Pierre-André Rey
email: pierre-andre.rey@ensam.eu

Johanna Senatore

Yann Landon

Effect of cutting tool geometry on hole quality in orbital drilling

Keywords: Orbital drilling, 3D chip geometry simulation, Tool geometry optimization

The orbital drilling process is a very complex machining operation. Due to the helical path of the tool in the material and the tool geometry that can be very complex, the geometry of the chip is very variable along the cutting edge and during a revolution of the tool. This complexity leads to variable cutting forces during drilling and makes it difficult to model and estimate for different tool geometries. The aim of this study is, therefore, to use a modelling of the orbital drilling process in order to study the influence of the geometry of the tool and the cutting conditions on the cutting forces. The final objective is to identify their impact on the quality of the drilled hole and, thus, to control the final quality. First, the chip geometry is modelled from the cutting parameters and the macro-geometry of the tool. It needs to determine the tooth trajectory into the material for each point of the cutting edge. Cutting force models, based on the instantaneous chip thickness, are then implemented. An experimental study validates the modelling by cutting force measurements carried out during orbital drilling tests. From this modelling, it is now possible to study the influence of the geometry of the cutting tool on the forces in order to control the loading on the tool and therefore the final quality of the drilled hole.

Introduction

For mechanical assembly of structures, the drilling of the fastening holes is an important matter. Especially in the aeronautical industry, the drilling process must reach not only high-level requirements about hole quality and material integrity control but also economical target in terms of productivity [START_REF] Tönshoff | Machining of holes: developments in drilling technology[END_REF][START_REF] Lindqvist | Orbital drilling of sandwich constructions for space applications[END_REF]. It explains the constant interest brought to the drilling process by academic researchers as through industrial solution developments. According to the increasing diversity of materials used in aircraft structures and their different characteristics, the process had to evolve to allow the drilling of holes in multi-material stacks made of highly different materials as composite laminates, titanium alloys, aluminum alloys etc. [START_REF] Brinksmeier | Drilling of composites and resulting surface integrity[END_REF][START_REF] Denkena | Helical milling of CFRPtitanium layer compounds[END_REF]. A number of research studies have been brought on the characterization of new solutions in terms of cutting tools technologies and of processes as vibration-assisted drilling [START_REF] Ladonne | Modelling the vibration assisted drilling process: identification of influential phenomena[END_REF][START_REF] Dref | Modelling kinematics and cutting forces in vibration assisted drilling[END_REF] or, more recently, the development of smart drilling [START_REF] Araujo | Smart drilling for Aerospace Industry: state of art in research and education[END_REF]. Among these solutions, the orbital drilling process can be cited. Orbital drilling, also called helical milling, involves making a hole with a tool of a diameter smaller than the diameter of the hole, driven on a helical path (Fig. 1).

Developed at the beginning for wood drilling, it has been brought to industrial application for metal drilling and then composite drilling for different reasons. First, it allows the drilling of different diameter holes with the same tool [START_REF] Denkena | Helical milling of CFRPtitanium layer compounds[END_REF]. Also, the interrupted cutting process occurring in orbital drilling leads to a good chip fragmentation, and thus to an easier and improved chip evacuation through the radial gap between the tool and the borehole surface [START_REF] Iyer | Helical milling: an enabling technology for hard machining precision holes in AISI D2 tool steel[END_REF]. It can be added that this process generates low cutting forces that lead to a reduction of classical drilling defects as burrs in metallic materials or delamination in composite laminates [START_REF] Denkena | Helical milling of CFRPtitanium layer compounds[END_REF][START_REF] Brinksmeier | Burr and cap formation by orbital drilling of aluminum[END_REF][START_REF] Sadek | Characterization and optimization of orbital drilling of woven carbon fiber reinforced expoxy laminates[END_REF]. Moreover, this process is also justified for drilling titanium alloys [START_REF] Ezugwu | Titanium alloys and their machinability a review[END_REF]. But its use has been also reduced because of the drilling duration and the more complex control of the trajectory (with tool deflection issue) [START_REF] Iyer | Helical milling: an enabling technology for hard machining precision holes in AISI D2 tool steel[END_REF]. Nevertheless, from a global economic point of view, the orbital drilling process may permit to reduce the number of operations to reach the finished hole. In that case, it appears to be more productive to obtain the final hole in one operation of orbital drilling rather than in 2 or 3 operations of axial drilling and reaming. And considering the matter of trajectory programming, industrial solutions have been developed for this purpose, proposing orbital drilling units with an eccentric spindle [START_REF] Company | Orbital drilling inventor[END_REF]. The impact on the microstructure and the fatigue behavior of drilled aerostructures was also studied, and Sun et al. [START_REF] Sun | Hole-making processes and their impacts on the microstructure and fatigue response of aircraft alloys[END_REF] showed that orbital drilling has to be well optimized to reach the same level of fatigue behavior the one obtains with axial drilling. After that, the control of the hole quality has to be done through an optimization of the process: cutting conditions and tool geometry [START_REF] Pereira | A review of helical milling process[END_REF]. Ozturk et al. [START_REF] Ozturk | Mechanics and dynamics of orbital drilling operations[END_REF] proposed a unified mathematical model which predicts three-dimensional chatter stability as a function of orbital pitch length, spindle speed, and orbital speed of the tool and permits the optimization of the cutting conditions. Considering tool geometry, the influence of both axial and tangential feeds on the hole diameter has been investigated for simple flat-end tools [START_REF] Denkena | Helical milling of CFRPtitanium layer compounds[END_REF][START_REF] Brinksmeier | Orbital drilling kinematics[END_REF] based on a simple modelling of the chip geometry in orbital drilling dedicated to these simple-shape tools. For more complex tool geometries, another model of chip geometry and cutting forces was developed and proposed by Rey et al. [START_REF] Rey | Modelling of cutting forces in orbital drilling of titanium alloy Ti-6Al-4V[END_REF] or, more recently, by Zhou et al. [START_REF] Zhou | Modeling of non-linear cutting forces for dry orbital drilling process based on undeformed chip geometry[END_REF]. The tool geometry was introduced into the modelling by considering local cutting conditions (cutting speed gradient along the cutting edges) and forces. The tool geometry was firstly expressed analytically before being divided into elementary edge portions. On each portion, a local cutting force model was applied. The elementary forces were then summed according to the evolution of the edge geometry. Finally, this model makes it possible to simulate the geometry of the instantaneous chip. The cutting forces simulation is then possible as a function of the geometry of the tool considered and permits to highlight the important role of the shape of the axial part of the tool. This allows a better understanding of cutting phenomena occurring in orbital drilling (e.g., tool deflection). This model can be used to optimize the cutting conditions and the tool geometry in relation to the cutting forces generated. Based on this type of modelling, Zhou et al. [START_REF] Zhou | Analysis of the chip-splitting performance of a dedicated cutting tool in dry orbital drilling process[END_REF] proposed an analysis of the chip-splitting performance of a dedicated cutting tool in dry orbital drilling. In this paper, this model is used to simulate the impact of different tool geometries in order to give a better understanding of the role of each part of the tool. A tool optimization is then possible. The results are discussed and compared to experimental tests for validation.

Kinematic modelling of orbital drilling

The modelling of the cutting forces by a semi-analytical approach used in this paper is based on the work of Rey et al. [START_REF] Rey | Modelling of cutting forces in orbital drilling of titanium alloy Ti-6Al-4V[END_REF] and is summarized in this paper. It requires precise The calculation of the instantaneous chip section is then developed. In the present study, the drilling operation was simulated by down-milling but the model developed could also be used for up-milling.

Geometrical definition of orbital drilling

Definition and modelling of the tool geometry

The tool considered in this study is the one of Rey et al. [START_REF] Rey | Modelling of cutting forces in orbital drilling of titanium alloy Ti-6Al-4V[END_REF]. Its macro-geometry (Fig. 2) is defined using the following parameters (Table 1): Each cutting edge is discretized into two parts: the radial part noted "chf," and the axial part noted "tip," which includes the central part when the tool is without center-cut.

A function H(r) is established in order to define analytically the edge profile. It allows to give the altitude along the tool axis of each point of the edge versus the radius r. The lowest tool point is defined at the altitude Z = 0. The function H(r) is decomposed into several functions to take into consideration the different parts of the edge:

• Central part if 0 < r < R cc :
with δ = κ r in the case of the edge with center-cut (red dotted line on Fig. 2)

• Axial part (tip): if R cc < r < R t -R b : • Radial part (chf) if R t -R b < r < R t : H(r) = R cc -R t -R b × tan 2 -r + R cc -r × tan - 2 H(r) = r -R t -R b .tan 2 -r
The surface (A i ) of revolution of the tool can now be modelled [START_REF] Rey | Modelling of cutting forces in orbital drilling of titanium alloy Ti-6Al-4V[END_REF] (Fig. 5).

Definition of cutting parameters

The helical trajectory of the tool in orbital drilling can be decomposed into an axial feed f a and a tangential feed f t (Fig. 3). The cutting parameters are thus presented in Table 2. Tangential feed speed (mm/min)

H(r) = R b - √ R b 2 -r -R t -R b 2
V ft = f t × N Orbital revolution speed N orb = V ft 1 3
2.2 Calculation of the instantaneous chip section

References definition

• Machine reference: fixed

• Orbital reference: permits to define the tool position "i" on the helical trajectory. This reference is described by the tool angular position θ i in relation to the machine reference

R m = HL i , X, Y, Z R oi = HL i , X oi , Y oi , Z
• Tool reference: described by the angular position φ i of the considered point of the tool in the tool revolution

Modelling of chip volume and calculation of the instantaneous chip section

In order to determine the chip section, the machined surface at instant (t) is firstly defined. This machined surface (S i ) is modelled by scanning the envelope surface of revolution of the tool (A) on the helical trajectory (Fig. 4).

The position of the tool in the next moment (t + ∆t) is defined by a step along the trajectory calculated from the tooth feed. The surface obtained is the surface of revolution (A i + 1 ) of the tool on this new position.

The chip geometry between (t) and (t + ∆t) is calculated by making the difference between these two surfaces (Fig. 5).

The instantaneous chip section is given by cross-section of the chip modelled (Fig. 6).

The chip section obtained is discretized along the cutting edge into elementary chips of thickness h i (normal to the edge profile) and of width b i .

The calculation of the volume of material removed and the geometry of the instantaneous chip are more detailed in Rey et al. [START_REF] Rey | Modelling of cutting forces in orbital drilling of titanium alloy Ti-6Al-4V[END_REF].

R t = CL i , X ci , Y ci , Z

Cutting forces modelling

In this work, a semi-analytical cutting model is chosen as it permits to reduce the number of coefficients and calibration tests [START_REF] Merchant | Mechanics of the metal cutting process[END_REF][START_REF] Altintas | Generalized dynamic model of metal cutting operations[END_REF]. The model of Rey et al. [START_REF] Rey | Modelling of cutting forces in orbital drilling of titanium alloy Ti-6Al-4V[END_REF] is used in this work. Knowing the instantaneous chip section, the cutting forces applied on the tool during drilling can be expressed. For this, the local forces "dF" on each discrete chip element are modelled as follows:

where K * is a specific cutting coefficient. As the chip thickness (h i ) is highly variable along the cutting edge and during the tool revolution, the specific cutting coefficient is considered non-constant [START_REF] Denkena | Consideration of the form of the undeformed section of cut in the calculation of machining forces[END_REF][START_REF] Lorong | Dynamic study of thin wall part turning[END_REF] and estimated by the following function [START_REF] Kienzle | Spezifische schnittkräfte bei der metallbearbeitung[END_REF]:

with K and q as two constants Thus three elementary forces applied on the tool are defined (Fig. 7):

(1)

dF = K * × h × b (2) K * = K × h -q (3) ⎧ ⎪ ⎨ ⎪ ⎩ dF c = K c × h i 1-q c × b i dF n = K n × h i 1-q n × b i dF t = K t × h i 1-q t × b i
where h i and b i are dependent on the angular position φ i and on the radius r Ai .

Considering that the chip geometry and the cutting speed are highly different in the axial part (noted "tip") and the radial part (noted "chf") of the tool (Fig. 2), the coefficients of the cutting force model are considered different (and identified separately with specific calibration tests). The modelling of the cutting forces becomes the following:

• if 0 ≤ r ≤ R t -R b : • if R t -R b < r ≤ R t :

Modelling the cutting forces applied on the axial part of the tool

On the axial part, the cutting forces

F c _ tip z (φ i ), F n _ tip z (φ i ),
and F t _ tip z (φ i ) are calculated for each tooth z of the tool, taking into account the differences in the tooth profile which can exist (e.g., with or without center-cut) (Fig. 8 -Rey et al. [START_REF] Rey | Modelling of cutting forces in orbital drilling of titanium alloy Ti-6Al-4V[END_REF]). For this, 6 axial cutting parameters have to be identified: K c _ tip , K n _ tip , K t _ tip and q c _ tip , q n _ tip , q t _ tip . These parameters are considered to be the same for each tooth.

These force components only consider cutting mechanisms. But specific mechanisms may occur under the center of the tool as degraded cutting mechanisms or extrusion. To consider this phenomenon into the modelling, an axial indentation force is added to the model. The selected modelling is the one proposed by Williams for axial drilling [START_REF] Williams | A Study of the Drilling Process[END_REF]: where S is the projected surface of the indentation zone under the center of the tool. For a given tool, S can be considered a constant since the axial feed speed remains low [START_REF] Dref | Modelling kinematics and cutting forces in vibration assisted drilling[END_REF][START_REF] Williams | A Study of the Drilling Process[END_REF]. This is especially the case in orbital drilling. Thus, this parameter can be included into the coefficient K ind .

(4) ⎧ ⎪ ⎨ ⎪ ⎩ dF c = dF c_tip � r Ai , i � = K c_tip × h i � r Ai , i � 1-q c_tip × b i � r Ai , i � dF n = dF n_tip � r Ai , i � = K n_tip × h i � r Ai , i � 1-q n_tip × b i � r Ai , i � dF t = dF t_tip � r Ai , i � = K t_tip × h i � r Ai , i � 1-q t_tip × b i � r Ai , i � (5)
⎧ ⎪ ⎨ ⎪ ⎩ dF c = dF c_chf � r Ai , i � = K c_chf × h i � r Ai , i � 1-q c_chf × b i � r Ai , i � dF n = dF n_chf � r Ai , i � = K n_chf × h i � r Ai , i � 1-q n_chf × b i � r Ai , i � dF t = dF t_chf � r Ai , i � = K t_chf × h i � r Ai , i � 1-q t_chf × b i � r Ai , i �

Modelling the cutting forces applied on the radial part of the tool

On the radial part of the tool, the cutting forces

F c _ chf z (φ i ), F n _ chf z (φ i )
, and F t _ chf z (φ i ) are calculated for each tooth z of the tool (Fig. 8).

As for the axial part, 6 radial cutting parameters have to be identified.

The proposed modelling expresses the local cutting forces for each tooth of the tool. The axial part and the radial part of the tool have been dissociated in order to consider different cutting phenomena that occur under the tool tip and on the chamfer. The parameters of the modelling have to be identified separately for both parts in order to be able to simulate the global forces generated in orbital drilling.

Experimental setup

In this paper, the case of a three-flute tool (Z = 3) of diameter D t = 9 mm, with a corner radius R b = 1 mm, and with a single tooth with center-cut (R cc = 2.2 mm) is presented. ( 6)

F ind = K ind × V fa q ind × S
The machined material was titanium alloy TiAl6V4 of 19-mm thick. The identification of the model parameters "K" and "q" was performed through calibration tests on a DMG DMU50eVo CNC machining center, equipped with a 24,000 rpm/25 kW spindle. The methodology for the identification of these coefficients is presented in Rey et al. [START_REF] Rey | Modelling of cutting forces in orbital drilling of titanium alloy Ti-6Al-4V[END_REF]. The identified coefficients are summarized in Table 3.

It is difficult to compare these coefficients directly as different cutting mechanisms are involved and different chip thicknesses are encountered. In order to compare the results of the calibration phase, the equivalent coefficients K c * and K n * are calculated for a given value of h from (Eq. 2). It appears that the coefficients for the radial part of the tool are nearly twice as important as for the axial part. This can be explained by the differences in terms of cutting geometry. On the radial part and on the axial part of the tool, the rake and clearance angles are different. Moreover, the edge sharpness may also be different. But also, the cutting speed may explain a part of this difference. It can also be noted that coefficients K c * and K n * are nearly equal for a given part of the tool. This shows the importance of normal forces in orbital drilling, while cutting forces (normal to the chip section) are often considered preponderant. The influence of edge sharpness radius must also be considered and studied. Many studies have shown that this parameter could have a strong influence on cutting forces and more particularly on titanium alloys which are considered to have low machinability [26,[START_REF] Wyen | Influence of cutting edge radius on cutting forces in machining titanium[END_REF]. It is recommended to have a low edge radius for this material to minimize the cutting forces. In this study, the edge radius was estimated for the axial part between r ɛ = 10 to 14 μm and for the radial part between r ɛ = 18 to 25 μm. These values could therefore partly explain the greater values of the coefficients of the radial part.

The validation of the model was done by comparing simulated forces and measured forces in orbital drilling of holes of diameter D h = 11.1 mm. For all tests (calibration and validation), cutting forces were recorded using the 6-component Kistler 9257B dynamometer and sampled at 10 kHz. The cutting conditions were variable for the experimental plan. Only the cutting speed V c was kept at a constant value of 30 m/min (prescribed by the tool manufacturer to ensure the maximum tool life) as its influence on the cutting forces is negligible for small variations [START_REF] Denkena | Helical milling of CFRPtitanium layer compounds[END_REF].

Result of the cutting forces modelling

First, from the identified coefficients, it is possible to simulate the forces applied on the axial part of the tool, considering the same parameters for the three teeth. For this, (tangent to the borehole, along Y oi ) and F R _ tip (radial to the borehole, along X oi ). The axial force F A _ tip is obtained by adding the projection of F n _ tip along axis Z and the indentation force F ind (Fig. 9):

In the same manner, the forces F c _ chf and F n _ chf , applied on the radial part of the tool, are simulated and projected along the axis Y oi , X oi , and Z. The resulting forces are respectively F T _ chf (tangent to the borehole, along Y oi ), F R _ chf (radial to the borehole, along X oi ), and F A _ chf (axial force, along Z) (Fig. 9):

(7) ⎧ ⎪ ⎨ ⎪ ⎩ F T_tip = ∑ Z z=1 � F c_tip z × Y oi + F n_tip z × Y oi � F R_tip = - ∑ Z z=1 � F c_tip z × X oi + F n_tip z × X oi � F A_tip = ∑ Z z=1 � F n_tip z × Z � + F ind
The precise angular position of the tool in the borehole during drilling (θ i ) can be easily calculated. But experimentally, this angular position is difficult to measure during a drilling test. Therefore, the simulation of the cutting forces along X and Y axis cannot be compared to experimental results as the shift angle cannot be identified. For this reason, the comparison between the modelling and the measurements was based on the resultant force applied on the tool in the plane perpendicular to its axis (noted F xy ) and on axial force F z . From the measurements, F xy can be calculated with the measured forces F x and F y :

The measured forces F xy and F z can be respectively compared to simulated forces F RT and F A , where:

The measured cutting forces are presented in Rey et al. [START_REF] Rey | Modelling of cutting forces in orbital drilling of titanium alloy Ti-6Al-4V[END_REF]. The comparison between simulations and experimental measurements was performed considering the tool fully engaged into the material. Thus, in the paper, the entry and exit phases are not presented. Three tool revolutions are presented (Fig. 10).

It can be noted that the simulated axial force F A represents with a good fidelity the axial forces generated in orbital drilling (F z ).

The simulation of the resultant force F RT shows also very good results. The characteristic evolution of this force is well simulated, in relation with the evolution of F T and F R explained above. So, the proposed model permits to represent and to explain the specific evolution of this bending force (in a plane perpendicular to the axis of the tool). Moreover, the amplitude of this effort is well predicted, which validates the modelling and the identification of the cutting parameters.

(8) ⎧ ⎪ ⎨ ⎪ ⎩ F T_chf = ∑ 3 z=1 � F c_chf z × Y oi + F n_chf z × Y oi � F R_chf = - ∑ 3 z=1 � F c_chf z × X oi + F n_chf z × X oi � F A_chf = ∑ 3 z=1 � F n_chf z × Z � (9) ⎧ ⎪ ⎨ ⎪ ⎩ F T = F T_tip + F T_chf F R = F R_tip + F R_chf F A = F A_tip + F A_chf (10) F xy = √ F x 2 + F y 2 (11) F RT = √ F R 2 + F T 2

Hole

Tool

• Fig. 9 Global cutting forces on the tool As a result, the error between simulation and experimental results was assessed through the Bravais-Pearson correlation coefficients R RT and R A . For the transversal cutting force F RT , the coefficient R RT is 0.89. The mean error is 15 N, corresponding to 18.8%. This percentage is relatively high because of the low values of the cutting force. For the axial cutting force F A , the coefficient R A is 0.67. The mean error is 12 N, corresponding to 5.3%. These results reflect a high correlation between simulation and experimental results. The proposed modelling permits a good estimation of the forces generated in orbital drilling and can be used for the optimization of the process.

K c q c K c * = K c × h -q c (h = 0.01 mm) K n q n K n * = K n × h -q n (h = 0.01 mm) K ind × s q ind Radial part

Influence of the tool geometry

Influence of the geometry on the simulated cutting forces

Using the developed model, it is possible to simulate the cutting forces for different tool geometries and, thus, study the influence of this geometry. The goal is to correlate the tool tip geometry with the dimensional quality of the hole. This work will therefore focus on the forces having an impact on the final diameter and on the geometric quality of the hole.

The forces having an impact on the machined diameter are the forces that tend to bend the tool as a tooth is machining the surface of the hole. It was therefore chosen to study the force F R when each tooth is in contact with the surface of the hole. Thus, the force (F RZi ) is calculated when the tooth Zi is in contact with the surface of the hole by considering the radial force F R averaged over the area shown in Fig. 11. This zone is centered on the position where the tooth is in contact with the surface and represents 5° on each side of this position.

It is considered that the final surface of the hole is made by the tooth with the least effort because it is the tooth for which the tool bending will be minimal. It is, therefore, this effort (F RZmin ) that will be used to compare the different geometries.

Different tool tip geometries will be tested. For these simulations, the cutting conditions used are as follows: V c = 30m/ min ; f za = 0.005 mm/tooth; f zt = 0.04 mm/tooth.

As the geometry of the tool is complex, only a few characteristics will be studied. This work focuses on the axial zone of the tool. Indeed, it was identified that this zone is quite problematic. The tooth with the cut in the center was particularly fragile and wore very prematurely during the first holes. It is therefore interesting to study the utility of this center-cut and its impact on the drilling quality (Fig 12). The cutting area close to the tool center is always an issue for axial tools because the cutting conditions at this location are greatly degraded by the very low or zero cutting speed. Regarding orbital drilling, it is easy to calculate the effective speed in the (X, Y) plane (V e _ XY ) by considering the combination of the two rotations (N and N orb ):

The evolution of the effective speed along the cutting edge (Fig. 13-a) shows that it tends towards a zero value close to the center of the tool as it is the case with axial drilling. However, by analyzing more precisely the area close to the center of the tool, the effective speed becomes even negative (Fig. 13b) due to the combination of the rotation of the tool and the orbital rotation. Thus, even if the orbital rotation seems negligible in comparison to the rotation speed of the tool, it can have a significant impact on the cutting conditions in the center of the tool. It explains why the axial zone is so problematic in orbital drilling.

Using the model, different tool geometries will be simulated to study the possibility of eliminating or reducing ( 12)

V e_XY r Ai , φ i = r Ai × 2 × N -r ki r Ai , φ i × 2 × N orb × cos -r Ai , φ i -φ i
the material to be cut in this area. The first task consisted in simulating material removal with a tool without center-cut, so that all the teeth have a similar profile with a non-cutting radius at the center R cc (Fig. 15).

This simulation shows that it is essential to keep a noncutting radius at the center lower than the offset radius (R off ) to ensure that the cutting tool can perform all of the machining. In this study, the non-cutting radius varied from 0 to 1.5 mm (R off ) for a tool without a centercut: R cc = [0; 0, 5; 1; 1, 5]. For simulations with a tool having a center-cut, it is possible to have a non-cutting radius (R cc ) greater than the interpolation radius (R off ). In this case, the non-cutting radius varied from 0 to 2.5 mm: R cc = [0; 0, 5; 1; 1, 5; 2; 2, 5].

The other characteristics of the tool geometry remain identical to the tool used previously: three-flute tool (Z = 3) of diameter D t = 9 mm, with a corner radius R b = 1 mm (Fig. 14).

The other parameter studied with the simulation was the tool cutting edge angle κ r . The bending force (F RZmin ) is simulated for the different tool geometries with the following cutting conditions: V c = 30 m/min; f za = 0.005 mm/tooth; f zt = 0.04 mm/tooth.

The result of all these simulations is presented in Fig. 15. These graphs show that in general, the increase of the tool cutting edge angle or of the radius without

> =

Unmachined area center-cut leads to a decrease in the bending force F RZmin .

< < Tool

In the case of a tool with a center-cut, these variations are very significant, they go from 77 to -12N. This negative bending force means that the cutting forces generated by the three teeth tend to push the tooth machining the surface of the hole towards it. For the case of a tool without a center-cut, these variations are less important; the bending force goes from 77 to 31 N and never becomes negative.

To understand this result, the influence of these parameters on the evolution of the chip geometry of the axial part needs to be studied. The increase in radius without centercut or the presence of a tooth with center-cut significantly modifies the geometry of the chip (Fig. 16). In Fig. 16a, the increase in the non-cutting radius causes an increase of the chip section at the back of the tool and a decrease of the chip section at the front of the tool. The presence of a center-cut has the same effect by increasing the chip section at the back of the tool (Fig. 16b). However, by working in down-milling, the forces generated by the machining of the back part of the tool are oriented so as to push the tool towards the surface of the hole and therefore counter the bending forces generated by the radial part of the tool. It is a reason why the minimal bending force decreases as the radius without center-cut increases (Fig. 15b-d).

Fig. 17 shows the influence of the tool cutting edge angle on the chip section. It can be noted that the chip section at the back of the tool increases as the tool cutting edge angle increases.

This simulation explains the decreasing of the minimal bending force when the tool cutting edge angle increases (Fig. 16a-c).

In Fig. 10, the measured force F XY and the modelled force F RT show the successive passage of three teeth over the tool revolution. This is explained by the fact that the radial chip is not constant during the revolution of the tool. However, the differences between the peaks come from the presence of a single tooth with a center-cut. Thanks to the modelling, it is possible to study the evolution of the cutting forces to better understand the phenomena generated by the geometry of the axial part of tool. The tool performance and the cutting phenomena caused by the radial part of the tool are known and considerably studied in the literature. The cutting phenomena on the axial part are more complex due to the combination of the geometry of the tool tip and the complex tool path.

To better understand the mechanisms during drilling, the evolution of the tangential force F t at the contact point tool/work piece (according to Yoi) and of the radial force F r (Fig. 18) was modelled only for the axial part.

During one revolution of the tool, these two forces oscillate strongly, which means that the bending force is highly variable. As a consequence, a dynamic phenomenon is established and must be studied to understand the chip thickness of the radial part. Fig. 18 shows that when the tooth with center-cut (Z 1 ) is located in the "A" area ( 0 < 𝜑 < 𝜋 2 ), the tangential force is positive, and the radial force is negative but quite low, not causing a significant modification in radial chip section. When the tooth with center-cut (Z 1 ) is located in the "B" area ( 3𝜋 2 < 𝜑 < 2𝜋 ), the radial and tangential forces are both positive. Therefore, the resultant force tends to push the tooth being machined towards the surface. When the tooth with center-cut (Z 1 ) is located in the area "C," when the tooth (Z 2 ) is in contact with the hole surface, the radial force is positive and the tangential force is negative. Therefore, the resultant force tends to push the tooth (Z 2 ) towards the hole surface and in reverse tends to withdraw the tooth (Z 3 ) from the hole surface. Finally, when the tooth with center-cut (Z 1 ) is located in the area "D," when the tooth (Z 3 ) is in contact with the hole surface, the radial and tangential force are both negatives. Therefore, the resultant force tends to withdraw the tooth (Z 3 ) from the hole surface.

The hypothesis is that the radial chip section is not homogeneous for the three teeth because bending forces are different for each passage of the various teeth on the radial cut area. The radial chip thickness machined by the tooth (Z 2 ) is greater than that machined by the tooth (Z 3 ).

Results and discussions

To validate these simulations, orbital drilling tests were carried out. These tests were performed on a drill bench equipped with an orbital spindle. The cutting forces were recorded using the dynamometer Kistler and sampled at 10 kHz.

The hole diameter was 11.11 mm. Three different tool geometries were tested (Fig. 19). Only the axial part was different. The first tool is the tool previously studied with a tool cutting edge angle (κ r = 93°), one tooth with center-cut and the others with a radius without center-cut R cc = 2mm . The second tool is similar, only the tool cutting edge angle is different (κ r = 90° -flat tool tip). The third is similar to the second but without center-cut and with R cc = 1.5 mm. All the tools used were especially made for the study. The cutting conditions remained the same: V c = 30 m/ min; f za = 0.005 mm/tooth; f zt = 0.04 mm/tooth.

A series of holes was performed with each tool, with the same conditions (material, cutting parameters, hole diameter). The diameter of the holes was measured on a three-dimensional coordinate measuring machine (CMM).

The results in terms of diameter (Fig. 20) show that the presence of a tooth with center-cut on the axial part of the tool has a significant impact on the hole profile due to the bending phenomena. The hole profiles obtained with the tools no. 1 and 2 show an entrance hole diameter larger than the output diameter. This validates the previous simulation results (Fig. 18): the axial part of the tool causes a positive average radial force, increasing the hole diameter. At the exit of the hole, the axial part is not anymore into the material; therefore, the diameter of the hole decreases due to tool bending caused by the radial part of the tool.

Between the first tool and the second, only the tool cutting edge angle is different. The influence of this angle on the axial chip is showed on Fig. 17. The chip section for the second tool is more homogeneous between the back and the front parts of the tool, which can be seen also on the modelled cutting forces (Fig. 21). The maximum radial force modelled for the second tool is around 70 N whereas it reaches 90 N for the first tool. It explains why the variation of hole diameter is less important for the second tool than for the first one. Considering the third tool, the three teeth are identical and the axial part is flat so the chip is identical for the three teeth. That is why the model shows no cutting force (Fig. 21), the forces on each tooth cancelling each other.

Without the radial force generated by the axial part, the tool is submitted to a bending force due to the radial part, generating a smaller diameter. The increase of diameter at the exit of the hole is due to the relaxation of the bending force as the tool exits the material progressively.

For tools no. 1, no. 2, and no. 3, the F RZmin force is respectively 9.5 N, 25 N, and 50 N (Fig. 15), which reflects an increasing bending force for these tools and, therefore, a reduction in the drilled diameter. This agrees with the diameter measurements carried out (Fig. 20). The value of this force is difficult to use to accurately predict the final diameter because it would be important to take into account the dynamics of the cutting process. In addition, this force is extracted from a homogeneous cutting model over the whole of the axial part and comes from an estimate of a force at the center of the tool where the cutting conditions are strongly degraded for which the model has been validated. However, this value appears to be an interesting criterion for optimizing the geometry of the tools and/or the cutting conditions in order to limit the tool bending.

Conclusions

In this paper, the use of a force model for orbital drilling permitted to study and to better understand the important role of the geometry of the axial part of the drilling tool. Models available in the literature were developed for simple flat-end tools. But industrial tools used in orbital drilling are more complex, and their geometry has a great influence on the process. In this paper, a model of chip geometry and cutting forces developed in previous work was used for simulating complex tool geometries. This model permits to simulate the complex chip geometry and the associated cutting forces and to dissociate the role of each part of the tool on the chip and forces generated, helping to better understand the cutting phenomena occurring in orbital drilling (e.g., tool bending). This model was thus used to study the impact of the geometrical characteristics of the tool. It allowed to better understand the forces generated during the orbital drilling process and made it possible to link the geometry of the tool to the geometry of the drilled hole.

This model can be used to optimize the cutting conditions and the tool geometry in relation to the cutting forces generated (in direction and in amplitude). Currently, the orbital process struggles to meet the level of requirements of the aeronautical sector especially for the drilling of titanium alloys or multi-material stacks. The modelling developed could permit to improve the performance of this process.

The development of an accurate prediction of the tool bending magnitude and of the dimensional quality of the hole will be conducted in future works. This will need to pursue the development of the model to consider the microgeometry of the tool cutting edge and the evolution of wear, which remains a very critical point during the machining of this type of material.

Fig. 1 Fig. 2

 12 Fig. 1 Orbital drilling

Fig. 4 Fig. 5

 45 Fig. 4 References definition

Fig. 6 Fig. 7

 67 Fig. 6 Instantaneous chip section

Fig. 8

 8 Fig. 8 Representation of forces generated by the axial part of a tool with three teeth

Fig. 10 Fig. 11

 1011 Fig.[START_REF] Sadek | Characterization and optimization of orbital drilling of woven carbon fiber reinforced expoxy laminates[END_REF] Comparison of simulated and measured cutting forces. V c = 30 m/min; f za = 0.005 mm/tooth; f zt = 0.04 mm/ tooth

Fig. 12

 12 Fig. 12 Tooth wear with centercut

Fig. 14 Fig. 15

 1415 Fig. 14 Simulation of material removal for different values of the radius without center-cut

Fig. 16 Fig. 17

 1617 Fig. 16 Influence of the radius without center-cut (a) and the presence of the center-cut (b) on the geometry of the axial chip

Fig. 18

 18 Fig.[START_REF] Zhou | Modeling of non-linear cutting forces for dry orbital drilling process based on undeformed chip geometry[END_REF] Modelling of the radial and tangential forces of the tool axial part, considering the three teeth

Fig. 21

 21 Fig.[START_REF] Altintas | Generalized dynamic model of metal cutting operations[END_REF] Modelling of the cutting force of the tool axial part for the three tools

Table 1

 1 Geometrical parameters of the tool

	Tool radius	R t
	Number of teeth	Z
	Number of teeth with center-cut	Z cc
	Corner radius	R b
	Radius without center-cut	R cc
	Tool cutting edge angle	κ r

Fig. 3 Feed definition Table 2 Process parameters for orbital drilling

	Drilling radius	R h
	Interpolation radius, called offset	R off = R h -R t
	Pitch	P
	Cutting speed	V c
	Tool revolution speed (rev/min)	N = Vc 2 ×R t
	Axial feed (mm/rev)	f a
	Axial feed per tooth (mm/tooth)	f za =	f a Z
	Axial feed speed (mm/min)	V fa = f a × N
	Tangential feed (mm/rev)	f t
	Tangential feed per tooth (mm/rev/tooth)	f zt =	f t Z

Table 3

 3 Summary of the identified coefficients[START_REF] Rey | Modelling of cutting forces in orbital drilling of titanium alloy Ti-6Al-4V[END_REF]

Tool No.1 Tool No.2 Tool No.3

	93°= 90°= 90°W
	ith center-cut	Without center-cut
	Fig. 19 The different tool geometries tested
	Fig. 20 Average profile meas-
	ured on the hole for each tool	Tool No.1		Tool No.2		Tool No.3
		93°		90°		90°
		With center-cut		Without center-cut
	11.13			11.13		11.13
	11.12			11.12		11.12
	11.07 11.08 11.09 11.1 11.11 Diameter (mm)			11.07 11.08 11.09 11.1 11.11 Diameter (mm)		11.07 11.08 11.09 11.1 11.11 Diameter (mm)
	11.06			11.06		11.06
	11.05			11.05		11.05
	0 Measuring depth in the hole 1 0	2 0	0 Measuring depth in the hole 1 0	2 0	0 Measuring depth in the hole 1 0 2 0
		(mm)		(mm)		(mm)
	Tool No.1	Tool No.2		Tool No.3

Funding This work was carried out within the research project OPOSAP, funded by the region Midi-Pyrenees, the French state, and the European Commission. The work was also carried out within the context of the working group Manufacturing'21, which gathers 18 French research laboratories.

Code availability

The codes developed in this work are custom codes.

Author contributions All authors declare they have contributed to the study conception and design. Material preparation, data collection, and analysis were performed by P-AR. The first draft of the manuscript was written by P-AR, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Data availability All data and materials are accessible.

Declarations

Ethics approval The authors fully approve the journal's ethics policy.

Competing interests

The authors declare no competing interests.


Michele Guerini

Rasool Bux Khoso

Alizia Mantovani

Marco Giardino

Global drivers impact on Geodiversity and Geosystem services in Alagna Valsesia

published or not. The documents may come

Scan for more Info on my PhD Project

Michele Guerini michele.guerini@unito.it Global drivers impact on Geodiversity and Geosystem services in Alagna Valsesia (N-W Alps, Italy). Mapping and assessment for a sustainable development perspective Michele Guerini* 1-2 ; Rasool Bux Khoso 1 ; Alizia Mantovani 3 ; Marco Giardino 1-4 Natural capital consists of two natural elements: biodiversity and geodiversity. Geodiversity is often underestimated, but it is just as important as biodiversity because of the services it can provide to humans: Geosystem services. Because of its importance, geodiversity must be protected and promoted. Especially in today's context of global climate change, the conservation of geodiversity requires increased attention in order to protect geosystem services and geological heritage. UNESCO Global Geoparks (UGGps) are proving to be an effective tool for recognising the importance of geodiversity and protecting it through geological conservation measures, environmental education and the promotion of geotourism in the region. This study highlights the environmental framework of a mountainous area of the Sesia Val Grande UGGp by assessing and mapping geodiversity, geosystem services and geoheritage. The study then explores new approaches to provide targeted responses to local needs, with the aim of promoting geodiversity conservation and sustainable development.

  l e m b e r g M a n g a n e s e m i n e s P u l f e r S t a i n O t r o V a l l e y M o n t e R o s a g l a c i e r s A l p e S t o ff u l

1 2 3 4

 3 Geodiversity assessment highlights that Alagna Valsesia boasts high geodiversity in several areas , especially into the valley. Geosystem services assessment shows that geodiversity within Alagna provides all 5 types of geosystem services, especially regulating services. The geosites assessment shows 8 geosites in Alagna Valsesia with a great potential for geotourism, especially in the glacial and proglacial environment. The study demonstrates that using the DPSIR Framework allows to locate the global drivers of change for suggesting targeted responses to local needs. In Alagna Valsesia we identi�ied impacts on all the geosystem services provided, with a mention on loss of geodiversity and massive erosion, and suggested the responses linked to the SDGs to favor the sustainable development

  1. Earth Science Department, University of Turin, Italy; 2. EDYTEM, Université Savoie Mont Blanc, France; 3. Computer Science department, University of Turin, Italy; 4. Sesia Val Grande UGGp, Varallo, Italy

		20 25	20.1	18.6	16.4	17.9	19.2 19.4	17.6
		15
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	Humans factors	DPSIR Approach			Natural factors

Educa�onal ac�vuty for all genera�ons Elements of natural heritage Convenience and a�rac�on for geotourism 1. INTRODUCTION 2. STUDY AREA 3. METHODOLOGY 4. RESULTS 6. REFERENCES 5. CONCLUSIONS

					Literature Data	Geosite Recognition
					Fieldtrips Collection Data	Geodiversity Assessment	DPSIR Approach
			* Ref. 3		GIS Mapping	Geosystem Services
	Sesia Val Grande UGGp	Geosite map of Algna Valsesia		Bibliography, Data collection and analysis	Recognition of potential Geoheritage in the area	Solutions for sustainable use
	* Ref. 2		* Ref. 5		* Ref. 1
			Geosystem Services (GS)
			Regula�ng
			Suppor�ng
			Provisioning
			Cultural
			Knowledge
	.Massive erosion .Soil sealing .Environmental pollution .Water use for tourism .Land use Pressures Drivers .Mining .Tourism & Recreation .Transport .Infrastructure development .Fuel	.Loss of Geodiversity .Soil erosion .Loss of resources .Less water in�iltration .Worsening of environment States .Water stress .Waterproo�ing Impacts .Land use planning-management .Promote sustainable behaviour Responses SDGs achv 11.a 11.3 11.5 12.1 12.2.	Suppor�ng Provisioning Cultural Knowledge All The Services Regula�ng Impact on GS * Ref. 4	.Extreme events .Melting of ice & snow cover .Increasing natural processes .Permafrost degradation Pressures .Precipitation .Temperature .Permafrost .Glaciers .Rivers Drivers	.Floods .GLOF .Damage to utilities .Geodiversity Loss States .Exposed land .Loss of resources .Massive soil erosion Impacts SDGs achv 11.3 13.2 15. .Adaptation strategies .Water management .Enhance geodiversity Responses
			.Over-use of resources			.Slope instabilities .New lakes


Michele Guerini

Rasool Bux Khoso

Alizia Mantovani

Cristina Viani

Marco Giardino

Dynamic Geodiversity, Geosystem Services, and Sustainable Development: Insights from Sesia Val Grande UNESCO Global Geopark

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Geodiversity is a concept that is gaining popularity in the scien�fic community. Geodiversity, like biodiversity, is part of natural capital and can provide benefits to human communi�es: Geosystem Services. Because of its importance, it is necessary to conserve and promote the geodiversity. The UNESCO Global Geoparks are a powerfull tool to recognise the importance of the geodiversity and to promote Geoconserva�on, Educa�on, and Geotourism within the territory. In par�cular, the current global climate change requires more and more a�en�on to geodiversity and geoheritage conserva-�on, in order to protect the geosystem services.

In this study we explore a new approach in an a�empt to provide targeted responses to the needs of the territory, with the aim of pro-mo�ng geodiversity conserva�on and sustainable development

Scan for Abstract

  shown that the DPSIR framework is a good inve-s�ga�ve approach to iden�fy ac�ons needed for sustainable land development that enhances geodiversity, but also enables climate change mi�ga�on strategies. Future work will aim to assess the actual climate change in the area through the calcula�on of climate indices and the use of indicators. It will then be necessary to iden�fy geosites that are par�cularly affected by climate change in order to develop strategies to protect the geoheritage and enhance its value in order to raise awareness among tourists.Dynamic Geodiversity, Geosystem Services, and Sustainable Development:Insights from Sesia Val Grande UNESCO Global Geopark Michele Guerini 1,2 ; Rasool B. Khoso 1 ; Alizia Mantovani 3 ; Cris�na Viani 1 ; Marco Giardino1,4 (1) Earth Science Department, University of Turin, Italy (2) EDYTEM, Université Savoie Mont Blanc, Chambery, France (3) Computer Science Department, University of Turin, Italy (4) Sesia Val Grande UGGp, Varallo, Italy is a municipality within the Geoparks and claims to be a place of great geodiversity, especially into the valley.Geodiversity provides all 5 types of geosystem services, especially regula�ng services.The evalua�on of the geosites shows 8 geosites in Alagna Valseisa with a great poten�al for geotourism, especially in the glacial and proglacial environment. The DPSIR Framework allows to locate the global drivers of change and to iden�fy the ac�on needed to mi�gate the impacts. In Alagna Valsesia we iden�fied impacts on all the geosystem services, with a men�on on loss of geodiversity and massive erosion (caused mainly by more frequent landslides and extreme precipita�ons). With DPSIR we can suggest the responses linked to the SDGs -Suzuki DA, Takagi H. 2018. Evalua�on of Geosite for Sustainable Planning and Management in Geotourism. Geoheritage 10 : 123-135. -Forte JP, Brilha J, Pereira DI, Nolasco M. 2018. Kernel Density Applied to the Quan�ta�ve Assessment of Geodiversity. Geoheritage 10 : 205-217. -Togne�o F, Pero� L, Viani C, Colombo N, Giardino M. 2021. Geomorphology and geosystem services of the Indren-Cimalegna area (Monte Rosa massif-Western Italian Alps). Journal of Maps 17 : 161-172. -Pero� L, Bolla� IM, Viani C, Zanole� E, Caironi V, Pelfini M, Giardino M. 2020. Fieldtrips and virtual tours as geotourism resources: Examples from the Sesia Val Grande UNESCO Global Geopark (NW Italy). Resources 9 : 1-34. -Omann I, Stocker A, Jäger J. 2009. Climate change as a threat to biodiversity : An applica�on of the DPSIR approach. Ecological Economics 69 : 24-31.


Noemi Mauro
email: noemi.mauro@unito.it

Zhongli Filippo Hu
email: zhonglifilippo.hu@unito.it

Liliana Ardissono
email: liliana.ardissono@unito.it

Service-oriented Justification of Recommender System Suggestions

Keywords: Summarization of recommendation lists, Service Blueprints, Explainable AI, Sentiment analysis

In the selection of products or services, overviewing the list of options to identify the most promising ones is key to decision-making. However, current models for the justification of recommender systems results poorly support this task because, as they exclusively focus on item properties, they generate detailed justifications that are lengthy to skim. Moreover, they overlook the existence of a complex item fruition process which can impact customer satisfaction as well. For instance, consumer feedback shows that relevant factors in home booking include both the properties of apartments, and previous customers' perceptions of the interaction with the personnel who manages the homes. To address this issue, we propose a visual model that exploits an explicit representation of the service underlying item fruition to generate a high-level, holistic summary of previous consumers' opinions about the suggested items. From this overview, the user can identify the relevant items and retrieve detailed information about them, in a selective way, thus reducing information load. Our model is instantiated on the Airbnb experiences domain and uses the Service Blueprints to identify evaluation dimensions for the incremental presentation of data about items. A preliminary user study has shown that our model supports user awareness about items by enabling people to quickly filter out the unsuitable recommendations, so that they can analyze in detail the most relevant options.

Introduction

As reported in [START_REF] Chen | Experiment on sentiment embedded comparison interface[END_REF], product comparison is a crucial decision stage that buyers usually perform before they make a choice. Some recommender systems support this type of activity by generating post-hoc justifications of suggestions that highlight the main features of items which might interest the user [START_REF] Musto | Linked open data-based explanations for transparent recommender systems[END_REF][START_REF] Ni | Justifying recommendations using distantly-labeled reviews and fine-grained aspects[END_REF][START_REF] Musto | Generating post hoc reviewbased natural language justifications for recommender systems[END_REF][START_REF] Chen | Experiment on sentiment embedded comparison interface[END_REF]. However, these systems generate possibly lengthy, item-centric descriptions that only partially represent consumer experience, and might overload the user with too much data. For instance, in the e-commerce domain, it is not only a matter of liking the purchased item. The overall experience also includes the customer care, which might strongly impact the overall satisfaction about products. In service fruition, the user might have to interact with multiple actors, all of which might positively or negatively influence the final service evaluation.

We claim that, to enhance user awareness about suggestions, recommender systems should present both item-centric data, and perceptions about item fruition, in a compact format that helps the user identify the options to be evaluated in detail. For this purpose, we propose a service-oriented, visual model that supports the summarization of holistic information about items, taking the overall context in which they are experienced into account. The visualization is based on interactive bar graphs that describe customer experience quantitatively. Moreover, our model organizes the aspects emerging from online reviews around coarse-grained and fine-grained evaluation dimensions which support a selective and incremental inspection of consumer feedback. We use the Service Blueprints [START_REF] Bitner | Service blueprinting: A practical technique for service innovation[END_REF] to define the evaluation dimensions of customer experience for information summarization. Service Blueprints support the design and development of physical and online products and services, by focusing on the actions that the customer carries out, and on the tangibles (s)he encounters, during item fruition. They help organize the feedback about items around the user.

We apply our model to the presentation of Airbnb experiences, which consist of informal, leisure activities published by people in the Airbnb platform (https://www.airbnb.com). An experience could be a boat tour in a river, or a half day spent with somebody who teaches the preparation of a traditional dish. The host of the experience typically participates in it, and an experience can involve multiple guests. Thus the user interacts with other people during its enactment. Our service-oriented representation of items, centered around tangibles and human actors, is suitable to model all these stages of fruition. Moreover, it can be applied to other services involving multiple actors, such as home booking.

In the following, Section 2 outlines the related work. Section 3 presents the proposed summarization model, and Section 4 concludes the paper.

Background and Related Work

Service Blueprints

A Service Blueprint [START_REF] Bitner | Service blueprinting: A practical technique for service innovation[END_REF] is a visual description of the user experience with a service, such as a hotel, or an online retailer. It models the stages a person encounters when (s)he uses the service, from the start point (e.g., enter website or enter shop) to the end one (e.g., customer care), taking both onstage and backstage actions into account. Different from standard workflows, blueprints focus on customer actions. Therefore, they model the actors and the tangibles the user is expected to interact with. A typical Service Blueprint has five components. The Physical evidence includes the tangibles that the customer comes in contact with. For instance, in a hotel service, this component represents the hotel website, the parking, the desk, the elevators, the room, and so forth. The Customer actions include the actions that the guest performs when (s)he experiences the service; e.g., the reservation and the arrival at the hotel. The Onstage/visible contact employee actions are the actions that the contact employees perform while they interact with the customer; e.g., desk registration. The Backstage/invisibile contact employee actions are the actions carried out by the employees that are not visible to the customer but are useful to serve her/him. The Support processes include the activities carried out by non-contact employees, such as the reservation and registration systems used by the hotel.

Explaining and Justifying Recommendations

The research about recommender systems promotes their transparency to enhance user trust and acceptance of results [START_REF] Tintarev | Explaining recommendations: design and evaluation[END_REF][START_REF] Pu | Trust-inspiring explanation interfaces for recommender systems[END_REF]. In the systems based on a single item suggestion algorithm, results are typically explained in terms of inference traces [START_REF] Herlocker | Explaining collaborative filtering recommendations[END_REF][START_REF] Nunes | A systematic review and taxonomy of explanations in decision support and recommender systems[END_REF][START_REF] Tintarev | Explaining recommendations: design and evaluation[END_REF]. For instance, some aspect-based recommender systems explain suggestions by highlighting the features of items which match, or mismatch, the target user's preferences [START_REF] Muhammad | A live-user study of opinionated explanations for recommender systems[END_REF]. Other ones support feature-based item comparison [START_REF] Chen | Explaining recommendations based on feature sentiments in product reviews[END_REF][START_REF] Millecamp | What's in a user? towards personalising transparency for music recommender interfaces[END_REF], or information exploration based on the visualization of the relevance of items to the keywords of search queries [START_REF] Chang | SearchLens: composing and capturing complex user interests for exploratory search[END_REF][START_REF] Di Sciascio | Rank as you go: User-driven exploration of search results[END_REF]. Graph-based recommenders use the connections between users and items as explanations [START_REF] Amal | Relational social recommendation: application to the academic domain[END_REF][START_REF] Wang | Ripplenet: propagating user preferences on the knowledge graph for recommender systems[END_REF]. A few systems fuse recommendation and explanation in the same process [START_REF] Muhammad | A live-user study of opinionated explanations for recommender systems[END_REF][START_REF] Dong | User-based opinion-based recommendation[END_REF][START_REF] Lu | Why i like it: Multi-task learning for recommendation and explanation[END_REF]. We aim at justifying recommendations, rather than explaining how the system generated them. The reason is that the traces of inference might be too complex for the user [START_REF] Nunes | A systematic review and taxonomy of explanations in decision support and recommender systems[END_REF]. Moreover, if s(he) applies other evaluation criteria than those implemented in the recommender system, the explanations might be hardly convincing. However, different from previous justification approaches [START_REF] Pu | Trust-inspiring explanation interfaces for recommender systems[END_REF][START_REF] Chen | Experiment on sentiment embedded comparison interface[END_REF][START_REF] Musto | Linked open data-based explanations for transparent recommender systems[END_REF][START_REF] Ni | Justifying recommendations using distantly-labeled reviews and fine-grained aspects[END_REF][START_REF] Musto | Generating post hoc reviewbased natural language justifications for recommender systems[END_REF], which directly extract detailed features of items from reviews, we identify the aspects to be visualized by using the underlying service model, represented as a Service Blueprint, to steer the analysis of consumer feedback. This enables us to present items in a high-level, holistic perspective, which covers all the stages of interaction with services that are behind item fruition, and provides a synthesis of consumer opinions that facilitates the inspection of large sets of results. The usage of Service Blueprints also distinguishes our work from classical aspect-based justification and explanation [START_REF] Muhammad | A live-user study of opinionated explanations for recommender systems[END_REF][START_REF] Pu | Trust-inspiring explanation interfaces for recommender systems[END_REF][START_REF] Millecamp | What's in a user? towards personalising transparency for music recommender interfaces[END_REF], which focus on few peculiarities of items. By applying the blueprints, we extract the sentiment about a small set of evaluation dimensions that describe the overall experience with items.

Methodology

During April and May 2019, we scraped the Airbnb web site to collect data about the published experiences. We selected the content written in English. For each experience E, we extracted the reviews about it, and details such as name, host, price, planned activities, and URL of a picture of E. We collected data about 254,253 Airbnb users, 11,086 experiences and 336,288 reviews.

Reviews can cover different evaluation dimensions. For instance, the following example describes multiple aspects (tour, atmosphere) and provides feedback about the host: "Very nice tour, very different than all the big commercial tours. The guide was really nice and created a friendly atmosphere from the start!".

Service-based Representation of Airbnb Experiences

We introduce a service-oriented representation of items to capture the multiple perspectives about them emerging from online reviews. This representation is obtained in two steps. The first one is the definition of the blueprint describing the service. In this step, we represent the customer actions to be carried out during the fruition of the experiences. Moreover, we identify the actors and the tangibles which the guest is exposed to, as these elements contribute to her/his overall satisfaction. The second step is the identification of evaluation dimensions measuring consumers' perceptions about items, depending on those actors and tangibles.

We built the blueprint of Airbnb experiences, shown in Fig. 1, by studying both the service, and existing models available for the hotel booking domain [START_REF] Bitner | Service blueprinting: A practical technique for service innovation[END_REF]. The first tangible that the guest encounters is the Airbnb web site, through which (s)he can interact with the host of the experience and make the reservation. When the experience starts, the guest arrives at the meeting point and (s)he interacts with the host and with the other guests. Then, (s)he participates in the planned activities. Finally, (s)he closes the interaction with the other people.

While the Physical Evidence level of the blueprint describes the objects that the guest is expected to encounter, it does not represent human actors. However, the Customer Actions level provides both types of information. Thus, to obtain a holistic assessment of consumer perceptions, we map Customer Actions to evaluation dimensions associated with their involved tangibles and actors; see Fig. 2. To support both the summarization and a detailed organization of information, we define two levels of dimensions: a fine-grained one, related to the tangibles and actors involved in the Customer Actions, and a coarse-grained one, which describes the perceptions about the overall service at a high level. Fine-grained evaluation dimensions are grouped into coarse-grained ones, as shown by the colors in Fig. 2. For completeness, we introduced the Experience dimension, which is not strictly related to tangibles or human actors, because online reviews frequently mention intangible aspects such as ambience and time, which we want to represent to thoroughly model consumer feedback.

Analysis of Reviews

Given the Service Blueprint and the mappings to evaluation dimensions, we analyze the perceptions about an experience E in the following pipeline:

1. Extraction of the aspects mentioned in the reviews of E. Starting from the lemmatized text of each review R, we use the Spacy dependency parser [START_REF]Explosion AI: SpaCy -industrial Natural Language Processing in python[END_REF] to build the syntax tree of the sentences of R. For each syntax tree, we apply an extension of the Double Propagation algorithm [START_REF] Qiu | Opinion word expansion and target extraction through double propagation[END_REF] to identify aspects and corresponding adjectives, as well as the adjectives referring to the host, who is frequently mentioned by name, or using pronouns, in nominal predicates. The output of this step is a list of < aspect, adjective > pairs describing the mentioned aspects, combined with the adjectives used to qualify them. The second term is null when the adjective is missing.

2. Computation of the sentiment about aspects. We compute the polarity of each < aspect, adjective > pair as the mean polarity value returned by the TextBlob [START_REF] Loria | TextBlob: Simplified text processing[END_REF] and Vader [START_REF] Hutto | VADER: A parsimonious rule-based model for sentiment analysis of social media text[END_REF] opinion mining libraries. Then, we normalize this value in [START_REF] Ahlberg | Visual information seeking: Tight coupling of dynamic query filters with starfield displays[END_REF][START_REF] Chen | Explaining recommendations based on feature sentiments in product reviews[END_REF]. We also count the occurrences of the pair in the reviews of E to measure how frequently people express the corresponding opinion.

3. Classification of aspects w.r.t. service evaluation dimensions. For this task, we combine entity recognition, which supports the identification of places and human actors, with the consultation of a dictionary for each finegrained evaluation dimension. Each dictionary includes the lemmatized terms referring to the related tangibles or actors. The Places dictionary includes terms such as park, square, and river. The People of the group one includes words like people, group, and travelers. The Experience dictionary includes intangible concepts such as atmosphere, and time. Table 1 shows a subset of the aspects concerning an experience E. Each row shows an aspect and an adjective extracted from the reviews about E, the degree of appreciation (Evaluation) of the < aspect, adjective > pair in [START_REF] Ahlberg | Visual information seeking: Tight coupling of dynamic query filters with starfield displays[END_REF][START_REF] Chen | Explaining recommendations based on feature sentiments in product reviews[END_REF], and its frequency within these reviews.

The last component of the row is the fine-grained evaluation dimension in which the aspect is classified.

To build the dictionaries, we first extracted the terms from a sample set of Airbnb reviews, by applying the techniques described in the step above. Then, three researchers from our University staff collaborated to map terms to the fine-grained evaluation dimensions of Fig. 2. When the researchers disagreed with each other, they discussed the outcome with the authors of the present paper. Notice that Airbnb experiences vary a lot, depending on the proposed activity. Therefore, a complete representation of terms within the dictionaries is practically impossible. Thus, when the aspect extraction returns a term that cannot be classified in any dictionary, we assume by default that it refers to the Experience dimension that has a broader scope than the other ones.

Visual Summary of User Feedback

We aim at enabling the user to overview a recommendation list, looking for the most promising items, and then focus on a few of them for a deeper analysis. This principle is inspired by Ben Shneiderman's zoom and pan information exploration model [START_REF] Ahlberg | Visual information seeking: Tight coupling of dynamic query filters with starfield displays[END_REF], which puts the user in control of the search process by supporting an incremental access to information. In our model, the classification of aspects in fine-grained and coarse-grained evaluation dimensions, and the computation of the associated sentiment, are the key elements to obtain this type of interaction because they support the presentation of a multi-faceted evaluation of consumer perceptions, at different levels of detail.

Fig. 3 shows the presentation of an Airbnb experience E. Below the picture of E, there are its host name, title, price, and a "Show more" link to retrieve its Fig. 3: Visualization of the Airbnb experience of Table 1. A visual legend, not shown, maps the colors of the bars to coarse-grained evaluation dimensions. details. The bar graph summarizes consumer perceptions about E by showing the values of the coarse-grained evaluation dimensions emerging from its reviews. Each bar represents previous consumers' level of satisfaction about a dimension D, and it takes as value the weighted mean of the evaluations of the aspects classified in D. For each aspect, we use as weight its frequency in the reviews of E to tune its influence based on how many people share the opinion. The Search on Web Site bar is missing because the reviews do not refer to it.

By clicking on a bar, a box opens to present a few aspects extracted from the reviews of E that refer to the related coarse-grained dimension. For instance, see the "Experience appreciation" widget in Fig. 3. We aim at selecting diverse attributes to provide the user with rich qualitative data about previous guests' feedback. Thus, within the box of a coarse-grained dimension D, we show the most relevant aspects concerning each of its fine-grained dimensions, with their frequency. For instance, "great experience (39), . . . , big boat (17), . . . , great snack (5)". We use the frequency of aspects in the reviews of E as a measure of relevance because it reflects the conformity of consumer opinions.

Preliminary Test with Users in the Home Booking Domain

We tested with users a preliminary version of our model that is applied to the home booking domain and employs the Service Journey Maps [START_REF] Richardson | Using customer journey maps to improve customer experience[END_REF] to represent items and to analyze online reviews. See [START_REF] Mauro | A service-oriented perspective on the summarization of recommendations[END_REF] for details. This preliminary test makes sense because, similar to Airbnb experiences, the target of this system are young and middle-aged people who select items for leisure and work.

We recruited 11 participants (54.54% women; 45.46% men) representative of the target users. People joined the user study on a voluntary basis, without any compensation, and they gave their informed consent to participate in it.

Participants were between 19 and 57 years old (mean age = 36.36), and their educational level covered middle, high school and university. They declared that they regularly use the Internet. Based on the ResQue questionnaire [START_REF] Pu | A user-centric evaluation framework for recommender systems[END_REF], we asked participants if they tend to trust a person or thing, even though they have little knowledge about it: 9.09% of people declared that they very probably trust it, 18.18% probably trust it, 54.55% probably do not trust it, and 18.18% very probably do not trust it. We can thus hypothesize that the majority would desire to have a relevant amount of data to evaluate the recommended items.

We defined two tasks to test two versions of the visual model and we investigated their support to decision-making. In TASK1, for each presented home, we only showed the bar graph representing the coarse-grained evaluation dimensions we defined for home booking. In TASK2, for each presented home, we linked every bar of the graph to a set of aspects that justify its value. The user could view the aspects concerning a specific evaluation dimension by clicking on the associated bar. We asked participants to rate 5 homes for each task, or to opt out if they were not able to make a choice given the presented information.

In TASK1, 12.73% of the ratings were "I don't know"; in TASK2, participants rated all the homes. This means that, if the bar graph does not provide any qualitative data to justify the values of the evaluation dimensions, it poorly supports decision-making. Notice that, in the post-task questionnaire after completing TASK1, 54.54% of people declared that the given information was not enough to rate the homes. Differently, in TASK2, the number of people encountering decision-making problems was lower.

These findings suggest that a visual summary of previous experience with items, based on coarse-grained evaluation dimensions, helps the user efficiently filter out unsuitable options. However, users want to explore the details about the promising items before making any selection decisions, a typical attitude towards high-investment products and services. This finding highlights the importance of the method that is applied to select the aspects for summarization. As the Service Journey Maps do not explicitly represent onstage and backstage actions, tangibles and actors, they poorly support the classification of consumers' opinions in fine-grained evaluation dimensions. We thus concluded that we need the power of Service Blueprints, and a two-level representation of evaluation dimensions, to organize the aspects extracted from reviews in an efficacious way.

Conclusions and Future Work

We described a service-oriented visual model for the justification of item suggestions that enables the user to overview recommendation lists by means of a holistic analysis of consumer feedback, based on Service Blueprints. Different from previous work, our model summarizes previous consumers' perceptions about the whole item fruition process, rather than only focusing on item properties. In a preliminary user test, we collected positive feedback about the support of our model to user awareness and decision-making. These findings encourage to use the model to enhance information exploration support, and the presentation of recommender systems' results, with a user-centric perspective on items. A systematic user test of our model is part of our future work.

Fig. 1 :

 1 Fig. 1: Service Blueprint of Airbnb experiences.

Fig. 2 :

 2 Fig. 2: Evaluation dimensions of Airbnb experiences. Colors represent the associations fine-grained and coarse-grained dimensions.

Service Blueprint of Airbnb Experiences

	Physical	Evidence	Website	Website	Website	transportation Parking Public	Tools Places	Tools Places
	Customer	Actions	Search on website	Talk with host	Book the experience	Arrive at the meeting point	Meet host and people of the group	Enjoy the experience	Thank the host
	Onstage	Actions				Process registration
	Backstage	Actions			Make reservation for guest
	Support	Processes	Airbnb website (search engine)	Airbnb website (chat system)	Airbnb website (reservation and system) registration

Table 1 :

 1 Aspects extracted from the reviews of an Airbnb experience. The rows are colored as the coarse-grained evaluation dimensions of Fig.2.

	Aspect	Adjective	Evaluation	Frequency	Fine-grained Evaluation Dimension
	experience	great	4.43	39	Experience
	tour	great	4.43	19	Experience
	boat	big	3.00	17	Tools
	time	great	4.43	15	Experience
	host	great	4.43	14	Host
	host	friendly	3.87	14	Host
	experience	amazing	4.19	13	Experience
	group	amazing	4.19	10	People of the group
	snack	great	4.43	5	Tools