Thomas Ganne

Jérôme Crépin

Serge Serror

André Zaoui
email: zaoui@lms.polytechnique.fr

´ro ˆme Cre ´pin

Andre ´zaoui

Cracking behaviour of PVD tungsten coatings deposited on steel substrates

Keywords: Magnetron sputtering, Residual stresses, Crystallographic texture, Critical cracking stress, Tungsten, Coating

published or not. The documents may come

Cracking behaviour of PVD tungsten coatings deposited

on steel substrates: the influence of film thickness

Introduction

Coatings are more and more frequently used in order to improve the mechanical, thermal and chemical properties of various substrates. Their shielding effect has been proved to be highly efficient as long as they are not subjected to significant mechanical damage. Consequently, any optimisation procedure of the coating-substrate systems requires the identification of the damage mechanisms and a good understanding of the connection between microstructural characteristics, which strongly depend on the processing conditions [1], and the global damage behaviour [2]. This paper aims at improving the understanding and the quantitative description of the damage behaviour of brittle PVD coatings deposited on a ductile substrate from the experimental study of tungsten-steel systems. This analysis cannot be achieved without an accurate characterisation of the initial state of the coatings, especially their residual stresses and crystallographic texture. Thermal stresses are the result of cooling from the deposition to room temperature, due to the thermal expansion coefficient mis-match between the coating and the substrate. Additional residual stresses, the so-called "intrinsic stresses", result from growth mechanisms: they are due to the accumulation of crystallographic flaws developing in the coating [1]. They are generally compressive, with an order of magnitude of 1 to 4 GPa. A number of studies have been devoted to the investigation of residual stresses in such coatings but the influence of the processing conditions and of the coating thickness on their overall level still needs to be known more precisely; the same conclusion applies to crystallographic textures. These questions are investigated in W-steel systems in Section 3.

The damage mechanisms which are the most frequently active in such systems are the coating mode I cracking and the interface debonding [2][3]. The quantitative mechanical characterisation of damage initiation and kinetics requires adequate experimental techniques: in this study, in-situ tensile and four-point bending tests have been performed inside a S.E.M. chamber; an acoustic emission analysis has been developed for the study of the cracking kinetics whereas a microextensometry technique has been used for a better investigation of the strain field heterogeneity along the interface. The combined use of these techniques has led to the identification of the active cracking mechanisms, and to the determination of an intrinsic critical cracking stress and of the associated energy release rate from Hu-Evans's model [4] (Section 4). Moreover a preliminary analysis of the cracking kinetics has been conducted (Section 5), through the comparison of the experimentally defined minimum distance between two neighbour cracks with the one which can be predicted from Hu-Evans's model [4]; special attention has been paid to the influence of the coating thickness and partial results have been obtained on the influence of the processing conditions and the substrate yield stress and plastic behaviour on the limit crack density.

Materials

Tungsten coatings have been deposited on steel substrates by magnetron sputtering. The technical details of the sputtering procedure used have been reported in a previous paper [5]. Two processing conditions have been chosen. The first ones, called "A conditions" (3 kW target power, 3.5 Pa Ar pressure, Ϫ25 V substrate bias; samples labelled from A to I with a coating thickness ranging from 0.6 to 30 µm), have been used to study the influence of the coating thickness on the overall mechanical properties of the samples (residual stresses, critical cracking stress, . . .). The second processing conditions, called "B conditions" (1.5 kW target power, 2 Pa Ar pressure, Ϫ25 V substrate bias; samples J and K) have been chosen for 30 µm thick coatings in order to check the dependence of the cracking mechanisms on the processing parameters. In both cases, the obtained coatings exhibit a specific microstructure of densely packed fibrous grains.

The experimental analysis of the mechanical characteristics of the coatings, such as the residual stresses or the critical cracking stress, requires the knowledge of their elastic properties. Several authors [6][7][START_REF] Angelis | Proceeding of Ann[END_REF] have determined the Young modulus of PVD tungsten coatings submitted to a compressive residual stresses state by nanoindentation and bending tests: they have shown that the experimental values, ranging from 360 to 430 GPa, are close to those of the bulk material (410 GPa). So, we assume in what follows that the Young modulus, the Poisson ratio as well as the coefficient of thermal expansion of our coatings, which are submitted to compressive residual stresses, are given by the bulk tungsten values (see Table 1).

Most of the studied substrates, constituted of a 35NCDV12 steel, exhibit a martensitic structure. Some of them ("type 2") have been subjected to an annealing treatment in order to modify their yield stress so as to be able to investigate the influence of the substrate yield stress on the cracking behaviour of the coatings. The mechanical properties of both substrates are also reported in Table 1. The different sample characteristics (coating thickness, processing conditions and substrate) are reported in Table 2.

Crystallographic texture, residual stresses and chemical analysis

The initial state of the coatings studied has been characterised through the investigation of their crystallographic texture, residual stress state and chemical composition.

Crystallographic texture

The crystallographic texture of f.c.c coatings with a ͗110͘ growth orientation [9] and of b.c.c coatings with a ͗111͘ growth orientation [10] has already been shown to depend on their thickness. This dependence has been checked on our samples through an X-ray diffraction analysis using a Cu K α radiation; the analysed volume is 1 mm 2 ×1 µm (average depth) large. The texture has been characterised by the texture coefficient T hkl [11] which is defined, for any (hkl) orientation, as the ratio of the diffraction ray intensity, I hkl , to the theoretical diffraction ray intensity of an isotropic tungsten powder, I o,hkl , normalised by the average value of these ratios over the number n of (hkl) rays studied:

T hkl ϭ ͩ I hkl I o,hkl ͪͲͩ n Ϫ1 n i ϭ 1 I i hkl I i o,hkl ͪ (1)
where I hkl =(hkl) studied ray intensity, I o,hkl =(hkl) theoretical ray intensity, and n=number of (hkl) rays studied. The greater this coefficient with respect to 1, the stronger the texture along the considered direction. This analysis has been confirmed by classical pole figures. Figure 1 shows that the T 222 coefficient increases with the coating thickness, which characterises the development of a ͗111͘ texture. This result is in agreement with Gergaud's [10] observations on Mo coatings, which have the same crystallographic structure. The texture can be considered as isotropic only for the 0.6 µm thick A coating.

Residual stresses

Residual stresses have been determined by Xray diffraction with a Cu K α radiation (l cu =0.1504 nm), according to the "sin 2 y" method [START_REF] Maeder | Revue Franc ¸aise de[END_REF]. From the Bragg relation, the measured lattice spacing of {hkl} planes, d hkl , is linked to the elastic strain component e fy by:

e fy ϭ ln ͩ d hkl d 0 hkl ͪ Ϸ d hkl Ϫd 0 khl d 0 hkl ϭϪ cotan(q)•(q (2) Ϫq 0 )
where d 0 hkl is the stress-free lattice spacing and e fy refers to the Euler angles f and y defined in Fig. 2. The {123} planes have been chosen for a better measurement accuracy.

Since tungsten single crystals can be considered as elastically isotropic [START_REF]Smithells Materials Reference Book[END_REF], the same property is true for textured polycrystalline coatings [START_REF] Zaouali | [END_REF]. According to a Reuss-type assumption (homogeneous stress state in the diffracted volume), the stress-strain relation is given by: 3) is independent of f:

e
e fy ϭ 1 ϩ n E •s•sin 2 yϪ n E •s, (4)
so that s can be directly derived from the slope of a( e fy , sin 2 y) plot, through the value of the elastic constants of the material.

In order to validate the assumption of an isotropic plane residual stresses state, measurements have also been performed at various f angles. With use of the property of vanishing normal stress (s 33 ) at the free surface of the diffracted volume, all the components of the stress tensor and the stress-free lattice parameter can be determined (the latter is linked to q 0 by the Bragg relation: 2d 0 sinq 0 =l).

Figures 3 and4 show that, for several thickness values (f=0) and for several f angles (t f =1.6 µm), the relation between e fy and sin 2 y is linear; this result confirms the implicit assumption of a negligible stress gradient in the diffracted volume. For the 0.6 and 1.6 µm thick coatings (A and C, Fig. 4), the whole thickness of the coatings has been investigated. For different f angles and with the assumption of vanishing s 33 , all the components of the stress tensor of samples B, D, G and J have been estimated (Table 3).

The good agreement that can be observed between the results obtained from these two methods definitely validates the assumption of an isotropic plane residual stress state in the coatings. Moreover, these results are in agreement with numerical results from 3D finite element computations where the residual stresses are generated by an adequate temperature field in a bimaterial with different thermal expansion coefficients. The results are reported in Appendix A. They show that the stress gradient is negligible in the coating thickness, which validates our experimental results. The stress-free lattice parameter has been found to be equal to 0.3168±0.0001 nm for samples A to I and to 0.3170±0.0002 nm for samples J and K. These values are a little bit larger than the bulk tungsten parameter (0.3165 nm), which is in agreement with previous results on PVD tungsten coatings [START_REF] Vink | [END_REF][16].

Chemical analysis

Gergaud [10] has shown that, for identical processing conditions, the gas (argon) concentration in PVD Mo coatings increases with the development of a ͗111͘ texture. In order to check this effect on the growth of our PVD tungsten coatings, the amount of gas (Ar) embedded in tungsten coatings has been determined by electron probe microanalysis (EPMA) with a Cameca SX-50 device. The K α X-ray emission of argon emitted by the sample under electron bombardment (20 kV, 100 nA) has been compared to the analysis of an Ar-Si standard sample containing 5.2%wt. Ar. No gradient of the chemical composition has been observed through the coating thickness. The Ar concentration is equal to 0.2±0.1 %at. for samples A to I and 0.3±0.1 %at. for samples J and K. This result can be related with the differences of processing conditions and with the fact that the lattice stress-free parameter d 0 is slightly larger for samples J and K than for the other ones.

Discussion

There is a definite correlation between the texture development and the decrease of the residual stresses level when the coating thickness is increas-ing. Dobrev [9] has proposed to correlate this texture development with the ion channelling directions in the crystal lattice. For a b.c.c lattice, these channelling directions are more and more favorable in the sequence ͗110͘, ͗100͘, ͗111͘. During the impact between an energetic particle and the crystal lattice, the energy dissipation induces either a partial recrystallisation due to the "thermal spike" effect or the creation of defects due to consecutive binary collisions. Therefore, under this bombardment, the coating is growing along the more favorable channelling directions, which corresponds to a ͗111͘ growth direction for a b.c.c lattice.

Moreover, according to Thornton and Hoffman [1], the creation of defects increases the intrinsic residual stress level. As the size of the affected region increases in the sequence ͗110͘, ͗100͘, ͗111͘,a͗111͘ growth direction induces a lower density of defects and subsequently a decrease of the intrinsic residual stress level. This is in agreement with our experimental observations: when the coating thickness is increasing, a ͗111͘ texture develops and the residual stress level decreases.

Cracking behaviour

The experimental devices used to characterize the cracking behavior of magnetron tungsten coatings are first described; we then report on the results of an investigation of the cracking mechanisms.

The substrate-coating samples are submitted to tensile tests, at a nominal 3.10 Ϫ4 s Ϫ1 strain rate, in a Raith tension loading stage which lies inside the chamber of a scanning electron microscope (SEM). Such a device allows a more precise and realistic investigation of the cracking mechanisms under loading, of the cracking kinetics and of the substrate-coating adhesion. The tests are instrumented with an extensometer device and coupled with an acoustic emission analysis, using a piezo-electric sensor with a 20 kHz filter and a sampling period of 100 ns (Fig. 5), so as to be able to determine the critical strain (±0.01%) for crack initiation and to analyse the cracking kinetics.

Several variables [START_REF] Roget | Techniques de l'inge ´nieur[END_REF] can be used to characterise the acoustic signals V(t), such as their maximum amplitude (V c ), their average amplitude (V m ) or the efficient value (i.e., the root mean square),V R.M.S. of the acoustic signals. The definition of the latter coefficient V R.M.S. is given by: 2 dt T ϭ 10 ms to a few seconds.

V R.M.S. (t) ϭ Ί 1 T ͵ t ϩ T t [V(t)]
(5)

A shape factor, F, can also be used [18]: it is defined as the ratio of the maximum amplitude (V c ) to the efficient value (V R.M.S. ).

The correlation between acoustic emission signals recorded during mechanical tests and crack initiation has first been investigated. In addition, four-point bending tests have been performed inside the S.E.M. chamber (Fig. 6) to allow the observation of the interface between the substrate and the coating under loading. Gold microgrids [19] of 2 µm path have been deposited on the cross-section of the samples in view of a qualitative analysis of the strain field heterogeneity.

Damage analysis

SEM observations show a strong influence of the coating thickness on the cracking behaviour. For thinner coatings (0.6 and 1.6 µm), cracks follow the grain boundaries of the substrate, which are revealed by ion etching (Fig. 7(a)) performed inside the deposition chamber in order to clean the substrate surface and to improve the interface adhesion. Similar observations have been made on 3 µm thick TiN coatings deposited on a steel substrate [20].

For thicker coatings (6, 13.9, 30 µm), cracks are straight and perpendicular to the tensile axis (Fig. 7(b)). This mode I cracking behaviour of PVD coatings is frequently observed under bending and tensile loadings [20][21].

No debonding has been observed at the interface but strain localisation develops in the substrate at the crack tips, as revealed during a four-point bending test, by the deformation field of a gold microgrid deposited on the crack tip area of a 6 µm thick coating (Fig. 8).

The microgrid is clearly deformed close to the crack tip (Fig. 8) and plastic strain mechanisms appear to be active in the substrate inside deformation bands inclined by 45°with respect to the substrate-coating interface. At a 10-14 µm distance of the crack, the microgrid is more homogeneously deformed and the strain level is lower than in the crack tip zone (Fig. 8).

Acoustic emission analysis

The influence of the substrate yield stress is illustrated in Fig. 9 by the responses of a similar 13.9 µm thick coating deposited either on type 1 or type 2 substrates. The black square points correspond to (s, e) values associated with acoustic signals detected during the test. The acoustic signals are characterised by their F ratio, which can be used as an acoustic signature of the cracks. As this ratio F is found to be constant during the tensile test (Fig. 10), it can be concluded that one cracking mechanism only is active throughout the test. We can infer from this result that no interface debond-Fig. 8. Microgrid deformation close to the crack during a four-point bending test on a 6 µm thick coating with a type 1 substrate. Fig. 9. Global responses of a W PVD coating sample (E-F, t f =13.9 µm) deposited on substrates 1 and 2: characteristic curve of the W coating/steel samples for a thickness larger than 6 µm. ̅:E-W/substrate 1;-F-W/substrate 2; :( s, e) values associated with acoustic emission. Fig. 10. Amplitude vs. V r.m.s. plots for a 31 µm thick PVD coating. This curve is characteristic of the W coating/steel systems under tensile loading for a coating thickness larger than 6 µm.

ing occurs. This conclusion is corroborated by direct SEM observations.

Intrinsic critical cracking stress and energy release rate

Approaches

The experimental results of the tensile tests have been reported in Table 4 for all the samples with mention of their thickness, their residual stresses and the substrate type. It can be noticed (Table 4) that the strain e s xx,c associated with cracking initiation depends on the coating thickness, so that it cannot be considered as an intrinsic damage parameter for tungsten coatings. Since it is likely to depend on the residual stress level, the associated critical stress parameter can be thought to lead to a more relevant cracking criterion.

In order to check this point, we consider a coating-substrate perfectly bound system, subjected to uniaxial tension along the x axis (Fig. 5). Both materials are assumed to be elastic, homogeneous and isotropic; the coating is submitted to an isotropic plane residual stress state. Since the coating is much thinner than the substrate, stresses can be assumed as uniform throughout the coating thickness with s iz =0. Using the continuity conditions on the displacements and tensions across the interface, we easily obtain [22]:

⇒ | s f xx ϭ 1Ϫn f n s 1Ϫn f 2 E f •e s xx ϩ s f r s f yy ϭ n f Ϫn s 1Ϫn f 2 E f •e s xx ϩ s f r , (6)
where indices f and s refer to the film and the substrate, respectively, and r stands for "residual".A s the Poisson ratios n f and n s are almost equal (n f =0.28 and n s =0.29), eq. ( 6) simplify into

⇒|s f xx ϷE f •e s xx ϩ s f r ; s f yy Ϸs f r . ( 7
)
So the cracking initiation can be characterised by the critical stress s f xx,c , associated with the critical strain e s xx,c through the relation:

s f xx,c ϷE f •e s xx,c ϩ s f r . ( 8
)
The energy release rate G for the crack extension through the film can be determined for all coatingsubstrate systems from the Hu and Evans approach [4]. The derivation assumes an elastic behaviour of both the substrate and the coating but considers a plastic yielding zone at the crack tip in the substrate. The interface between the coating and the substrate is considered as perfect. This approach, described in [4], is based on an energy balance of a strained system when the coating is cracking. It leads to the following expression for G:

G ϭ (s f xx,c ) 2 •t f E f ͫ p•F ͩ E s E f ͪ ϩ s f xx,c 3•t s y ͬ , (9)
where F ϭ (E s / E f ) is a function of the elastic contrast [4] and t s y is the shear yield stress of the substrate. For our systems, F is equal to 0.71.

Results

The results corresponding to the fracture properties of tungsten coatings are presented in Table 4. Except for very thin coatings (samples A, B and C), the resulting s f xx,c values are found to be remarkably close to each other (s f xx,c Ϸ275 ± 50 MPa) whatever the coating thickness and the substrate yield stress (Table 4), so that they can be considered as defining an intrinsic critical cracking stress. This value is in good agreement with previous results derived from a 3-point bending test for PVD tungsten coatings deposited on a steel substrate [7]. Nevertheless, our experimental investigation, which has been performed under homogeneous loading conditions, leads to a more accurate determination of this parameter and to a more conclusive proof of its intrinsic nature, especially through the independence of the critical measured stress with respect to the processing conditions. For samples A to C, whose released energy associated to cracking seems to be very low, the used acoustic sensor was not sensitive enough to detect the first cracking events.

The associated energy release rate G values range from 3.4 to 17.4 J/m 2 . They are in agreement with those determined by Harry with the same approach. A dependence of G on the coating thickness can be observed since G is proportional to t f (see eq. ( 9)).

Discussion

A first observation can be made on the measurement uncertainty. Although the determination of the critical strain is quite precise (10 Ϫ4 ), it induces a precision of the order of 18% for the critical stress and 30% for the energy release rate. Despite this fact, the order of magnitude of the obtained values for s f xx,c and G can be considered and discussed.

Even if the brittle behaviour of tungsten coatings is qualitatively consistent with the one of the bulk material obtained from tensile loading at 20°C [23], there is a great difference on the level of fracture stress between our experimental results and those reported in literature. According to [24], the fracture stress is in the order of 560 MPa for coarse tungsten and of 3900 MPa for a 20 µm diameter drawn wire. Moreover, from Farre [23], sintered tungsten has a fracture stress of 1000 MPa and a cleavage stress of 2100 MPa. These values of intragranular fracture stress are much greater than ours: this difference suggests an intergranular fracture mechanism for our coatings. Moreover, as the cleavage stress increases with the decrease of the grain size, the intergranular fracture is all the more favourable because our coatings have a columnar microstructure with a grain size in the order of 10 to 100 nm (Fig. 11).

On the other side, G is defined as the available energy of the crack extension through the coating per surface unit. So a crack develops if G is larger than or equal to twice the surface free energy value (2g c ). This value, which is independent of the coating thickness, is then overestimated by our experi- mental determination, which is concerned with the thinnest coating exhibiting mode I cracks (6 µm).

In the case of cleavage fracture of tungsten, Miannay [START_REF] Miannay | Me ´canique de la rupture[END_REF] has shown that the theoretical fracture energy (2g c ) is equal to 6.3, 9.2, 16.3 J/m 2 for (110), (100) and (111) planes, respectively. In addition, for b.c.c. materials, the cleavage planes are not the (110) (lowest energy) but the (100) planes. To explain this effect, Cottrell has proposed a mechanism based on the self-locking of slip systems which is responsible for strong interactions between two (110) dislocation systems and for the formation of locks: this results in an increase of the fracture energy with regard to the theoretical values [START_REF] Franc ¸ois | Viscoplasticity, damage, fracture and contact mechanisms[END_REF]. Consequently, whereas our experimental values of G have been overestimated, the theoretical predictions can be considered as underestimated. Nevertheless, our values are lower than the theoretical ones by a factor of 2.7, which is certainly underestimated. Since this difference is much larger than the experimental uncertainty of our G measurements (30%), it can be concluded that the energy approach also suggests a mechanism of intergranular fracture of our PVD tungsten coatings, associated with their specific small grained columnar microstructure which is not expected in bulk tungsten specimens. This fracture mode is likely to be strongly favoured by the large specific area of grain boundaries in PVD coatings.

Analysis of the minimum spacing between cracks

The crack density, defined as the number of observed mode I cracks per unit sample length, rapidly increases with strain and then slowly tends to a saturation value. This maximum crack density is known to be decreasing with the coating thickness [7], but no experimental information is available in literature on the influence of the substrate yield stress s y on this saturation density. Since the minimum average spacing between two neighbour cracks (l exp ) is inversely proportional to the maximum crack density, the l exp values can be usefully compared for different coating thickness values and different substrates (Table 5).

The average minimum spacing between cracks is found to increase with the coating thickness for a given substrate and to decrease with the substrate yield stress for a given coating (same thickness and residual stress level).

These effects can be analysed according to the Hu-Evans model [4] whose assumptions have been given in Section 4.4.1. By use of a shear lag-type treatment with a linear redistribution of the uniaxial tensile stress (s f xx ) in an uncracked coating portion on a distance d from the crack (Fig. 12) and Fig. 12. Linear stress redistribution in a coating close to a crack [4]. assuming a perfectly plastic Von Mises behaviour of the substrate at the crack tip, we obtain:

t y t f ϭ s f xx x with t y ϭ s y ͱ3
and for xՆd, (10)

t y t f ϭ s f xx,c d
where x is the distance from the crack tip, t y is the substrate yield shear stress and s f xx,c is the critical cracking stress of the coating. The minimum spacing l between two cracks can be bounded by d (because the cracking criterion cannot be satisfied below) and 2d (because this criterion is surely satisfied beyond):

d Ͻ l Ͻ 2d. ( 11
)
Consequently, we get from (10) and (11):

ͱ3 Ͻ l•s y s f xx,c •t f Ͻ 2ͱ3. ( 12
)
So, l is decreasing with the substrate yield stress and increasing with the coating thickness, as observed experimentally.

The yield shear stress t y of the substrates is evaluated from the Von Mises criterion (t y ϭ s y / √3): it is equal to 635 MPa and 460 MPa for the substrates 1 and 2, respectively. The l theoretical value can be estimated from the intrinsic cracking stress and compared to the l exp experimental value (Table 6).

The theoretical values are significantly lower than the experimental ones. This can be considered as the consequence of the main assumption of this approach, namely the substrate strain uniformity under the coating. This assumption conflicts with what can be observed on our samples: the microgrids technique shows (Fig. 8) that the deformation field suffers a marked localisation in the substrate close to the crack tips. Consequently, although Hu-Evans's approach offers the advantage of emphasising the important role of local plasticity at the crack tips in the substrate, it strongly underestimates the strain heterogeneity induced by the cracking development and its influence on the saturation crack density value.

Conclusion

Tensile tests coupled with an acoustic emission analysis and a microextensometry technique have been used to investigate the cracking behaviour of W PVD coatings deposited on steel substrates. Taking account of the residual stresses field in the coatings, as it results from the processing conditions, an intrinsic critical cracking stress, of the order of 275±50 MPa, has been defined. This value is independent both of the coating thickness and the substrate yield stress. The low level of this intrinsic critical stress and of the associated energy release rate compared with those of bulk tungsten suggest an intergranular fracture mechanism of our PVD coatings. This fracture mode is all the more favourable because the microstructure of coatings is small grained and columnar with an important specific area of grain boundaries. The maximum crack density is decreasing with the coating thickness and increasing with the substrate yield stress. This effect can be explained qualitatively through a shear lag-type analysis, including plastic yielding of the substrate at the crack tips, as proposed by Hu and Evans [4]. Nevertheless, the development of strain localisation modes from the crack tips, deduced from the experimental analysis of the strain field in the substrate close to the interface, makes the main assumption of Hu-Evans's approach more and more inadequate as the overall strain is increasing. This strain localisation effect should be more pronounced when the coating thickness is increasing and the substrate yield stress is decreasing, which would lead to a satisfying interpretation of our experimental results.

More systematic experimental investigations are needed for the analysis of strain localisation phenomena in the substrate and for the quantitative description of their influence on the cracking kinetics of the coatings. They are in progress, in parallel with associated numerical simulations.

for microgrids deposition and microextensometry developments. DGA is gratefully acknowledged for financial support.

Appendix A

To generate a residual stress state in the studied structure (coating-substrate system with different thermal expansion coefficients), we can introduce an adequate temperature field without external loading. We aim at reproducing the homogeneous biaxial (plane) residual stress state in the coating determined experimentally. Due to the symmetry of the problem (Fig. 13), one quarter of the sample only can be studied. Faces 1a and 3 are symmetry planes and the other faces remain free. First, we apply a uniform temperature variation T 1 →T 0 (with T 1 ϾT 0 ) to an elastic substrate-coating portion (Step 1). This situation is not yet representative of the experimental conditions since, for a tensile specimen, the central part of the specimen is continued by coated heads. Therefore Face 1b is not a free surface in the actual specimen. Consequently, we superimpose on Face 1b an axial displacement field opposed to the one resulting from the computation of the first numerical Step. This displacement field is not necessarily linear and depends on the ratio of the coating thickness to the substrate thickness and on the temperature variation applied in Step 1.

So, this new boundary condition permits a realistic description of the central part of the sample without modelling the entire tensile specimen. The other faces remain free except on Face 1a and 3 (symmetry conditions) (Step 2, Fig. 15). The numerical computation was performed with the finite element software Abaqus ® under the assumption of a perfect coating-substrate interface. Tungsten coating and steel substrate are considered as elastic and isotropic with temperature-independent moduli. These thermal and mechanical properties are reported in Table 1. The 3D mesh was realised through 16 000 elements with 8 nodes and 8 integration points.

After the thermal computation (Step 1), the stress state of the coating is compressive and it is not homogeneous in the 1 direction. Moreover, the substrate is submitted to a stress gradient with a tensile stress state close to the interface and a compressive stress state beyond (Fig. 14).

After the Step 2 loading on Face 1b (Fig. 15), the computed stress state is almost homogeneous in the coating (Figs. 16 and 17). This stress state is representative of the experimentally measured one (Figs 16 and18) and the stress gradient is maintained in the substrate as illustrated in Fig. 17 Note that the s 33 stress component does not change between Steps 1 and 2, and that the s 33 stress gradient in the substrate thickness is of the same order of magnitude as the s 11 stress gradient obtained after the thermal computation (Fig. 18).

Fig. 1 .

 1 Fig. 1. Variation of the coating texture coefficient versus the thickness.

Fig. 3 .

 3 Fig. 3. (e fy ,sin 2 y) plots for different thickness values of PVD coatings.

Fig. 4 .

 4 Fig. 4. (e fy ,sin 2 y) plots for different angles f for the 1.6 thick PVD W coatings.

Fig. 5 .

 5 Fig. 5. Experimental tensile device used to study the cracking behaviour of coatings.

Fig. 6 .

 6 Fig. 6. Four point bending sample.

Fig. 7 .

 7 Fig. 7. Damage mechanisms observed on the coating surface (SEM images)-(a) t f =0.6 µm, e=3%, Sample B; (b) t f =13.9 µm, e=5.3%, Sample E, mechanisms observed for a thickness larger than 6 µm.

Fig. 11 .

 11 Fig. 11. SEM photograph of the columnar microstructure of 30 µm thick PVD tungsten coating.

Fig. 13 .

 13 Fig. 13. Boundary conditions of the thermal-mechanical computation and geometry of the substrate-coating portion.

Fig. 14 .

 14 Fig. 14. Distribution of the s 11 stress (Pa), step 1 (thermal-mechanical computation).

Fig. 15 .

 15 Fig. 15. Step 2, boundary conditions.

Fig. 16 .

 16 Fig. 16. Distribution of the s 11 stress (Pa), step 2.

Fig. 17 .

 17 Fig. 17. s 11 stress profile in the sample thickness, step 2.

  .

Fig. 18 .

 18 Fig. 18. Distribution of the s 33 stress (Pa), step 2.

Table 1

 1 Mechanical properties and thermal expansion coefficients of PVD tungsten coatings and 35NCDV12 steel substrates

		E (GPa)	na (10 Ϫ6 K Ϫ1 )	Yield stress, s y (MPa)
	PVD W	410	0.28	4.6	-
	Substrate type 1	200	0.29	12.3	1100
	Substrate type 2	200	0.29	12.3	800
	Table 2
	Sample characteristics
	Sample	Coating Thickness (µm)		Elaboration conditions	Substrate
	A	0.6		A		Type 1
	B	1.6		A		Type 1
	C	1.6		A		Type 1
	D	6		A		Type 1
	E	13.9		A		Type 1
	F	13.9		A		Type 2
	G	31.1		A		Type 1
	H	30.9		A		Type 1
	I	30.1		A		Type 2
	J	30.4		B		Type 1
	K	30.2		B		Type 2

12 sin2f ϩ s 22 sin 2 fϪs 33 )sin 2 y ϩ

		ϩ s 22 ϩ s 33 ) ϩ	1 ϩ n E (s 13 cosf
		ϩ s 23 sinf)sin2y.
		In addition, if we assume an isotropic residual plane stress state (s i3 [i={1,2,3}]=s 12 =0 and
		s
	1 ϩ n E s 33 Ϫ	n E (s 11 (3)
		axes
	(X e 1 ,X e 2 ,X e 3 ).

fy ϭ 1 ϩ n E (s 11 cos 2 f ϩ s Fig. 2. Definition of y, f, and q angles in the sample 11 =s 22 =s), eq. (

Table 3

 3 Residual stresses of PVD W coatings determined by X-ray diffraction with the assumptions of isotropic plane residual stresses and s 33 =0

	Sample	Thickness (µm) (Assumption: isotropic plane stress) s	(Assumption: s 33 =0) (MPa)
			(MPa)
	B D6 1.6 G3 1 J3 0	Ϫ2460±30 Ϫ2390±50 Ϫ1590±30 -	s 11 =Ϫ2443±60 s 22 =Ϫ2507±30 s 11 =Ϫ2374±40 s 22 =Ϫ2462±45 s 11 =Ϫ1602±55 s 22 =Ϫ1450±55 s 11 =Ϫ3497±83 s 22 =Ϫ3385±83	s 12 =s 13 =s 23 =0±50 s 12 =s 13 =s 23 =0±50 s 12 =s 13 =s 23 =0±50 s 12 =s 13 =s 23 =0±50

Table 4

 4 Values of residual stresses (s f r ), e s xx,c , s f xx,appl , s f xx,c and G for different coating thickness values and substrates (the residual stresses derive from previous measurements; see Section 3.2)

	Substrate	Coating	s f r (MPa)	e s xx,c (%)(±0.01%)	s f xx,appl	s f xx,c (MPa)	G (J/m 2 )
	s y (MPa)	thickness (µm)			(MPa)
	A 1100	0.6	Ϫ2560	0.88%ϾeϾ0.55%		/	/
	B 1100	1.6	Ϫ2460	0.88%Ͼe Ͼ0.55%		/	/
	C 1100	1.6	Ϫ2450	0.68%Ͼe Ͼ0.66%		250-330±50	/
	D 1100	6	Ϫ2390	0.66%	2700	310±50	3.4±1.0
	E 1100	13.9	Ϫ1670	0.47%	1930	260±50	5.2±1.8
	G 1100	31.1	Ϫ1600	0.46%	1890	290±50	15.5±4.8
	H 1100	30.9	Ϫ1500	0.43%	1765	265±50	12.0±4.2
	J 1100	30.2	Ϫ3500	0.92%	3770	270±50	12.6±4.3
	F 800	13.9	Ϫ1680	0.47%	1930	250±50	3.8±1.4
	I 800	30.4	Ϫ1550	0.44%	1810	260±50	11.3±4.1
	K 800	30.2	Ϫ3500	0.93%	3810	310±50	17.4±5.1

Table 5

 5 Average minimum spacing between two consecutive cracks (l exp ) for different coating thickness values and different substrates

	Coating thickness (µm)	Substrate yield stress (MPa)	l exp (µm)	Processing conditions
	6	1100	62	A
	14	1100	77	A
	14	800	108	A
	31	1100	111	A
	30	800	140	A

Table 6

 6

	Comparison between experimental values (l exp ) and theoretical values (l)
	Coating thickness (µm)	Substrate yield stress (MPa)	l exp (µm)	l (µm)
	6	1100	62	2.6ϽlϽ5.2
	14	1100	77	6.0ϽlϽ12.0
	14	800	108	8.3ϽlϽ16.6
	31	1100	111	12.95ϽlϽ25.9
	30	800	140	17.9ϽlϽ35.8

Acknowledgements

The authors wish to thank M. Farges (DCE/CTA) for coating elaboration, M. Le Cornec (DCE/CTA) for texture and residual stresses determination, M. Terrien (LMS) for his assistance to the acoustic emission analysis and D. Caldemaison (LMS), M. Bornert (LMS) and P. Doumalin (LMS)


Manon Cassier
email: manon.cassier@u-cergy.fr

Zied Sellami
email: zsellami@linagora.com

Jean-Pierre Lorré
email: jplorre@linagora.com

Meeting Intents Detection Based on Ontology for Automatic Email Answering

Keywords: Ontology engineering, knowledge acquisition from text, knowledge-based recommendation systems

Automatic email answering is a difficult AI problem that combines classification, natural language understanding and text generation techniques. We present an original approach and a tool based on an ontology to automatically reply to meeting emails. We constructed the ontology from a French corpus of 1150 emails in which the concepts represent detailed meeting intents (proposing a meeting, cancelling a meeting, rescheduling a meeting) and different answer templates. Each intent concept is a semantic rule formalized according to the FrameNet methodology. These rules are used to detect intents in emails and also to extract relevant information (such as date, time or person) used for generating replies. The main advantage of our approach is the generation of more precise answers than those proposed by other approaches. We tested the intent detection step on a set of 297 emails and compared it with different supervised machine learning algorithms. Obtained results are encouraging, with an accuracy 20% higher than results obtained with other algorithms.

Introduction

Automatic email answering is an interesting feature in a business context. According to the Radicati Group 2017 report1 an employee receives in a day 88 emails and sends 34. Email management would represent between 5 and 10 hours of an employee's time over a month. A too massive use of emails can heavily impact work productivity by causing information overload. Automatic reply to emails therefore represents a considerable challenge.

Our work fits in a larger project to design an Open-Source collaborative platform for businesses called OpenPaaS2 including a mailbox, an enterprise social network and a shared agenda. Considering the problem of email management, our goal is to propose an email processing assistant that would be able to assist the user by automatically prioritizing emails, sending notifications when urgent emails are received and generating answers.

In this paper, we present our approach for automatic email answering based on an ontology to automatically detect meeting intents in emails and to generate appropriate answers using text pattern matching and named entities detection.

The paper is organized as follows. In Section 2, we review related work. In Section 3, we detail our approach by describing the ontology and how it is used for detecting meeting intents and for generating answers. In Section 4, we compare our system to other intents detection techniques and we test the quality of the generated answers. In Section 5 we discuss the limits and possible improvements of our approach. Section 6 concludes.

The problem of automatic emails answering has been studied frequently in the last ten years. [START_REF] Katakis | Email mining: Emerging techniques for email management[END_REF]) offer a complete view of the different approaches frequently used for email classification, automatic email summary generation or email answering. Automatically answering to an email requires two steps: detection of intents to find those that require an answer and the generation of the answer according to the detected intent.

These steps were mainly studied through different approaches, exploiting either machine learning algorithms or text-pattern matching. [START_REF] Malik R | Automatically selecting answer templates to respond to customer emails[END_REF] use key phrase extraction and text similarity to perform email classification. A Naïve Bayes model automatically detects key phrases of length up to 3 words in the incoming email and maps them with question-answers pairs already identified to choose which one is similar. [START_REF] Kannan | Smart reply: Automated response suggestion for email[END_REF] propose Smart Reply for Inbox by Gmail (Google) by using recurrent neural networks (RNNs) as long short-term memory (LSTM) networks to predict the most likely responses for an incoming message. Most of the answers proposed by this system are appropriate for informal context -for instance for yes/no questions. However, these answers do not correspond to the precise response model we expect for professional use. Moreover, these types of approaches require access to a considerable body of data that we do not have.

We believe that symbolic approaches can describe more precisely the elements that the answers must contain. [START_REF] Carvalho | Modeling Intention in Email[END_REF] introduces the notion of Email acts inspired by the Speech Act Theory of [START_REF] Austin | How to do things with words: The William James Lectures delivered at Harvard University in[END_REF] and [START_REF] Searle | Speech Acts: An Essay in the Philosophy of Language[END_REF]. These acts are described as "nounverb pairs that express typical intentions in email communication -for instance, to request for information, to commit to perform a task or to propose a meeting". [START_REF] Carvalho | Modeling Intention in Email[END_REF] proposes a taxonomy of these "Email Speech Acts" (i.e. "intents" or "intentions") that are associated with some verbs or nouns. This taxonomy contains only general concepts3 , which are not enough to describe the phenomena that we wish to process in emails. In our approach, we focus on the meet act which may itself be divided into several sub-intents. [START_REF] Sneiders | Automated email answering by text pattern matching[END_REF] and [START_REF] Kosseim | Using information extraction and natural language generation to answer e-mail[END_REF] propose two question-answering approaches based on symbolic rules to answer emails. These approaches use pattern matching to find specific questions in emails. For each question, a set of standard answers are defined and used on generating a draft reply. In our work, we want to detect answers, affirmations, notifications, assertions, and so on. We want to model a more complex structure than regular expressions. We use FrameNet4 to formalize semantic frames to detect intents on emails.

The FrameNet project [START_REF] Ruppenhofer | Framenet ii: Extended theory and practice[END_REF] gives a formalization of semantic frames as described in Charles J. Fillmore's works [START_REF] Fillmore | Frame semantics and the nature of language[END_REF]. Each semantic frame is represented by one or more frame elements (FEs) that are evocated by words called lexical units (LUs). These frames are only checked if the frame elements are actually present in a sentence.

For example, in case of proposing an appointment by email, the semantic frame appointment proposal in the sentence "Je vous propose de faire une réunion jeudi prochain" (I suggest we meet next Thursday) can be detected thanks to the lexical units "propose" and "réunion" and the frame elements SPEAKER (i.e. "Je"), PROPOSITION (i.e. "propose") and TIME (i.e. "jeudi prochain").

Our contribution consists on building a domain ontology from a French corpus of corporate emails based on FrameNet formalization. This ontology is used to extract meeting intents from emails and for automatic emails answering.

Ontology-based Email Answering

We manually built an OWL ontology5 from a large corpus of 30000 business emails6 -empty, English or social network notification emails were removed. The OWL file of the ontology is available on the project github repository7 .

We used the TXM concordancer8 to explore emails and quickly retrieve those containing meeting intents. This step also allowed us to extract two sets of respectively 1150 annotated emails for the ontology building (with 458 emails containing at least a meeting intent and 692 without any meeting intent) and 177 annotated emails (with 143 emails containing meeting intents) for a subsequent evaluation step.

The first level of the ontology contains 3 core concepts: the Core Intent that models meeting intents, the Core Answer concept that models answer patterns for intents and the Frame Element Concept that models frame elements classes (e.g Time, Date, Address, Person).

Meeting Intent Concepts

We identified 18 specific meeting intent concepts organized on 3 main concepts (Request, Proposal and Notification) as presented in table 1.

Each intent concept contains lexical units and frame elements (which are Annotation Properties in the ontology) that allow detecting a specific intent in an email. A lexical unit is defined with a String value (lemma form), a part-of-speech tag (e.g. VERB, NOUN, ADJ) or a regular expression (EXPR) and an Annotation Value (Mandatory or Optional). A frame element is defined with a Frame Element Class resource (these are generally what we detect as named entities) or a Frame Element Individual resource -which are defined by a regular expression containing words, expressions or characters that evocate them -and an Annotation Value (Mandatory or Optional). For instance, the request concept contains the frame element INTERROGATION -represented by a regular expression containing the strings "?", "est-ce que", "quel", etc. (i.e. the English WH questions words equivalents) -and the lexical units "demander" (ask for), "est-ce possible" (is it possible), "(je|nous) souhait(e|ons)" (I|we wish), etc. The Annotation Value determines if a lexical unit or a frame element is necessary or not for identifying the intent on the email (i.e. if the element is specific to the current intent or not). This information helps to compute a score when using the ontology to automatically annotate emails.

Table 1: Description of the core intents of the meet act ontology.

Request

The request concept includes both requests for information and request addressed to the recipient to perform some activity related to an appointment. It contains 7 intents: request an appointment cancellation, request an appointment confirmation, request to schedule an appointment, request an appointment change, request details about an appointment, request availabilities for an appointment and request a participation to an appointment.

Proposal

The propose concept includes proposals addressed to the recipient to do something or to take part in something related to an appointment. It contains 3 intents: propose an appointment, propose an appointment cancellation and propose an appointment change.

Notification

The notification concept includes all the messages that just observe a fact about an appointment. It contains 8 intents: availability confirmation, appointment confirmation, appointment cancellation notification, unavailability notification, availability notification, appointment reminder notification, precisions about an appointment and appointment change notification.

Shared frame elements and lexical units are described in the 3 main concepts so that subconcepts that represent intents inherit them. For example, the frame element Interrogation is transmitted to all intents related to the main concept request differentiating them from intents related to other concepts propose and notification.

A higher concept called meeting containing only lexical units that refer to an appointment (e.g. "entretien", "RDV", "reunion") is used to filter the incoming emails and detect only those containing sentences related to an appointment.

The ontology is enriched by non-hierarchical relations between intents (called implication relation) that model associations of intentions within a same email. For example, in the sentence "Je ne suis pas disponible demain, pouvons-nous décaler l'entretien ?" (I'm not available tomorrow, can we change the time of my interview), unavailability notification intent implies an appointment change request.

Answer Pattern Concepts

For each meeting intent, we modeled in the ontology a set of answer templates (patterns). These templates were determined by analyzing the responses that people give to each type of appointment email. An answer can be shared between multiple intents.

For example, a template for accepting meeting proposal is: « D'accord pour {[DET;values=le,la,l';default=le] [Appointment_Intent;default=rendez-vous]} {[Time]} {[DET;values=avec;default=avec] [Person;default=]}. ». In this template, Time, Person and Appointment_Intent will be changed with corresponding values recovered from the sentence in the email that instantiated the accepting meeting proposal intent. Note that our system will be able to use the calendar module to check when the person is available for an appointment. This information will be also used to generate a more precise answer.

The System

Our system is integrated to the OpenPaaS UnifiedInbox module (Figure 2) that communicates with our answers suggestion service through a REST API. The system is open-source and available on github9 . For each new email, a JSON request is sent to the Web Service module with the text of the email and other metadata (sender, recipients, date). The system analyzes the query and returns a JSON response with answer suggestions as presented in Figure 3. Our system has four main modules:

1. An Ontology Manager that uses the Apache Jena API to parse and load the ontology in a Java object.

IC 2019

2. An Intent Detector that integrates the CoreNLP 10 (Manning et al., 2014) tool which makes tokenization, POS tagging and named entities detection on the text of the email. It exploits at the same time the Duckling Facebook 11 tool that is more successful for the detection of encrypted named entities. When two different named entities are proposed, a resolver automatically selects the proposal made by Duckling Facebook. The text of the email and the lexical units contained in the ontology are then completely stemmed 12 by the Snowball Stemmer 13 to allow easier matching of forms with text shapes. The module retrieves the information available in the ontology and makes a projection of the lexical units of the higher intent meeting on the text of the email to check if the current email contains meeting intents or not. This projection is made on the entire message thread to which the target email belongs to avoid missing emails that do not contain the word "rendez-vous" (appointment) or a synonym but do contain an intention. For example, it allows conserving the sentence "Oui, je suis disponible" (Yes, I'm available) answering to "Êtes-vous disponible demain pour un rendez-vous ?" (Are you available tomorrow for a meeting?). If no intent is detected, the system does not propose any intent and the Web Service returns the message meeting intent not detected. Otherwise, it makes a projection of the other lexical units and frame elements only on the sentences of the incoming email (without taking the message thread or the order of appearance of LUs and FEs in account) and computes a score by assigning a higher weight to units marked as mandatory in the ontology to select the three more likely intents -after several experiments, we also chose to give a higher score to lexical units than frame elements. Finally, the module computes an annotation score (described below) to each intent detected thanks to the matching step.

3. An Answering Generator that generates an answer for each intent detected. This module integrates an email generator that recovers the answers templates associated to detected intents. It adds a greeting according to the time -for example, "Bonsoir" (Good evening) after 8:00 pm -and a closing formula as "Cordialement" (Sincerely). It computes a score for each answer, corresponding to the sum of the instances scores of each intention in the email. This module will also exploit the user's calendar to check its availabilities and complete the Time, Person and Appointment_Intent tags in the answers templates.

4. A Web Service that returns the three first associated answers in a JSON format.

The annotation score is computed after a filtering of the matching results. The system only keeps concepts of the ontology lowest level (i.e. the intents) that match at least two instances of lexical units or frame elements and of which at least one of the lexical units matched is mandatory.

The score assigned to an intention corresponds to its relevance value (1) in relation to the sentence studied. It corresponds to the sum of its annotation value (2) and its specificity value (3) as described below.

Relevance value = Annotation value + Specif icity value

(1) The choice of a stemmer is motivated by the fact that we did not find an open source lemmatizer effective enough for French.

13 SnowballStemmer:https://snowballstem.org/ Specif icity value = Ratio 1 + Ratio 2 + Ratio 3 + Ratio 4

(3)

Ratio 1 = [ 1 nbM andatoryLU ] if the mandatory LU is instanciated [ -1 nbM andatoryLU ] if the mandatory LU is not instanciated (4) Ratio 2 = [ 1 nbM andatoryF E ] if the mandatory F E is instanciated [ -1 nbM andatoryF E ] if the mandatory F E is not instanciated (5) Ratio 3 = [ 1 nbOptionalLU ] if an optional LU is instanciated (6) Ratio 4 = [ 1 nbOptionalF E ] if an optional F E is instanciated (7)
The parameters of the annotation value are obtained empirically. An instance of an intent is correct if it is instantiated with mandatory and specific lexical units or frame elements.

The specificity value is obtained by calculating the ratio of frame elements and lexical units. This formula makes it possible to estimate the level of coverage of the instance in relation to the properties of its class.

We detail next how these scores are calculated for an intent of request availabilities for an appointment in the sentence "Pouvez-vous m'indiquer vos disponibilités pour planifier un rendez-vous?" (Please let met know when you're available). The different parameters used to compute the annotation value of the sentence are:

• Number of Instances = 4 ("pouvez-vous", "disponibilités", "planifier" and "rendezvous")

• Specific FE = 0

• Specific LU = 2 ("disponibilités" and "planifier")

• Specific mandatory instances = 1 ("disponibilités")

• Inherited mandatory instances = 2 ("pouvez-vous" (request intent) and "rendez-vous" (meeting intent))

Annotation value = 4 + 4 * (2 + 1) + 2 = 18 The request availabilities for an appointment contains 14 mandatory LU, 4 optional LU, 1 mandatory FE and 1 optional FE. We calculate the specificity value with the results of the 4 ratios.

Ratio 1 = 1 14 + 13 * -1 14 Ratio 2 = -1 1 = -1 Ratio 3 = 1 4 = 0.25 Ratio 4 = 0
The specificity value of the intent request availabilities for an appointment for the sentence is:

Specif icity value = -0.86 -1 + 0.25 = -1.61 Finally we get the relevance value of the intent to the sentence:

Relevance value = 18 -1.61 = 16.39

We tested the performance of the system with 177 annotated emails. Each email in the corpus was classified into different folders representing intents. For example, an email containing an intention to confirm an appointment and request details about an appointment was filed in two folders named with the intent title. Thus, the 143 emails that contain intents in our corpus offer a total of 297 occurrences of the different intents.

We evaluated our system according to two criteria: its performance in email classification and the relevance of the answers it proposes. We made the same evaluation with other systems based on machine learning algorithms.

Effectiveness in Intents Detection

To compare our approach to machine learning approaches, we trained machine learning models with the 1150 annotated emails used to build the ontology and we tested them on the 177 emails corpus. We applied a Cross Validation method on predictive models using Logistic Regression, Decision Tree, Random Forest and Naive Bayes algorithms with a Bag of Words approach scored with TF-IDF. The Cross Validation suggests a best model based on Random Forest algorithm for the intent detection task.

We calculated precision, recall and f-score of the two systems based on the first intent proposition and the three intent propositions.

Table 2 and3 show respectively the results of our system and of the machine learning based model. For each evaluation step, our system presents far better results than the machine learning based model. In addition, we notice that our system captures more easily distinctions between the different intents (despite a decrease in the f-score caused by some confusion between intentions that share the same FEs and LUs) while machine learning has difficulties making multiclass distribution.

Table 4 shows the percentage of instances correctly identified by both systems for each intent. We compute these ratios according to the three proposed intents by each system since it is more likely to find the right intention on three proposals.

Our ontology-based system obtains the best results for almost all categories. The low results obtained by the machine learning system for all the categories can be explained by the size of the training corpus. Such a system requires many annotated examples to be able to learn and classify intents on its own. This experience shows instead that our approach is viable even with a small set of data.

The machine learning system obtained the best score on detecting the precisions about an appointment intent. It can be explained by the fact that many emails of the corpus contain this intent. A closer look at the results shows that the system tends to classify almost all emails with this intent. It is therefore logical that the proposed answer is often correct for this case. Anyway, our system seems able to correctly classify most of the intents. We notice that it does not identify the request an appointment change intent that is probably too close to the propose an appointment. In many cases, our system suggests a propose an appointment intent instead of a request an appointment change intent. Moreover, this case is not really a problem as long as given that the two intentions share similar answer templates. We do not combine these two intents into one because the request concept and the proposal concept are semantically different.

Evaluation of the Relevance of the Proposed Answers

We compared the relevance of the answers proposed by our system and by Google's Smart-Reply [START_REF] Kannan | Smart reply: Automated response suggestion for email[END_REF] -that uses Deep Learning and LSTM neural networks to propose 3 answers -for 15 emails. The relevance of the answers was evaluated by 26 Linagora employees through a questionnaire.

For each email, we asked annotators to choose between two blocks of answers -the Google's one and ours -the one that they felt contained the most relevant answer. When none of the proposed answers were relevant to the email submitted, annotators could select an option "no relevant answer". However, we did not allow annotators to select proposals from both systems at the same time even though the proposed answers were all relevant. We wanted to get as close as possible to a real situation where the user of an automatic answering system must necessarily choose one answer between several choices. We did not indicate which answers were proposed by the Google's system or by ours to obtain the most objective results possible.

Table 5 details the obtained results from 26 annotators by explaining the intents contained in each email submitted for annotation. The table shows that the answers obtained by our system are preferred for 8 emails out of 15 with an agreement of more than 75% for 5 emails against 7 emails out of 15 for the Google's system -with an agreement of more than 75% for only 2 emails. On the 12th mail, the same number of annotators chose the answers of the two systems. The total number of annotators' choices for responses from our system (202) is higher than Google's responses (158). We can also notice that our answers are still chosen by more than two annotators in all the cases for which the answers proposed by Smart-Reply are mainly selected by the annotators. In all cases, the answers proposed by our system were chosen by at least one annotator which proves that these answers are grammatically correct. This experience shows that our approach allows us to provide answers that are relevant and acceptable to users. The results show that our answer proposals can fully compete with those proposed by the Google's system. A closer look at the annotators' choices also shows that the user prefers the most complete answers possible -that are proposed by our system. These are particularly chosen when the answer requires a mention of specific elements such as availabilities or precisions about an appointment. Figure 4 illustrates an example of sentence mainly chosen by annotators for an email containing an appointment confirmation request intent.

Advantages, Limitations and Further Research

Automatic email answering is a large studied problem. We propose a new way to resolve this problem by using an ontology to detect intent on emails and answer to them. Unlike machine learning solutions like Google's Smart-Reply our approach does not need large data set to run. Using an ontology is also an effective way to model complex semantic facts from text like meeting intents. Our system proposes answers more precise -thanks to the detection of complex intents -and complete with details on time, date or place of the meeting -thanks to named entities detection. Our approach is generic and new rules can easily be added to the ontology to detect new intents and answer to them. In our opinion, such a system is more controllable and understandable than a machine learning solution.

However, our approach has some limitations since the addition of intent concepts can be costly in time and thought. If the rules described by an intent concept are too precise, the system can easily merge several intents and provide noise. On the contrary, the system risks missing many occurrences and thus generating silence if the rules associate too few lexical units and frame elements.

In addition, the effectiveness of this kind of symbolic rules depends on several factors:

• the domain: for instance, the meeting intents can be detected with the lexical units "table ronde" (a round-table discussion) or "colloque" (a symposium) in an academic setting, but rather with the lexical units "réunion" (a meeting) or "point" (a point) in a professional setting.

• the spelling: as the OpenPaaS mailbox does not integrate a spell checker, some lexical units may not be detected if misspelled. Moreover, it is difficult to manage all variations of declensions without an effective lemmatizer for French, which has many irregular verbs.

• synonym management: the rules must account for as many synonyms as possible to be able to recognize them all. A non-identified synonym cannot be recognized by the system.

We plan to improve our work by enriching the ontology with other intents (recruitment intents, reminder intents, document sharing intents, etc.). We'll improve the generation of email answers by detecting situations that require a formal form of address in emails -for example, when the user must address a hierarchical superior. We also plan to link the answering mechanism to the user's electronic agenda to automatically check availabilities dates and generate answers depending on the user's constraints.

Also, it seems important to us to find a way to automatically manage lexical units synonymy -by using synonym dictionary like WordNet for instance -and to integrate a French lemmatizer to improve the detection of lexical units and frame elements in emails.

Finally, it is possible to adapt the tool to detect intents in the other langages available in CoreNLP (English, German, French, Chinese and Spanish). At the ontology level, the user will have to add the POS properties of the new language and enrich the ontology with LUs and EFs in this language. Currently, the Java code support French and English. To process a new language, we need to integrate in the Intent Detector module the CoreNLP pipeline associated with this new language. The Answering Generator module requires a lemmatizer adapted to the language. It will be necessary to modify it according to the chosen langage.

Conclusions

In this paper, we presented an ontology-based approach for automatic email answering. The ontology was manually built from a corpus of French emails. It models meeting intent concepts using the FrameNet principles and different answer templates for each intent. The automatic email answering system contains four main modules to process the incoming email, spot intents with pattern matching, compute a score according to rules described in the ontology and propose answers.

We evaluated the effectiveness of the intents detection by calculating precision, recall and f-score measurements and compared the results with those obtained by machine-learning approaches. We also evaluated the relevance of the proposed answers by them with those proposed by the Google's Smart-Reply system.

In both cases, we get encouraging results with our system despite a limited annotated corpus. These results show that our approach is a suitable alternative to machine learning techniques (especially DNN) which are very demanding in annotated data.

We plan to enrich the ontology with other kinds of intents. We will improve the answering generation mechanisms by detecting the gender of the recipient and the T-V distinctiona linguistic formality in the French language which refers to "tu" and "vous" usages. A synonym dictionary and a lemmatizer will also be integrated to improve the matching of lexical units and frame elements in emails.

Figure 1 :

 1 Figure 1: A Frame representation of an appointment proposal.

Figure 2 :

 2 Figure 2: Screenshot of an example of answers suggestions in OpenPaaS.

Figure 3 :

 3 Figure 3: General architecture of the automatic email answering system.

Figure 4 :

 4 Figure 4: Sample annotation for an email.

Table 2 :

 2 Precision, recall and f-score obtained by the ontology-based system.

		Precision Recall F-score
	According to the 1st intent	0.50	0.48	0.49
	According to the three intents	0.71	0.67	0.69

Table 3 :

 3 Precision, recall and f-score obtained by the machine learning based model.

		Precision Recall F-score
	According to the 1st intent	0.25	0.17	0.20
	According to the three intents	0.25	0.23	0.24

Table 4 :

 4 Effectiveness of intent detection for each intent type.

	Intents	Number of in-stances to find	Ontology based results	Ontology based ratio	ML based results	ML based ratio
	Request an appointment can-cellation	2	2	100%	0	0%
	Request an appointment con-firmation	22	16	73%	13	59%
	Request to schedule an ap-pointment	8	3	38%	0	0%
	Request an appointment change	3	0	0%	0	0%
	Request details about an ap-pointment	10	9	90%	1	10%
	Request availabilities for an appointment	25	20	80%	6	24%
	Request a participation to an appointment	7	3	43%	0	0%
	Propose an appointment	39	30	77%	29	74%
	Propose an appointment can-cellation	2	1	50%	0	0%
	Propose an appointment change	11	7	64%	0	0%
	Availability confirmation	4	2	50%	2	50%
	Appointment confirmation	8	4	50%	2	25%
	Appointment cancellation no-tification	7	4	57%	0	0%
	Unavailability notification	36	23	64%	6	17%
	Availability notification	3	2	67%	1	33%
	Appointment reminder notifi-cation	25	16	64%	3	12%
	Precisions about an appoint-ment	47	35	75%	42	89%
	Appointment change notifica-tion	4	1	25%	0	0%

Table 5 :

 5 Distribution of the chosen answers for each email.

			Ontology Google's	No
	Email Contained intent	Based	Smart	relevant
			System	Reply	answer
	1	Propose an appointment	15	5	6
		Request availabilities for an appointment
	2	Request a participation to an appointment	5	19	2
	3	Appointment confirmation	7	18	1
	4	Request an appointment confirmation	24	1	1
	5	Propose an appointment	16	8	2
		Request availabilities for an appointment
	6	Propose an appointment cancellation	9	17	0
	7	Precisions about an appointment	4	20	2
	8	Appointment confirmation	5	20	1
		Request an appointment confirmation
	9	Propose an appointment change	13	10	3
	10	Propose an appointment cancellation	20	0	6
	11	Request details about an appointment	21	5	0
	12	Unavailability notification	12	12	2
	13	Propose an appointment	6	17	3
		Availability notification
		Request availabilities for an appointment
	14	Propose an appointment	22	4	0
		Request an appointment confirmation
	15	Appointment cancellation notification	23	2	1
		Total: 202	158	30

Report available at https://www.radicati.com/wp/wp-content/uploads/2017/01/ Email-Statistics-Report-2017-2021-Executive-Summary.pdf

OpenPaaS: https://open-paas.org/

The taxonomy contains the 7 concepts request, propose, deliver, commit, directive, commissive and meet.

The FrameNet Homepage: https://framenet.icsi.berkeley.edu/fndrupal/ (last accessed 2018/06/08.

We build the ontology with the Protégé tool (Stanford Center for Biomedical Informatics Research, 2016): http://protege.stanford.edu/.

The corpus can not be distributed for confidentiality reasons.

https://github.com/openpaas-ng/automatic-email-answering/blob/master/ intent6.owl

TXM Concordancer (Heiden, 2010): http://textometrie.ens-lyon.fr/.

https://github.com/openpaas-ng/automatic-email-answering