent the favored means to avoid quality deficiencies. In this perspectives, the need for developing new integrated maintenance-quality strategies became evident and the simultaneous consideration of maintenance policies and quality deterioration has recently become an important research area.

Our objective in this study is to develop new analytical model joining maintenance and quality simultaneously for a manufacturing system composed of a single machine producing a single product. The machine is subject to progressive degradation according to an increased random failure rate which affects the product quality. We consider a PM strategy with minimal repair. The preventive and corrective actions have non-negligible durations. We aim to determine the optimal preventive maintenance plan taking into account maintenance costs and non-quality effects by determining the optimal number of batches produced before each preventive maintenance action.

The reminder of this paper is organized as follow: next section gives an overview of works that study maintenance policies and the integrated maintenance strategies. Section 3 introduced the problem statement and the notation used. In section 4, we present an analytical model. Section 5 is dedicated to present numerical example to illustrate the proposed model and results obtained and in the last section we present a sensitivity study to show the impact of the variation of some parameters on the optimal solution.

Motivation

Practitioners and academicians recognize that the close link between product quality and equipment maintenance should no more go unnoticed. In fact, the integration of these two aspects is economically beneficial to organizations. Then, more research considering simultaneously maintenance policies and quality deterioration needs to be conducted to highlight this relationship. In this study, we present new mathematical model joining quality deterioration and maintenance policies in order to maximize the total benefit. The objective is to determine the optimal preventive maintenance plan taking into account maintenance cost and non quality effect.

II. LITERATURE REVIEW

In this section, we present a state of the art of researchers which studied maintenance optimization models and integrated models. Maintenance as a separate problem has widely discussed in the literature over the last decade. Works on maintenance policies have started with Barlow et al. [4] in 1960 and it was achieved with a huge number of contribution. M. Vasili et al. [START_REF] Vasili | Maintenance optimization models : a review and analysis[END_REF] present a brief review of existing maintenance optimization models: They discussed several reliable models and methods and investigated future prospects in this area. In Dekker [START_REF] Dekker | Quality oriented maintenance scheduling[END_REF] a review on applications of maintenance optimization is given.

Production, quality, and maintenance policy are important components of a manufacturing system. Companies' decision makers and researchers have realized that strategies dissociating these functional areas are no more efficient. The research community has allocated considerable efforts on modeling and optimizing integrated models in order to understanding and solving these problems. Some researchers have studied models joining maintenance and quality, others have studies the combination of maintenance and production and considerable works have interested in integrated models joining these three aspects.

M. Ben Daya [START_REF] Ben-Daya | Integrated production maintenance and quality model for imperfect process[END_REF] proposed an integrated model for the joint determination of economic production quantity and preventive maintenance level for imperfect production process. He proved that performing maintenance gives way to a reduction of quality control related cost. M. Radhoui et al. [START_REF] Radhoui | Integrated maintenance and control policy based on quality control[END_REF] studied a joint preventive maintenance and quality control policy for a manufacturing system producing conforming and non-conforming products. The number of non-conforming items is the decision variable for the maintenance intervention. A buffer stock is constructed whenever the rate of non-conforming products attains a specific limit. The objective was to determine this limit and the size of buffer stock that minimize the overage total cost per unit time integrated maintenance quality and inventory cost. A. Chelbi and N. Rezg [START_REF] Chelbi | Analysis of a production/Inventory system with randomly failing production unit subject to a minimum required availability level[END_REF] consider a policy based on the age of the equipment. They determine the optimal age of the system, the most appropriate time to be submitted to preventive maintenance, and the optimal size of the buffer stock. The aim is to minimize the total cost per time unit, taking into account a minimum required system availability level. A.Ollila and M. Malmipuro [START_REF] Ollila | Maintenance has a role in quality[END_REF] present in their study that maintenance has a major impact on product quality. In a case study, they showed that the maintenance is among the three most important causes of quality deterioration.

Control chart as a commonly applied tool of statistical process control has been extensively used to monitor and even reduce process variation by identifying and eliminating sources of variation. Many models addressing statistical process control and maintenance simultaneously have appeared in the literature. The first model for the economic design of X bar control charts was introduced by J. A. Duncan [START_REF] Duncan | The economic design of x-bar charts used to maintain current control of a process[END_REF]. Duncan's model stimulated most of the work in this area and has been extended by several authors. For most case, its not possible to separate statistical process control from preventive maintenance [START_REF] Rahim | Integrated models in production planning, inventory, quality and maintenance[END_REF]. M. Ben Daya [START_REF] Ben-Daya | Integrated production maintenance and quality model for imperfect processes[END_REF] developed an integrated model for the joint optimization of the economic production quantity, the economic design of x-control chart, and the optimal maintenance level, for a deteriorating process where the incontrol period follows a general probability distribution with increasing hazard rate. In addition, M. A. Rahim [START_REF] Rahim | Joint determination of production quantity, inspection schedule, and control chart design[END_REF] and M. Ben-Daya and M. A. Rahim [START_REF] Ben-Daya | Effect of maintenance on the economic design of x-control chart[END_REF] proposed an integrated model based on X control chart and preventive maintenance in which the in-control time follows a probability distribution with increasing hazard rate. K. Linderman et al. [START_REF] Linderman | An integrating systems approach to process control and maintenance[END_REF] developed an integrated model, which applies an EWMA control chart, to reduce cost. They assumed perfect maintenance and expressed three scenarios for their model. In two scenarios, process continues until planned maintenance time. In the third scenario, control chart detects out-of-control condition.

III. PROBLEM STATEMENT

A. Description of the problem

We consider a single machine producing a single product type and subject to random failures. It is assumed that the product quality is subject to an increasing deterioration due to the machine degradation. Each batch produced loses quality compared to the previous and it is sold at a discount price. The relation between the price of two consecutive batches is :

P i = α i × P i-1 .
with α i is the quality degradation ratio of the i th batch.

We note that :

P 1 = α 1 × P max P 2 = α 2 × P 1 = α 2 × α 1 × P max . . P N = α N × α N -1 × .. × α 1 × P max Consequently P i = i j=1 α j × P max .
We assume that the machine failure rate have an increased failure rate following Weibull distribution. The choice of this distribution law is explained by the importance of the weibull distribution in characterizing equipement failure [START_REF] Dodson | Weibull Analysis[END_REF] and its applicability to a wide range of industrial settings [START_REF] Williams | Economic control chart designs and the in-control time distribution: A sensitivity study[END_REF]. The failure rate λ(t) is given by ( 1)

λ(t) = b × t b-1 (1)

With

• b: Shape parameter

• : Scale parameter We recall that we adopt an increasing failure rate and we assume that b ≥ 2. We applied a (PM) policy with minimal repair at failures with non-negligible durations of preventive and corrective maintenance tasks. Preventive maintenance actions are performed each N batches produced. Following each PM action, the manufacturing system is considered restored to a state as good as new and the product quality returns to 100%. The corrective maintenance action is undertaking only when a breakdown occurs. Our strategy is illustrated in figure [START_REF] Radhoui | Integrated maintenance and control policy based on quality control[END_REF].

B. Objective

The objective of this study is to determine the optimal number of produced batches before the preventive maintenance action taking into account the preventive maintenance cost, the selling price and the quality deterioration degree of products. We aim to maximize the total profit per time unit for a full cycle.

IV. ANALYTICAL STUDY

In this section, we formulate a mathematical model joining maintenance and quality. The aim is to find analytical model that enable us to determine the optimal value of the decision variable N* maximizing total profit (PT) per time unit for a full cycle.

A. Notation

In order to develop the analytical model related to our problem, we present the following notations : The expected total profit by time unit for this integrated model is given by (2):

P T = S p -(M p + C cm + CP ) T (2)
With

S p = N i=1 P max × i j=1 α j C cm = M c × N * P roc 0 λ(t)dt CP = C p × N T = N × P roc + µ p + µ c × N * P roc 0 λ(t)dt
The expected total profit per time unit for this model is presented by the equation ( 2).

P T (N ) = N i=1 P max × i j=1 α j N × P roc + µp + µ c × N * P roc 0 λ(t)dt - M p + M c × N ×P roc 0 λ(t) dt + C p × N N × P roc + µp + µ c × N * P roc 0 λ(t)dt (3)
We assume that we have a constant degradation of the product quality. We note α the overage ratio of quality degradation. Then, the total profit (PT) can be written as :

P T (N ) = N i=1 P max × α i-1 N × P roc + µ p + µ c × N ×P roc 0 λ(t) dt - M p + M c × N ×P roc 0 λ(t) dt + C pu × N N × P roc + µ p + µ c × N ×P roc 0 λ(t) dt (4)
The optimal number of batches N* will be obtained by maximizing (3).

Lemma 1. There exist an optimal solution maximizing the total profit (P T ) par time unit.

Proof. We recall that b is the shape parameter of the failure rate λ(t).

-For b= 2 lim

N →0 P T (N ) = -Mp µp lim N →+∞ P T (N ) = -Mc µc -For b > 2 lim N →0 P T (N ) = -Mp µp lim N →+∞ P T (N ) = -∞
Hence, for all b ≥ 2, there exists a finite N * ∈]0, +∞[ which maximize P T (N ).

V. NUMERICAL EXAMPLE

A numerical example is presented in this section in order to illustrate the analytical model developed in the previous section. The imput data for the numerical example are summarized in tableI below ('mu' stand for monetary units and 'tu' for time unit). We have used MATHEMATICA ® software to obtain the optimal solution. Solving the problems formulated by equation (3), we obtained optimal results presented in tableII. This table shows that the preventive maintenance action must be taken every 7 batches. The figure 2 presents the variation of the total profit (PT) for a full cycle. It shows clearly the existence of a maximum which corresponds to N*. We recall that the aim of our work is to found the optimal number of batches produced N* before the preventive maintenance action which maximizes the total profit (PT) per time unit.

VI. SENSITIVITY STUDY

In this section, we intend to study the sensitivity of the proposed model to the variation of some parameters including the selling price P max , corrective maintenance cost M c and the quality degradation ratio α.

the variation of model's parameters are presented in following tables IV, V andVI. Stationary variables used in this sensitivity study are grouped in table III. Given the results shown in tables IV,V, VI , we note that when we increase the selling price, the number of batches produced before making a preventive maintenance decreases and the total expected profit for a full cycle by time unit increases.

On the other hand, the increase of the corrective maintenance cost results the decrease of the number of batches produced before preventive intervention and the total profit. Moreover, when we increase the quality degradation ratio, the number of batches produced increases as well as the total profit. Table VII summarizes the influence of the variation of some parameters on the final results.

VII. CONCLUSION

The current paper presented an integrated maintenance quality problem applied to manufacturing system subject to progressive degradation according to Weibull distribution law of failure. We an integrated optimization of the maintenance schedule and the quality deterioration in order to derive an optimal integrated maintenance-quality strategy taking into account the selling price, preventive maintenance cost and quality deterioration. Our approach consists on selling products at a discount price due to the loss of quality caused by the machine degradation and the objective is to determine the optimal number of batches produced before each preventive maintenance action. The contribution of this study is the planning of the preventive maintenance action after a number of batches produced contrarily to previous works in which the preventive maintenance action is related to the time. In addition, the preventive and corrective maintenance actions have non negligible durations. The proposed model has been illustrated with a numerical example. A sensitivity study is used to present the influence of the variation of some model parameters. Extensions to this work are under consideration. One of them consists in the investigation of situations in which preventive maintenance actions are imperfect.

Fig. 1 .

 1 Fig. 1. Problem statement

P

  T : Average total profit per time unit of a full cycle S p : Total selling price CP : Total Production cost C p : Unit production cost M p : Preventive maintenance cost C cm : Corrective maintenance cost M c : Corrective maintenance action cost T : Average duration of a full cycle λ(t) : Failure rate b a λ(t)dt : Average number of failure per unit of time during [a, b] N : Number of batches produced in a full cycle Proc : Average processing time µ p : Average duration of preventive maintenance action µ c : Average duration of corrective maintenance action P max : The selling price of the first batch P i : The selling price of the i th batch α j : Degradation quality ratio of j th batch ∈ [0, 1] α : Overage degradation of quality B. Mathematical model The expected total profit per time unit (PT) for the first integrated model includes: • Total selling price • Corrective maintenance Cost • Preventive maintenance cost • Total production cost

Fig. 2 .

 2 Fig. 2. The variation of total profit by time unit (PT) for a full cycle

TABLE VI RESULT

 VI OF THE VARIATION OF Mc

	Mc	α	Pmax	N *	PT *
	1200 0.92	450	7	35.74
	2000 0.92	450	6	34.3

TABLE VII IMPACT

 VII OF THE VARIATION OF Pmax, Mc AND α

	Pmax	N*	PT*
	Mc	N *	PT *
	α	N *	PT *

Xème Conference Internationale : Conception et Production Intégrées, CPI 2015, 2-4 Décembre 2015, Tanger -Maroc. Xth International Conference on Integrated Design and Production, CPI 2015, December 2-4, 2015, Tangier -Morocco.


Ikram Tajri
email: tajriikram@gmail.com

Abdelghani Cherkaoui
email: cherkawi@emi.ac.ma

Proposition d'un modèle sur la complexité de la relation -Lean, Entreprise, Employé & Ergonomie Cognitive‖-Cas d'une PME marocaine dans le secteur thermomécanique

Keywords: Lean, performance, stress, ergonomie cognitive, aides cognitive I

Dans un contexte de concurrence mondiale, maintenir ou renforcer sa compétitivité tout en conservant ses marges devient une priorité pour toute entreprise. Parmi toutes les pratiques, le système Lean a incontestablement fait ses preuves. Il rend l'entreprise plus performante en termes de qualité, de coûts et de délais par une gestion rationnelle des flux. Si l'impact positif du Lean sur la performance de l'entreprise a été démontré, son impact sur l'Homme ne l'est pas pour autant et plusieurs travaux de recherche ont montré que le Lean est une organisation qui génère du stress pour les employés. Cette réalité appelle l'ergonomie cognitive à produire les connaissances nécessaires permettant une meilleure interaction entre l'espace cognitif de l'employé et son environnement de travail. Dans cet article nous proposons tout d'abord un modèle sur la complexité de la relation « Lean, Entreprise, Employé & Ergonomie Cognitive ». À travers ce modèle, nous montrons que le Lean est une organisation qui améliore la performance de l'entreprise. Ensuite, nous mettons l'emphase sur les effets du Lean sur les employés avec un focus particulier sur le stress généré par le Lean et nous analysons dans le même sens la relation « Stress -Performance ». Enfin, nous abordons le rôle de l'ergonomie cognitive dans l'amélioration des performances de l'entreprise et l'amélioration de l'interaction du Lean avec l'espace cognitif de l'employé à travers les aides cognitives. Nous abordons aussi, dans cet article, les questionnaires permettant d'expérimenter notre modèle et nous présentons, en fin, les résultats de son expérimentation dans le cadre d'une entreprise marocaine dans le secteur thermomécanique. Les résultats collectés ont permis de confirmer les différentes hypothèses formulées dans le cadre de notre modélisation.

INTRODUCTION

Face à une concurrence internationale de plus en plus vive et à des impératifs de performance de plus en plus croissants, les entreprises doivent mettre en oeuvre les leviers de croissance essentiels et faire preuve d'agilité et d'adaptation pour gagner en compétitivité. Le Lean constitue, dans ce sens, une voie d'avenir pour améliorer la compétitivité de l'entreprise, réduire les surcoûts et augmenter sa rentabilité.

En plus de la mise en oeuvre d'un ensemble de techniques et d'outils, le Lean est un mode de management qui repose sur le facteur humain et suggère que le personnel travaille avec un état d'esprit orienté vers la réduction des pertes et du gaspillage. La motivation et les comportements des Hommes sont des conditions nécessaires pour une intégration réussie du Lean.

Si les effets positifs du Lean sur les performances de l'entreprise ont été démontrés, ses effets sur les employés ne le sont pas pour autant et plusieurs recherches ont montré que le Lean génère du stress. Dans ce sens, l'intervention de l'ergonomie cognitive pour sa conception et son implémentation semble importante pour mieux gérer ce stress en améliorant l'interaction des employés avec un environnement Lean.

Dans cet article, nous présentons un modèle sur la complexité de la relation « Lean, Entreprise, Employé & Ergonomie Cognitive ». Ce modèle permet d'illustrer, tout d'abord, l'impact du Lean sur l'amélioration des performances de l'entreprise. Il aborde, ensuite, les effets du Lean sur les employés tout en mettant l'emphase sur le stress généré par le Lean. Enfin, le modèle souligne le rôle de l'ergonomie cognitive pour améliorer les performances de l'entreprise et assurer une meilleure adéquation entre l'environnement Lean et l'espace cognitif de l'Homme à travers les aides cognitives.

Nous abordons aussi, dans cet article, les principes des questionnaires qui permettront d'expérimenter le modèle proposé et nous présentons, dans le même sens, les résultats de son expérimentation dans le cadre d'une entreprise marocaine dans le secteur thermomécanique. Les résultats collectés ont confirmé les différentes hypothèses formulées dans le cadre de notre modélisation.

II. PROPOSITION D'UN MODÈLE EXPLICATIF DE LA COMPLEXITÉ DE LA RELATION « LEAN, ENTREPRISE, EMPLOYÉ & ERGONOMIE COGNITIVE »

A. La construction du modèle A partir des recherches, analyses et synthèses bibliographiques sur les champs disciplinaires Lean, ergonomie et stress, nous avons mis en évidence les relations suivantes :

 La relation « Lean-Entreprise » : émergence de la performance :

Le Lean est une organisation intéressante faisant progresser la performance et les résultats de l'entreprise. Ceci est confirmé par les travaux de Alarcôn et al. [START_REF] Luis | Assessing the impacts of implementing lean construction‖[END_REF], par l'étude McKinsey [START_REF] Bouvard | Donner un nouvel élan à l'industrie en France[END_REF] et aussi les travaux de Rivera et al. [START_REF] Rivera | Measuring the impact of Lean tools on the cost-time investment of a product using cost-time profiles[END_REF], Abdulmalek et al. [START_REF] Abdulmalek | Analyzing the benefits of Lean manufacturing and value stream mapping via simulation: A process sector case study[END_REF] et De Treville et al. [START_REF] De Treville | Could Lean production job design be intrinsically motivating? Contextual, configurational, and levels-of-analysis issues[END_REF].

La définition de la performance par Ganster et Perrewé [START_REF] Ganster | New Developments in Theoretical and Conceptual Approaches to Job Stress[END_REF] comme « un ensemble de comportements engagés par des employés et qui sont pertinents pour atteindre les objectifs organisationnels », montre que d'une part la performance est réalisée par les employés avec leurs caractéristiques cognitives (capacités et limites) et d'autres part, la performance est mesurée en comparant le travail réel et le travail prescrit.

 La relation « Lean-Homme » : émergence du stress : Le Lean est une organisation qui a des impacts positifs sur les employés et sur leurs conditions de travail. Ceci a été confirmé par les travaux de Berggren (1992) (cité par [START_REF] Saurin | The impacts of Lean production on working conditions: A case study of a harvester assembly line in Brazil[END_REF]), les travaux de Ballé ([8] et [START_REF] Ballé | The Psychology of Lean Management[END_REF]) et les travaux de (Hopp et Spearman, 1996;Monden, 1983;Womack et al, 1990 ;Mac Duffie (1995a[START_REF] Moors | STRESS ET TRAVAIL: ORIGINES ET APPROCHES[END_REF])) cité par [START_REF] De Treville | Could Lean production job design be intrinsically motivating? Contextual, configurational, and levels-of-analysis issues[END_REF]. D'autres travaux ont montré que le Lean entraine aussi des impacts négatifs pour les employés. Cela a été appuyé par les travaux de De Treville et al. [START_REF] De Treville | Could Lean production job design be intrinsically motivating? Contextual, configurational, and levels-of-analysis issues[END_REF], Womack et al. [START_REF] Womack | The Machine That Changed The World[END_REF], Ballé [START_REF] Ballé | The Psychology of Lean Management[END_REF], Jackson et Mullarkey [START_REF] Jackson | Lean production teams and health in garment manufacture[END_REF], Ferreira [START_REF] Ferreira | Guidelines for Assessing the Impacts of Lean Production on Working Conditions[END_REF], Conti et al. [START_REF] Conti | The effects of Lean production on worker job stress[END_REF], P. Hasle [START_REF] Hasle | Lean Production-An Evaluation of the Possibilities for an Employee Supportive Lean Practice‖ National Research Centre for the Working Environment[END_REF] et B. Lyonnet [START_REF] Lyonnet | Un état des lieux réalisé pour l'Université d'été Lean qui a eu lieu du 1 au 3 septembre 2008[END_REF]. Ces impacts négatifs génèrent du stress qui, à son tour, influence la performance de l'entreprise selon les travaux de Légeron [START_REF] Légeron | Le stress au travail : de la performance à la souffrance[END_REF], Laurent M.C. [START_REF] Laurent | Stress et travail[END_REF], Jex cité par [START_REF] Ganster | New Developments in Theoretical and Conceptual Approaches to Job Stress[END_REF] et S. Moors [START_REF] Moors | STRESS ET TRAVAIL: ORIGINES ET APPROCHES[END_REF]. Il faudrait noter à ce niveau que le stress perçu par les employés est subjectif. Il s'opère en effet une comparaison entre ce qu'on demande de l'employé (exigences de l'entreprise) et ses capacités à répondre à ces exigences (caractéristiques de l'employé) ou ce qu'on met à sa disposition pour faire face aux exigences (latitude décisionnelle, récompenses…). Cela a été expliqué par les modèles de Karasek ((Karasek, 1979) cité par [START_REF] Laurent | Stress et travail[END_REF], [START_REF] Berghmans | Stress au travail : Des nouveaux outils pour les ressources humaines[END_REF] et [START_REF] Gollac | Les conditions de travail[END_REF]), Siegrist ((Siegrist et al., 1997) cité par [START_REF] Laurent | Stress et travail[END_REF]) et Mackay et Cooper ((Mackay et Cooper, 1987) cité par [START_REF] Berghmans | Stress au travail : Des nouveaux outils pour les ressources humaines[END_REF]) .

 La relation « Lean-Ergonomie » :

Selon Bourgeois et Gonon [START_REF] Bourgeois | Le Lean et l'activité humaine. Quel positionnement de l'ergonomie, convoquée par cette nouvelle doctrine de l'efficacité ?[END_REF], il existe entre l'ergonomie et le Lean des points d'entente (notamment avec l'ergonomie physique) et des points de divergence (avec l'ergonomie cognitive).

 La relation « Entreprise-Ergonomie » :

L'ergonomie est abordée aujourd'hui aussi du point de vue performance. En effet, les définitions récentes de cette discipline explicitent le rôle de l'ergonomie dans la promotion de la performance de l'entreprise. Ceci se confirme par les travaux de [START_REF] Zink | Performance Measurement from Marco ergonomics Perspective‖[END_REF], [START_REF] Strasser | Principles, methods and examples of Ergonomics Research and work design‖[END_REF], [START_REF] Vanwonterghem | Ergonomics and Human Factors: Methodological considerations about evidence based design of work systems‖[END_REF], F. DANIELLOU, l'Association Internationale d'Ergonomie (IEA) et la Société d'Ergonomie de Langue Française (SELF). Dans le même sens, les travaux de Koningsveld [START_REF] Koningsveld | The impact of ergonomics‖[END_REF], abordent, avec plus de détail, l'impact de l'ergonomie sur la performance de l'entreprise en termes d'augmentation de productivité, réduction des coûts opérationnels, amélioration de la compétitivité et des valeurs de la société.

 La relation «Homme-Ergonomie » :

L'ergonomie cognitive propose des aides cognitives (voir par exemple [START_REF] Tajri | Emergence du stress et de la performance[END_REF]) et des dispositifs afin d'améliorer l'interaction de l'espace cognitif de l'Homme avec l'environnement de travail. L'agrégation de ces relations donne le modèle global suivant (figure n°1 Dans un environnement Lean, les ‗déterminants entreprise' se caractérisent par une surcharge de travail, la complexité de la tâche, quantité excessive de travail, exigences mentales, contraintes de temps liées au travail, stresseurs...etc. autrement dit, le Lean influencent le travail prescrit [START_REF] Luis | Assessing the impacts of implementing lean construction‖[END_REF].

Les ‗déterminants opérateur' : ce qui caractérisent l'opérateur et influencent ou conditionnent son travail. Ils incluent les buts et les objectifs de l'opérateur, les idées préconçues (les attentes), les capacités cognitives, l'expérience et la formation.

Une évaluation cognitive des ‗déterminants opérateur' en relation avec les ‗déterminants entreprise' et les caractéristiques du Lean (reconnaissance, respect pour l'Homme..) fait ressortir des écarts positifs ou négatifs qui ont un effet sur l'opérateur. Ce processus d'évaluation permet d'évaluer s'il existe un déséquilibre et de déterminer ses conséquences. Selon le déséquilibre ressenti, l'effet sur l'opérateur peut être positif (motivation, performance) ou négatif (stress). L'effet sur l'employé (motivation ou stress) influence à son tour les ‗déterminants opérateur' (objectifs, capacités cognitives…) [START_REF] Bouvard | Donner un nouvel élan à l'industrie en France[END_REF].

Dans un environnement Lean, les ‗déterminants opérateur' incluent en plus le niveau de compétence (individuelle et collective), la polyvalence, l'engagement sur l'amélioration continue, l'esprit de défi, etc. L'opérateur face à un travail prescrit (déterminants entreprise) déclenche le processus de réalisation du travail réel selon ses propres déterminants (3).

Le travail réel : correspond à la manière dont l'opérateur pense qu'il agit et la manière dont il agit réellement. C'est son activité. La tâche réelle est donc la représentation que l'utilisateur se fait de son travail.

La comparaison du travail réel avec le travail prescrit fait apparaître un écart qui peut avoir un effet positif (performance, augmentation du chiffre d'affaire, amélioration de l'image de marque, ambiance de travail stimulante, qualité des résultats, augmentation de clientèle,

(2)

(6) (7) (5) (3) (4)
etc.) ou négatif (malfaçons, diminution de la production et non-respect du délai de livraison, diminution du rendement au travail, diminution de la qualité, anticipation par la surcapacité et les stocks tampons, désorganisation, perte de clientèle, mauvaise image de marque, etc.) (4).

Si le Lean présente plusieurs points d'entente avec l'ergonomie physique, il présente d'autres points de divergence avec l'ergonomie cognitive [START_REF] De Treville | Could Lean production job design be intrinsically motivating? Contextual, configurational, and levels-of-analysis issues[END_REF]. Les points de divergence entre le Lean et l'ergonomie présentent des limites pour le Lean et qui sont soulevées par l'ergonomie cognitive à travers la proposition des aides cognitives qui influencent à la fois le travail prescrit dans un environnement Lean et les ‗déterminants opérateur' (( 6) & ( 7)). L'objectif étant d'améliorer l'interaction entre l'espace cognitif de l'employé et l'environnement de travail et avoir de bonnes performances en conséquence.  Les principales difficultés que l'entreprise a affrontées pour adopter la démarche Lean ;

B. L'expérimentation du modèle : la construction des questionnaires

 L'impact du stress sur la performance (perçu par l'encadrement) ;

 Le rôle de l'ergonomie cognitive pour améliorer l'applicabilité du Lean dans l'entreprise (inspiré des normes de la série ISO 10075 ( [START_REF]principes ergonomiques concernant la charge de travail mental-termes généraux et leurs définitions[END_REF], [START_REF]principes ergonomiques relatifs à la charge de travail mentalpartie 2 : principes de conception[END_REF] et [START_REF]mental-partie 3 : principes et exigences concernant les méthodes de mesurage et d'évaluation de la charge de travail mental[END_REF])).

III. CAS DE L'ENTREPRISE EXTRUS

L'entreprise EXTRUS est un leadeur national en procédés thermomécaniques (Extrusion). Elle opère dans quatre secteurs d'activité à savoir l'hydraulique, l'agriculture, le bâtiment et l'emballage. L'entreprise a un métier très intégré qui couvre la conception, la fabrication et/ou la commercialisation des produits différents tels que les tuyaux en PVC et en PE, le film en PE et les profilés en PVC.

L'entreprise arrive à offrir à ses clients une large gamme de produits. La clientèle de l'entreprise est très diversifiée et principalement placée sur le plan national alors que ses principaux fournisseurs sont placés à l'étranger (les Etats-Unis et l'Europe).

A. Présentation du contexte du projet Lean chez l'entreprise EXTRUS L'entreprise s'est engagée depuis plus de deux ans dans le déploiement des concepts du Lean Manufacturing afin de réduire son coût de revient et pérenniser son avantage compétitive. En effet, cette entreprise enregistre des pertes significatives dans ses unités de production qui l'ont obligées alors à mettre au point une stratégie ambitieuse de réduction des coûts. Dans l'optique de déployer les concepts Lean et améliorer la satisfaction de ses clients, l'entreprise a choisi ‗l'unité de production x' comme zone pilote. En effet, cette unité fabrique plusieurs versions pour le même client et se trouve obliger de constituer un stock pour chaque référence afin de satisfaire la demande diversifiée du client. Cette unité devait ainsi augmenter sa productivité et sa réactivité tout en en réduisant ses coûts de fabrication. Pour ce faire, l'entreprise a dû principalement améliorer la disponibilité des équipements de production de cette unité, réduire et maîtriser son temps de changement de série et donc améliorer le taux de rendement synthétique (TRS) et enfin améliorer le taux de respect du planning (taux de service).

B. Evaluation du degré de maturité du Lean

La validation de nos hypothèses nécessite avant tout de mesurer le degré de maturité du Lean au niveau de l'entreprise. La mesure a été faite selon les dix dimensions du Lean [START_REF] Shah | Defining and developing measures of Lean production[END_REF]. Pour chaque dimension, une échelle de notation est prévue de 0 à 3. Le score obtenu, par dimension, présente le degré d'application du Lean selon cette dimension vu par le responsable Lean de l'entreprise.

C. Impact du projet Lean sur les performances de l'entreprise

Pour assurer l'atteinte de ses objectifs stratégiques, l'entreprise devait avoir plus de flexibilité au niveau de son organisation et surtout de son système de production, afin de faire face aux nouveaux enjeux imposés par le nouveau contexte de l'entreprise. Ceci a constitué l'objet du projet Lean dans le cadre duquel on a mis en place un ensemble de chantiers (chantier SMED, chantier de gestion de la performance, chantier pièces de rechange, chantier standard, chantier 5S et chantier planification) pour garantir l'optimisation de la productivité et l'amélioration de la réactivité.

Dans la phase de pérennisation du projet, nous avons constaté un impact positif du projet sur le taux du rendement synthétique (TRS), qui a enregistré une augmentation importante d'une valeur moyenne de 33% (moyenne sur 5 mois durant les phases de diagnostic et de préparation qui ont précédé la mise en oeuvre du projet) à une valeur moyenne de 67% (moyenne de 12 mois dans la phase de pérennisation).

Cet impact positif est réalisé grâce aux bons résultats assurés par l'ensemble des chantiers mis en place à tous les niveaux et à toutes les intersections mutuelles.

Dans la phase de diagnostic, nous avons aussi constaté que les changements de série constituaient presque 35 % des NTRS 4 . Ce pourcentage est passé à 19 % dans la phase mise en oeuvre du projet. L'augmentation du TRS a engendré, à son tour, l'amélioration du taux de respect de planning et qui est passé en moyenne de 37% à 79%.

En fin, cette augmentation du TRS et du taux de respect planning offre une amélioration de la productivité et de la réactivité qui se traduit par un niveau optimal du niveau du stock par rapport au stock mini (qui est passé de 31% à 39%). Ce niveau offre un avantage commercial particulier à l'entreprise dans son secteur d'activité. En effet, la disponibilité de toute la gamme par rapport à une référence produit fini est une exigence du client. Cette exigence présente un facteur de différentiation entre les concurrents dans ce secteur.

Pour les clients de l'entreprise, la disponibilité de toute la gamme (c.-à-d. du plus petit jusqu'au plus grand diamètre) permet de mettre dans l'opération de transport et de stockage, les références les plus petites dans les plus grandes qui les suivent et ainsi de suite (le principe du télescopage) pour garantir une optimisation au niveau des coûts logistiques.

Enfin, l'ensemble des ces résultats confirme notre hypothèse selon laquelle le Lean améliore la performance de l'entreprise.

D. Impact du projet Lean sur les employés

Afin de mesurer l'impact du projet Lean sur les employés de l'entreprise nous avons mené notre étude sur la ligne de production d'une référence de produit commandée sous plusieurs versions. Une fréquence élevée de changement de série constitue pour les employés et l'encadrement de l'entreprise une « situation de travail problématique » qui génère du stress. En effet, afin d'optimiser les coûts logistiques, l'entreprise doit garantir la disponibilité des différents diamètres (toute la gamme) dans le stock pour exploiter le maximum les voyages et optimiser le taux d'occupation des moyens de transport en mettant les petits diamètres à l'intérieure des grands (télescopage). Cette condition génère beaucoup de changements qui atteignent parfois deux changements par semaine, alors que la durée de changement avant la mise en oeuvre du Lean était en moyenne de 70 h. Après l'application du projet Lean, le temps de changement a été réduit à une moyenne de 30 h. Les opérateurs se sont retrouvés alors en situation de stress avec des standards à respecter et l'obligation d'utiliser des outils « poka yoke » pour effectuer le changement dans les durées nominales. Ce stress entraine un manque de concentration des opérateurs et par conséquent des défauts de réglage et d'alignements par la suite. Cela impacte directement la qualité du produit fini.

Le stress perçu par les opérateurs sur cette ligne de produit avait un effet négatif sur la performance de l'entreprise. Cet effet a porté essentiellement sur l'augmentation du taux de non-conformité et de la durée d'exécution de la tâche.

La mesure de l'impact du Lean sur les employés est réalisée pour les conditions de travail 1 ère catégorie selon les dimensions : la communication & Formation, le rôle & l'organisation du travail, l'autonomie, la reconnaissance & respect pour l'homme, le soutien des chefs et collègues, la sécurité d'emploi, les perspectives d'évolution, d'apprentissage & préoccupations liées à la carrière et la prévisibilité du travail. La mesure de l'impact du Lean sur les employés a concerné aussi les conditions de travail 2 ème catégorie selon les dimensions : la cadence du travail, la demande illimitée sur la compétence et la polyvalence, la surcharge qualitative (complexité), le travail collectif, la surcharge quantitative et évaluation cognitive de la situation.

Pour chaque dimension, des questions préétablies sont posées au responsable Lean et à trois opérateurs avec une échelle de notation de 1 à 3. Le score obtenu mesure l'impact du Lean sur les conditions de travail ressenti par les employés de l'unité x. L'objectif de cette étape est de vérifier nos hypothèses selon lesquelles :

 Une intégration forte du Lean renforcera les conditions de travail 1 ère et 2 ème catégorie ;

 Les conditions de travail 1 ère catégorie permettent d'éviter les stresseurs 1 ère catégorie présentant ainsi un point de rencontre avec l'ergonomie alors que les conditions de travail 2 ème catégorie sont responsables d'apparition de stresseurs 2 ème catégorie constituant ainsi un point de divergence avec l'ergonomie ;

 L'intervention de l'ergonomie cognitive permettrait une meilleure adéquation des conditions de travail 2 ème catégorie avec les capacités cognitives des employés.

1) Evaluation de l'impact positif : les stresseurs 1ère catégorie Le score mesurant l'impact positif du Lean sur les employés donne des informations différentes selon le point de vue opérateur qui a enregistré un score de 18 dépassant le score moyen et le point de vue entreprise qui a enregistré un score de 13 inférieur au score moyen. En effet, pour le point de vue opérateur, un score dépassant le score moyen montre que les opérateurs sont généralement satisfaits de leurs conditions de travail (1 ère catégorie) ou au moins ces conditions de travail ne présentent pas pour eux une source de stress. Sur l'ensemble des dimensions mesurées, les opérateurs ont exprimé un niveau de satisfaction moyen à fort.

Notons à ce niveau que les certifications de l'entreprise selon les nomes QSE avec un grand degré de maturité des trois systèmes ont permis à l'entreprise d'avancer, avant même le projet Lean, sur plusieurs points par exemple la formation et la communication.

Pour le point de vue entreprise, l'impact du Lean sur les conditions de travail est généralement moyen parfois faible. La dimension la plus pénalisante est relative à la reconnaissance du travail des employés et le respect pour Homme.

En générale, nous retenons que la mise en place des pratiques Lean (à travers les certifications et le projet INMAA (l'INitiative Marocaine d'Amélioration)) au niveau de l'entreprise a amélioré les conditions de travail relatives à la communication & formation, le rôle & l'organisation du travail, l'autonomie, la reconnaissance, le soutien des chefs, la sécurité d'emploi, les perspectives d'évolution et la prévisibilité du travail. Ceci confirme notre hypothèse selon laquelle il y a une corrélation entre l'application du Lean et la diminution des « stresseurs 1 ère catégorie ».

2) Evaluation de l'impact négatif : les stresseurs 2ème catégorie La perception des impacts négatifs du Lean sur les conditions de travail est aussi variable entre le point de vue opérateur et le point de vue entreprise. En effet, le point de vue opérateur a affiché un score de 14 dépassant le score moyen et le ‗point de vue entreprise' a enregistré un score de 11 légèrement inférieur au score moyen.

Pour le ‗point de vue opérateur', un score dépassant le score moyen montre que les opérateurs ressentent généralement du stress généré par ‗les conditions de travail 2 ème catégorie'. Sur l'ensemble des dimensions mesurées, les opérateurs ont exprimé un niveau de stress moyen à fort.

Le ‗point de vue entreprise' rejoint le point de vue opérateur sur les dimensions liées à la cadence de travail, la surcharge qualitative et le travail collectif. Pour les autres dimensions de stress, l'encadrement de l'entreprise les considère comme étant faibles à moyennes.

En générale, nous retenons que la mise en place des pratiques Lean (à travers les certifications et le projet INMAA) au niveau de l'entreprise a généré du stress relatif à la cadence de travail, la demande illimitée sur la compétence et la polyvalence, la surcharge qualitative (complexité), le travail collectif, la surcharge quantitative et évaluation cognitive de la situation. Ceci confirme notre hypothèse selon laquelle il y a une corrélation entre l'application du Lean et l'apparition des « stresseurs 2 ème catégorie ».

E. Rôle de l'ergonomie cognitive dans l'amélioration de l'applicabilité du Lean au sein de l'entreprise

Selon le responsable Lean de l'entreprise, une amélioration de la conception de l'environnement Lean est possible. Cette amélioration porte essentiellement sur la réduction de la charge de travail mental par la réduction de la complexité des exigences de la tâche et aussi par une meilleure gestion des conditions de travail par: Les résultats collectés auprès de l'entreprise ‗EXTRUS' confirment la corrélation entre l'application du Lean et l'amélioration des conditions de travail 1 ère catégorie et aussi une corrélation entre la mise en oeuvre du Lean et l'apparition de stresseurs 2 ème catégorie. Etendre le champ d'étude à d'autres entreprises, avec des niveaux différents de maturité du Lean, est nécessaire pour valider nos hypothèses.

BIBLIOGRAPHIE



  Une meilleure gestion du flux d'information,  Une meilleure gestion des responsabilités,  Une meilleure gestion de la polyvalence et l'autocontrôle, Une meilleure gestion des contraintes temporelles liées au travail,  l'intégration de la flexibilité et de l'autonomie. L'introduction de ces mécanismes nécessite de prendre en considération l'ergonomie cognitive lors de la conception ou la modification des situations de travail Lean. IV. CONCLUSION Le Lean se présente aujourd'hui comme un mode d'organisation qui se concentre sur la valeur ajouté des processus de l'entreprise et en supprime ou limite les gaspillages. Si la mise en place d'un ensemble de techniques et d'outils est essentielle au succès d'un projet Lean, la dimension humaine d'une telle transformation doit impérativement être considérée. La prise en compte des facteurs humains (surtout cognitifs) font en effet la différence entre les entreprises utilisant avec succès le Lean, et les autres. Si l'impact positif du Lean sur la performance de l'entreprise semble évident, ses effets sur les conditions de travail ne le sont pas toujours et les recherches montrent que le Lean est un mode de management générateur de plusieurs stresseurs. Ces stresseurs, constituant un point de divergence entre le Lean et l'ergonomie cognitive, interpellent l'ergonomie à produire des connaissances pour mieux gérer le stress perçu par les employés, d'améliorer leur performance et augmenter leur engagement pour le bon fonctionnement du Lean. La modélisation de la complexité de la relation « Lean, Entreprise, Employé & Ergonomie Cognitive » à été proposée à l'issue de différentes analyses bibliographiques et le questionnaire permettant son expérimentation a été aussi construit sur la base de questionnaires validés scientifiquement.

TABLE I .

 I EVALUATION DU DEGRE DE MATURITE DU LEAN , mode de management). Malgré que l'intérêt porté aux conditions de rentabilité fait défaut dans ces normes, nous pouvons dire que les entreprises lancées dans les démarches managériales sont initiées au Lean au moins implicitement. Il n'est pas donc surprenant de découvrir que l'entreprise est forte sur ces points malgré qu'elle vienne d'adopter le Lean (explicitement) il y a deux années.

	(communication, formationCependant, le raisonnement global sur la maturité du
	Lean au sein de l'entreprise cache des situations différentes
	selon les dimensions d'application du Lean. En effet,
	l'entreprise a obtenu un score élevé sur les dimensions liées
	au fournisseur, au client et à l'implication des employés.
	Ces concepts sont, en effet, exigés par les normes QSE.
	L'entreprise a obtenu également un score très important
	relativement à la réduction du temps de changement de série
	et cela grâce au chantier SMED réalisé dans le cadre du
	projet Lean. Cependant, un effort à fournir au niveau des
	dimensions pénalisantes à savoir : la mise en oeuvre des
	principes de flux tiré par les cartes Kanban et le
	renforcement des principes MSP afin de prévoir les risques
	de non-conformités.
				Score	Score	Score	Score
				faible	moyen	maxi	obtenu
	D1	feedback au fournisseur:		1	2	3	2
	D2	Livraison juste à temps par les fournisseurs		1	2	3	3
	D3	Le développement du fournisseur		1	2	3	3
	D4	Implication des clients		1	2	3	3
	D5	Flux Tiré (faciliter la production JAT en intégrant les cartes kanban)	1	2	3	0
	D6	Flux continu: établir des mécanismes qui permettent et facilitent un flux	1	2	3	3
		continu de produits.
	D7	Réduction du temps: réduire les temps de changement de séries.	1	2	3	3
	D8	TPM (total productive/preventive maintenance)		1	2	3	2
	D9	MSP (maîtrise statistique des processus)		1	2	3	1
	D10 Implication des employés		1	2	3	3
		Score		10	20	30	23
	Le score obtenu montre que le degré d'application du	Environnement. Cette démarche intégrée (QSE) a permis à
	Lean au niveau de l'entreprise est généralement intéressant.	l'entreprise de couvrir plusieurs dimensions du Lean avant
	En effet, le score obtenu dépasse le score moyen et ce	même de s'engager formellement dans ce projet.
	malgré le fait que le projet Lean est relativement récent au
	niveau de l'entreprise. Ceci s'explique par le fait que	Notons à ce niveau que l'organisation Lean se recoupe
	l'entreprise a établi, mis en oeuvre, entretenu et amélioré	avec les démarches managériales sur plusieurs aspects liés à
	un système de management intégré Qualité, Sécurité et	l'amélioration continue, aux clients, aux fournisseurs
			(relation mutuellement bénéfiques) et aux employés

Xème Conférence Internationale : Conception et Production Intégrées, CPI 2015, 2-4 Décembre 2015, Tanger -Maroc. Xth International Conference on Integrated Design and Production, CPI 2015, December 2-4, 2015, Tangier -Morocco. Identify applicable sponsor/s here. If no sponsors, delete this text box (sponsors).

Le questionnaire suivant est issu de : PISTES © Vol. 7 No.

Mai 2005 --Recherche http ://www.pistes.uqam.ca/v7n2/articles/v7n2a1.htm 2 Service de Psychologie du Travail de l'Université de Liège http://www.woccq.be

Sandra G. Hart, "NASA-TASK LOAD INDEX (NASA-TLX); 20