a and b are related to the dimensions of the rectangular element. The matrices $K_1, K_2, K_3, K_4$ and L are intermediate matrices in the construction of the stiffness matrix $K$. $D_x, D_z, D_{xz}$ and $D_l$ are elements of the elasticity matrix $D_{xz}$ in the global reference $XZ$ (fig. 3). The matrix $D_{XY}$ is defined for an orthotropic material by Eq. 9 [14]:

$$D_{xz} = \begin{bmatrix} D_x & D_{1,xz} & 0 \\ D_{1,xz} & D_z & 0 \\ 0 & 0 & D_{xz} \end{bmatrix} \qquad (9)$$

The elasticity matrix $D_{lt}$ of the ply in its local frame $lt$ is expressed by Eq. 10. In the case studied, as the fibre orientation angle $\psi$ is zero, the elasticity matrices in the local and global reference frames are identical.

$$D_{lt} = D_{xz} = \begin{bmatrix} D_l & D_{1,lt} & 0 \\ D_{1,lt} & D_t & 0 \\ 0 & 0 & D_{lt} \end{bmatrix} \qquad (10)$$

The components of the elasticity matrix are defined by Eq. 11. These expressions are valid for the case of a laminate of thickness $e$ with all its plies oriented horizontally (Fig. 3).

$$D_l = \frac{E_l}{1 - v_{tl} v_{lt}} \frac{e^3}{12}, \quad D_t = \frac{E_t}{1 - v_{tl} v_{lt}} \frac{e^3}{12}, \quad D_{lt} = G_{lt} \frac{e^3}{12}, \quad D_{1,lt} = \frac{v_{lt} E_l}{1 - v_{tl} v_{lt}} \frac{e^3}{12}, \qquad (11)$$

Cutting force model for composite material: The new mechanistic cutting forces model implemented in the DyStaMill software is the model developed by Mullin et al. [15]. The expression of the cutting force applied in radial (Eq. 12) and tangential (Eq. 13) directions is similar to Altintas' model [16].

$$dF_r(\varphi) = K_{rc}(\beta) h dz + K_{re}(\beta) dS \qquad (12)$$

$$dF_t(\varphi) = K_{tc}(\beta) h dz + K_{te}(\beta) dS \qquad (13)$$

Altintas' model considers two contributions in both directions (radial $r$ and tangential $t$). The first ones are related to friction in the primary shear zone: a function of the radial and tangential cutting force coefficient, respectively $K_{rc}$ and $K_{tc}$, the instantaneous uncut chip thickness $h$ and the height of a tool edge $dz$. The second ones consider the effect of friction in the secondary shear zone and is a function of the radial and tangential edge force coefficient, respectively $K_{re}$ and $K_{te}$, and the local segment $dS$ of the cutting edge. However, cutting ($K_{rc}$ and $K_{tc}$) and edge ($K_{re}$ and $K_{te}$) coefficients vary according to the instantaneous fiber cutting angle $\beta$ (Eq. 14).

$$K_{ij}(\beta) = C_{0}^{ij} + C_{1}^{ij} \cos(2\beta) + S_{1}^{ij} \sin(2\beta) \quad \text{with } i = t, r \text{ and } j = c, e \qquad (14)$$

The method for identifying the coefficients and their values is given in [15]. These values are only valid for the tool/material pair used in [15]: a 2-flute Tungsten Carbide end mill with 9.525 mm (3/8 inch) diameter and 3° normal rake angle and UD-CFRP (alternating 0° and 90° directions).

## Numerical results

The composite used is a UD-CFRP with a “High Resistance” (HR) and an epoxy resin. The mechanical properties of the fibres and the matrix studied are given in Tab. 1.
<table><caption>Table 1: Material properties (fiber and matrix)</caption><thead><tr><th>Property</th><th>Value</th></tr></thead><tbody><tr><td colspan="2">Carbon fiber HR [12]</td></tr><tr><td>Fiber volume V<sub>f</sub> [%]</td><td>60</td></tr><tr><td>Elasticity's modulus in the fibre direction E<sub>f,f</sub> [GPa]</td><td>230</td></tr><tr><td>Elasticity's modulus in the transverse direction E<sub>f,t</sub> [GPa]</td><td>15</td></tr><tr><td>Poisson's ratio v<sub>f</sub> [-]</td><td>0.3</td></tr><tr><td>Fibre shear modulus G<sub>f,lt</sub> [GPa]</td><td>50</td></tr><tr><td colspan="2">Epoxy matrix [17]</td></tr><tr><td>Elasticity's modulus E<sub>m</sub> [GPa]</td><td>4.2</td></tr><tr><td>Poisson's ratio v<sub>m</sub> [-]</td><td>0.34</td></tr><tr><td>Matrix shear modulus G<sub>m</sub> [GPa]</td><td>1.567</td></tr></tbody></table>

The standard thickness of a unidirectional ply of “High Resistance” (HR) carbon fibre is 0.13 mm [12]. The number of stacked plies varies from 20 to 40 plies in steps of 10 plies. The thickness of the plates studied therefore varies from 2.6 to 5.2 mm in steps of 1.3 mm. The dimensions of the plate are 100 x 31.4 mm (L x h). The plate has 50 elements horizontally and 20 elements vertically. The tool used is a 2-flute Tungsten Carbide end mill. The diameter of the mill D<sub>c</sub> is 9.525 mm (3/8 inch). The properties of the plates and the tool studied, are listed in Tab.2.

<table><caption>Table 2: Plate and tool properties</caption><thead><tr><th>Property</th><th>Value</th></tr></thead><tbody><tr><td colspan="2">Plate</td></tr><tr><td>Thickness of a UD-CFRP ply e<sub>p</sub> [mm]</td><td>0.13</td></tr><tr><td>Number of plies n<sub>plies</sub></td><td>20 – 30 – 40</td></tr><tr><td>Thickness of the plate e [mm]</td><td>2.6 – 3.9 – 5.2</td></tr><tr><td>Length of the plate L [mm]</td><td>100</td></tr><tr><td>Height of the plate h [mm]</td><td>31.4</td></tr><tr><td>Number of elements horizontally</td><td>50</td></tr><tr><td>Number of elements vertically</td><td>20</td></tr><tr><td colspan="2">Tool</td></tr><tr><td>Number of flutes</td><td>2</td></tr><tr><td>Cutting diameter of the tool D<sub>c</sub> [mm]</td><td>9.525</td></tr><tr><td>Normal rake angle [°]</td><td>3</td></tr></tbody></table>

In terms of cutting conditions, the axial depth of cut ap was set at 10 mm. The radial depth of cut a<sub>e</sub> is equal to half the plate thickness (variable between 1.3 and 2.6 mm). The cutting speed v<sub>c</sub> is fixed at 5000 rpm. The feed per tooth f<sub>z</sub> is variable between 0.02 and 0.1 mm/th in steps of 0.02 mm/th. These cutting parameters are listed in Tab.3.

<table><caption>Table 3: Cutting parameters</caption><thead><tr><th>Property</th><th>Value</th></tr></thead><tbody><tr><td>Axial depth of cut a<sub>p</sub> [mm]</td><td>10</td></tr><tr><td>Radial depth of cut a<sub>e</sub> [mm]</td><td>1.3 – 1.95 – 2.6</td></tr><tr><td>Cutting speed v<sub>c</sub> [rpm]</td><td>5000</td></tr><tr><td>Feed per tooth f<sub>z</sub> [mm/th]</td><td>0.02 – 0.04 – 0.06 – 0.08 – 0.1</td></tr></tbody></table>
<table><caption>Table 1: Material properties (fiber and matrix)</caption><thead><tr><th>Property</th><th>Value</th></tr></thead><tbody><tr><td colspan="2">Carbon fiber HR [12]</td></tr><tr><td>Fiber volume V<sub>f</sub> [%]</td><td>60</td></tr><tr><td>Elasticity's modulus in the fibre direction E<sub>f,f</sub> [GPa]</td><td>230</td></tr><tr><td>Elasticity's modulus in the transverse direction E<sub>f,t</sub> [GPa]</td><td>15</td></tr><tr><td>Poisson's ratio v<sub>f</sub> [-]</td><td>0.3</td></tr><tr><td>Fibre shear modulus G<sub>f,lt</sub> [GPa]</td><td>50</td></tr><tr><td colspan="2">Epoxy matrix [17]</td></tr><tr><td>Elasticity's modulus E<sub>m</sub> [GPa]</td><td>4.2</td></tr><tr><td>Poisson's ratio v<sub>m</sub> [-]</td><td>0.34</td></tr><tr><td>Matrix shear modulus G<sub>m</sub> [GPa]</td><td>1.567</td></tr></tbody></table>

The standard thickness of a unidirectional ply of “High Resistance” (HR) carbon fibre is 0.13 mm [12]. The number of stacked plies varies from 20 to 40 plies in steps of 10 plies. The thickness of the plates studied therefore varies from 2.6 to 5.2 mm in steps of 1.3 mm. The dimensions of the plate are 100 x 31.4 mm (L x h). The plate has 50 elements horizontally and 20 elements vertically. The tool used is a 2-flute Tungsten Carbide end mill. The diameter of the mill D<sub>c</sub> is 9.525 mm (3/8 inch). The properties of the plates and the tool studied, are listed in Tab.2.

<table><caption>Table 2: Plate and tool properties</caption><thead><tr><th>Property</th><th>Value</th></tr></thead><tbody><tr><td colspan="2">Plate</td></tr><tr><td>Thickness of a UD-CFRP ply e<sub>p</sub> [mm]</td><td>0.13</td></tr><tr><td>Number of plies n<sub>plies</sub></td><td>20 – 30 – 40</td></tr><tr><td>Thickness of the plate e [mm]</td><td>2.6 – 3.9 – 5.2</td></tr><tr><td>Length of the plate L [mm]</td><td>100</td></tr><tr><td>Height of the plate h [mm]</td><td>31.4</td></tr><tr><td>Number of elements horizontally</td><td>50</td></tr><tr><td>Number of elements vertically</td><td>20</td></tr><tr><td colspan="2">Tool</td></tr><tr><td>Number of flutes</td><td>2</td></tr><tr><td>Cutting diameter of the tool D<sub>c</sub> [mm]</td><td>9.525</td></tr><tr><td>Normal rake angle [°]</td><td>3</td></tr></tbody></table>

In terms of cutting conditions, the axial depth of cut ap was set at 10 mm. The radial depth of cut a<sub>e</sub> is equal to half the plate thickness (variable between 1.3 and 2.6 mm). The cutting speed v<sub>c</sub> is fixed at 5000 rpm. The feed per tooth f<sub>z</sub> is variable between 0.02 and 0.1 mm/th in steps of 0.02 mm/th. These cutting parameters are listed in Tab.3.

<table><caption>Table 3: Cutting parameters</caption><thead><tr><th>Property</th><th>Value</th></tr></thead><tbody><tr><td>Axial depth of cut a<sub>p</sub> [mm]</td><td>10</td></tr><tr><td>Radial depth of cut a<sub>e</sub> [mm]</td><td>1.3 – 1.95 – 2.6</td></tr><tr><td>Cutting speed v<sub>c</sub> [rpm]</td><td>5000</td></tr><tr><td>Feed per tooth f<sub>z</sub> [mm/th]</td><td>0.02 – 0.04 – 0.06 – 0.08 – 0.1</td></tr></tbody></table>
The deformation is calculated for the various cases. Fig. 4 shows the deformation of the 2.6 mm thick plate when the tool enters the material at the top left corner. The shape of this deformation is similar for all the cases studied. Only the amplitude of the deformation varies and is explained below.

<figure><img src="image_1931.png" /><figcaption>Figure 4: Deformation of the 2.6 mm plate with cutting tool at the upper left corner</figcaption></figure>

Fig. 5 shows the average cutting force and the maximum deformation of a 2.6 mm plate as a function of the milling position. Fig. 5.a shows the maximum deformation for a 2.6 mm thick plate with a tooth feed variable between 0.02 mm/th and 0.1 mm/th in steps of 0.02 mm/th. The maximum deformation of the plate is reached at the ends of the plate (when x is equal to 0 or 100 mm). This deformation increases as the feed per tooth increases. It varies between 0.240 mm for a tooth feed of 0.02 mm/th and 0.494 mm for a tooth feed of 0.1 mm/th. This is because the average cutting force increases as the feed per tooth increases (Fig. 5.b). Between 0 and 50 mm, the cutting force increases. This is because the deformation decreases between 0 and 50 mm (Fig. 5.b), which increases the radial cutting depth ae and therefore the cutting force. Between 50 and 100 mm, the opposite effect occurs, as the deformation increases, resulting in a decrease in the radial cutting depth ae and therefore a reduction in the cutting force. The same trend can be observed for the other plate thicknesses.

<figure><img src="image_1932.png" /><figcaption>Figure 5: Spatial evolution for a 2.6 mm thick plate</figcaption></figure>
The deformation is calculated for the various cases. Fig. 4 shows the deformation of the 2.6 mm thick plate when the tool enters the material at the top left corner. The shape of this deformation is similar for all the cases studied. Only the amplitude of the deformation varies and is explained below.

<figure><img src="image_1926.png" /><figcaption>Figure 4: Deformation of the 2.6 mm plate with cutting tool at the upper left corner</figcaption></figure>

Fig. 5 shows the average cutting force and the maximum deformation of a 2.6 mm plate as a function of the milling position. Fig. 5.a shows the maximum deformation for a 2.6 mm thick plate with a tooth feed variable between 0.02 mm/th and 0.1 mm/th in steps of 0.02 mm/th. The maximum deformation of the plate is reached at the ends of the plate (when x is equal to 0 or 100 mm). This deformation increases as the feed per tooth increases. It varies between 0.240 mm for a tooth feed of 0.02 mm/th and 0.494 mm for a tooth feed of 0.1 mm/th. This is because the average cutting force increases as the feed per tooth increases (Fig. 5.b). Between 0 and 50 mm, the cutting force increases. This is because the deformation decreases between 0 and 50 mm (Fig. 5.b), which increases the radial cutting depth ae and therefore the cutting force. Between 50 and 100 mm, the opposite effect occurs, as the deformation increases, resulting in a decrease in the radial cutting depth ae and therefore a reduction in the cutting force. The same trend can be observed for the other plate thicknesses.

<figure><img src="image_1927.png" /><figcaption>Figure 5: Spatial evolution for a 2.6 mm thick plate</figcaption></figure>
Fig. 6 shows the spatial evolution of the average cutting force and maximum deformation for fixed cutting conditions (feed per tooth of 0.02 mm/th). The trends are similar to those shown in Fig. 5. Furthermore, the thicker the plate, the greater the cutting force, since the radial depth of cut is equal to half the plate thickness. Even if the cutting force increases, the maximum deformation decreases as the plate thickness increases. This is due to the increase in plate stiffness as plate thickness increases. The same trend can be observed for the other feed rate.

<figure><img src="image_1933.png" /><figcaption>Figure 6: Spatial evolution for a feed rate of 0.6 mm/th</figcaption></figure>

In conclusion, the behaviour of the plates for the different study cases is similar. The levels of cutting forces and deformation are different according to thickness and feed rate. However, they follow the same trends.

## Conclusions

As FRPs are increasingly used materials, it is important to study their behavior during the thin plate milling operation. The main goal of this paper is to adapt models for calculating form error during thin metal plate milling to FRPs. The introduced modifications make it possible to calculate the form error for the case studied: a UD-CFRP laminate with all its plies oriented in the feed direction.

In addition to experimental validation, the outlook for this work is to improve the model to generalize the case studied. Indeed, the finite element model must be generalized for an anisotropic material (fiber orientation angle different from 0°) or for mirror laminate, an equivalent orthotropic material (as many plies in the ψ direction as -ψ). In addition, edge trimming operations, with the axis of the milling cutter perpendicular to the plate, need to be studied.

## Acknowledgments

The authors would like to thank Région Wallonne for supporting this research as part of the MachFlexComp M-ERA.NET 2022 research project under grant 2210138.

## References

[1] J. Du, M. Geng, W. Ming, W. He, and J. Ma, 'Simulation machining of fiber-reinforced composites: A review', *Int. J. Adv. Manuf. Technol.*, vol. 117, no. 1-2, pp. 1-15, Jul. 2021. https://doi.org/10.1007/s00170-021-07531-3

[2] I. Del Sol, A. Rivero, L. N. López de Lacalle, and A. J. Gamez, 'Thin-Wall Machining of Light Alloys: A Review of Models and Industrial Approaches', *Materials*, vol. 12, no. 12, Art. no. 12, Jan. 2019. https://doi.org/10.3390/ma12122012
Fig. 6 shows the spatial evolution of the average cutting force and maximum deformation for fixed cutting conditions (feed per tooth of 0.02 mm/th). The trends are similar to those shown in Fig. 5. Furthermore, the thicker the plate, the greater the cutting force, since the radial depth of cut is equal to half the plate thickness. Even if the cutting force increases, the maximum deformation decreases as the plate thickness increases. This is due to the increase in plate stiffness as plate thickness increases. The same trend can be observed for the other feed rate.

<figure><img src="image_1928.png" /><figcaption>Figure 6: Spatial evolution for a feed rate of 0.6 mm/th</figcaption></figure>

In conclusion, the behaviour of the plates for the different study cases is similar. The levels of cutting forces and deformation are different according to thickness and feed rate. However, they follow the same trends.

## Conclusions

As FRPs are increasingly used materials, it is important to study their behavior during the thin plate milling operation. The main goal of this paper is to adapt models for calculating form error during thin metal plate milling to FRPs. The introduced modifications make it possible to calculate the form error for the case studied: a UD-CFRP laminate with all its plies oriented in the feed direction.

In addition to experimental validation, the outlook for this work is to improve the model to generalize the case studied. Indeed, the finite element model must be generalized for an anisotropic material (fiber orientation angle different from 0°) or for mirror laminate, an equivalent orthotropic material (as many plies in the $\psi$ direction as $-\psi$). In addition, edge trimming operations, with the axis of the milling cutter perpendicular to the plate, need to be studied.

## Acknowledgments

The authors would like to thank Région Wallonne for supporting this research as part of the MachFlexComp M-ERA.NET 2022 research project under grant 2210138.

## References

[1] J. Du, M. Geng, W. Ming, W. He, and J. Ma, 'Simulation machining of fiber-reinforced composites: A review', *Int. J. Adv. Manuf. Technol.*, vol. 117, no. 1-2, pp. 1-15, Jul. 2021. https://doi.org/10.1007/s00170-021-07531-3

[2] I. Del Sol, A. Rivero, L. N. López de Lacalle, and A. J. Gamez, 'Thin-Wall Machining of Light Alloys: A Review of Models and Industrial Approaches', *Materials*, vol. 12, no. 12, Art. no. 12, Jan. 2019. https://doi.org/10.3390/ma12122012
[3] I. Llanos, A. Robles, J. Condón, M. Arizmendi, and A. Beristain, 'Deflection error modeling during thin-wall machining', Procedia CIRP, vol. 117, pp. 169-174, 2023. https://doi.org/10.1016/j.procir.2023.03.030

[4] E. Rivière-Lorphèvre, E. Filippi, and P. Dehombreux, 'Numerical Simulation of Machining Operations on Flexible Parts', Key Eng. Mater., vol. 554-557, pp. 1984-1991, Jun. 2013. https://doi.org/10.4028/www.scientific.net/KEM.554-557.1984

[5] K. Kecik, R. Rusinek, J. Warminski, and A. Weremczuk, 'Chatter control in the milling process of composite materials', J. Phys. Conf. Ser., vol. 382, no. 1, p. 012012, Aug. 2012. https://doi.org/10.1088/1742-6596/382/1/012012

[6] K. Ciecielag and K. Zaleski, 'Milling of Three Types of Thin-Walled Elements Made of Polymer Composite and Titanium and Aluminum Alloys Used in the Aviation Industry', Mater. 1996-1944, vol. 15, no. 17, p. 5949, Sep. 2022. https://doi.org/10.3390/ma15175949

[7] Y. Song, H. Cao, W. Zheng, D. Qu, L. Liu, and C. Yan, 'Cutting force modeling of machining carbon fiber reinforced polymer (CFRP) composites: A review', Compos. Struct., vol. 299, p. 116096, Nov. 2022. https://doi.org/10.1016/j.compstruct.2022.116096

[8] G. V. G. Rao, P. Mahajan, and N. Bhatnagar, 'Micro-mechanical modeling of machining of FRP composites – Cutting force analysis', Compos. Sci. Technol., vol. 67, no. 3, pp. 579-593, Mar. 2007. https://doi.org/10.1016/j.compscitech.2006.08.010

[9] R. Rentsch, O. Pecat, and E. Brinksmeier, 'Macro and micro process modeling of the cutting of carbon fiber reinforced plastics using FEM', Procedia Eng., vol. 10, pp. 1823-1828, Jan. 2011. https://doi.org/10.1016/j.proeng.2011.04.303

[10] D. Iliescu, D. Gehin, I. Iordanoff, F. Girot, and M. E. Gutiérrez, 'A discrete element method for the simulation of CFRP cutting', Compos. Sci. Technol., vol. 70, no. 1, pp. 73-80, Jan. 2010. https://doi.org/10.1016/j.compscitech.2009.09.007

[11] H. N. Huynh, E. Rivière-Lorphèvre, F. Ducobu, A. Ozcan, and O. Verlinden, 'Dystamill: a framework dedicated to the dynamic simulation of milling operations for stability assessment', Int. J. Adv. Manuf. Technol., vol. 98, no. 5-8, pp. 2109-2126, Sep. 2018. https://doi.org/10.1007/s00170-018-2357-3

[12] D. Gay, Matériaux composites, 6e éd. revue et augmentée. Paris: Lavoisier Hermès, 2015.

[13] O. C. Zienkiewicz, The finite element method, 3., Expanded and rev. Ed.. reprint. London Hamburg: McGraw-Hill, 1986.

[14] B. Castanié, C. Bouvet, and D. Guedra-Degeorges, 'Structures en matériaux composites stratifiés', Concept. Prod., Oct. 2013. https://doi.org/10.51257/a-v2-bm5080

[15] R. Mullin, M. Farhadmanesh, A. Ahmadian, and K. Ahmadi, 'Modeling and identification of cutting forces in milling of Carbon Fibre Reinforced Polymers', J. Mater. Process. Technol., vol. 280, p. 116595, Jun. 2020. https://doi.org/10.1016/j.jmatprotec.2020.116595

[16] Y. Altıntaş and P. Lee, 'A General Mechanics and Dynamics Model for Helical End Mills', CIRP Ann., vol. 45, no. 1, pp. 59-64, Jan. 1996. https://doi.org/10.1016/S0007-8506(07)63017-0

[17] P. D. Soden, M. J. Hinton, and A. S. Kaddour, 'Lamina properties, lay-up configurations and loading conditions for a range of fibre-reinforced composite laminates', Compos. Sci. Technol., pp. 1011-1022, Mar. 1998. https://doi.org/10.1016/S0266-3538(98)00078-5
[3] I. Llanos, A. Robles, J. Condón, M. Arizmendi, and A. Beristain, 'Deflection error modeling during thin-wall machining', *Procedia CIRP*, vol. 117, pp. 169-174, 2023. https://doi.org/10.1016/j.procir.2023.03.030

[4] E. Rivière-Lorphèvre, E. Filippi, and P. Dehombreux, 'Numerical Simulation of Machining Operations on Flexible Parts', *Key Eng. Mater.*, vol. 554-557, pp. 1984-1991, Jun. 2013. https://doi.org/10.4028/www.scientific.net/KEM.554-557.1984

[5] K. Kecik, R. Rusinek, J. Warminski, and A. Weremczuk, 'Chatter control in the milling process of composite materials', *J. Phys. Conf. Ser.*, vol. 382, no. 1, p. 012012, Aug. 2012. https://doi.org/10.1088/1742-6596/382/1/012012

[6] K. Ciecielag and K. Zaleski, 'Milling of Three Types of Thin-Walled Elements Made of Polymer Composite and Titanium and Aluminum Alloys Used in the Aviation Industry', *Mater. 1996-1944*, vol. 15, no. 17, p. 5949, Sep. 2022. https://doi.org/10.3390/ma15175949

[7] Y. Song, H. Cao, W. Zheng, D. Qu, L. Liu, and C. Yan, 'Cutting force modeling of machining carbon fiber reinforced polymer (CFRP) composites: A review', *Compos. Struct.*, vol. 299, p. 116096, Nov. 2022. https://doi.org/10.1016/j.compstruct.2022.116096

[8] G. V. G. Rao, P. Mahajan, and N. Bhatnagar, 'Micro-mechanical modeling of machining of FRP composites – Cutting force analysis', *Compos. Sci. Technol.*, vol. 67, no. 3, pp. 579-593, Mar. 2007. https://doi.org/10.1016/j.compscitech.2006.08.010

[9] R. Rentsch, O. Pecat, and E. Brinksmeier, 'Macro and micro process modeling of the cutting of carbon fiber reinforced plastics using FEM', *Procedia Eng.*, vol. 10, pp. 1823-1828, Jan. 2011. https://doi.org/10.1016/j.proeng.2011.04.303

[10] D. Iliescu, D. Gehin, I. Iordanoff, F. Girot, and M. E. Gutiérrez, 'A discrete element method for the simulation of CFRP cutting', *Compos. Sci. Technol.*, vol. 70, no. 1, pp. 73-80, Jan. 2010. https://doi.org/10.1016/j.compscitech.2009.09.007

[11] H. N. Huynh, E. Rivière-Lorphèvre, F. Ducobu, A. Ozcan, and O. Verlinden, 'Dystamill: a framework dedicated to the dynamic simulation of milling operations for stability assessment', *Int. J. Adv. Manuf. Technol.*, vol. 98, no. 5-8, pp. 2109-2126, Sep. 2018. https://doi.org/10.1007/s00170-018-2357-3

[12] D. Gay, *Matériaux composites*, 6e éd. revue et augmentée. Paris: Lavoisier Hermès, 2015.

[13] O. C. Zienkiewicz, *The finite element method*, 3., Expanded and rev. Ed.. reprint. London Hamburg: McGraw-Hill, 1986.

[14] B. Castanié, C. Bouvet, and D. Guedra-Degeorges, 'Structures en matériaux composites stratifiés', *Concept. Prod.*, Oct. 2013. https://doi.org/10.51257/a-v2-bm5080

[15] R. Mullin, M. Farhadmanesh, A. Ahmadian, and K. Ahmadi, 'Modeling and identification of cutting forces in milling of Carbon Fibre Reinforced Polymers', *J. Mater. Process. Technol.*, vol. 280, p. 116595, Jun. 2020. https://doi.org/10.1016/j.jmatprotec.2020.116595

[16] Y. Altıntaş and P. Lee, 'A General Mechanics and Dynamics Model for Helical End Mills', *CIRP Ann.*, vol. 45, no. 1, pp. 59-64, Jan. 1996. https://doi.org/10.1016/S0007-8506(07)63017-0

[17] P. D. Soden, M. J. Hinton, and A. S. Kaddour, 'Lamina properties, lay-up configurations and loading conditions for a range of fibre-reinforced composite laminates', *Compos. Sci. Technol.*, pp. 1011-1022, Mar. 1998. https://doi.org/10.1016/S0266-3538(98)00078-5
# Surface characteristics comparison between additively manufactured Ti6Al4V and wrought Ti6Al4V turned samples

SAFFIOTI Maria Rosaria¹,ᵃ*, ROTELLA Giovanna² and DEL PRETE Antonio³

¹Department of Mechanical, Energy and Management Engineering, University of Calabria, Rende, CS 87036, Italy

²Department of Engineering, University LUM Giuseppe Degennaro, Casamassima, BA 70010, Italy

³Department of Engineering for Innovation, University of Salento, Lecce, LE 73100, Italy

ªmariarosaria.saffioti@unical.it

**Keywords:** Turning Process, Additive Manufacturing, Surface

**Abstract.** This paper aims to contribute increasing the knowledge surrounding *Ti6Al4V* by making a comprehensive comparative analysis of surface characteristics between additively manufactured and conventionally wrought *Ti6Al4V* turned samples. The tests were carried out by varying the process parameters such as cutting speed and lubricant conditions (dry and cryogenic), and by keeping fixed the feed rate and the depth of cut. The comparison of these two manufacturing methods allows to explore how the different surface properties, such as roughness, microstructure and hardness, can change.

**Introduction**

*Ti6Al4V* Titanium alloy, is known for its remarkable mechanical properties and biocompatibility, it has garnered widespread attention in various engineering applications, particularly in the aerospace and biomedical fields [1]. With the advent of advanced manufacturing techniques, additive manufacturing (AM) has emerged as a promising method for fabricating components with improved design flexibility. However, the surface characteristics of additively manufactured *Ti6Al4V* components have been the subject of many studies due to the inherent complexities associated with layer-wise deposition [2, 3].

The decision to use *Ti6Al4V* samples, in this study, was made because this particular kind of material is employed to make different components in many fields and it can be produced by conventional techniques (i.e. casting, extrusion, etc.) or AM technologies [4].

The differences between wrought and additively manufactured Ti6Al4V arise mainly due to their distinct manufacturing processes, leading to variations in microstructure, mechanical properties, and potential applications [5].

Wrought *Ti6Al4V* is widely used in applications requiring high strength and toughness, such as in aerospace components and biomedical implants. Its application is somewhat limited by the geometric complexity that can be achieved through conventional manufacturing techniques [6].

AM *Ti6Al4V* offers design flexibility, allowing the production of complex geometries that would be difficult or impossible to achieve with traditional manufacturing methods. This makes AM particularly attractive for custom implants in the biomedical field, lightweight structures in aerospace, and components requiring intricate internal features [7].

Thus, while wrought *Ti6Al4V* has a long history of use in demanding applications due to its reliable and well-known properties, additively manufactured *Ti6Al4V* opens new possibilities in design and functionality, even if with better knowledge on their microstructure, mechanical properties, and the demand for post-processing to achieve optimal performance is still needed.

<figure><img src="image_1934.png" /></figure>
# Surface characteristics comparison between additively manufactured Ti6Al4V and wrought Ti6Al4V turned samples

SAFFIOTI Maria Rosaria¹,*, ROTELLA Giovanna² and DEL PRETE Antonio³

¹Department of Mechanical, Energy and Management Engineering, University of Calabria, Rende, CS 87036, Italy

²Department of Engineering, University LUM Giuseppe Degennaro, Casamassima, BA 70010, Italy

³Department of Engineering for Innovation, University of Salento, Lecce, LE 73100, Italy

ªmariarosaria.saffioti@unical.it

**Keywords:** Turning Process, Additive Manufacturing, Surface

**Abstract.** This paper aims to contribute increasing the knowledge surrounding *Ti6Al4V* by making a comprehensive comparative analysis of surface characteristics between additively manufactured and conventionally wrought *Ti6Al4V* turned samples. The tests were carried out by varying the process parameters such as cutting speed and lubricant conditions (dry and cryogenic), and by keeping fixed the feed rate and the depth of cut. The comparison of these two manufacturing methods allows to explore how the different surface properties, such as roughness, microstructure and hardness, can change.

**Introduction**

*Ti6Al4V* Titanium alloy, is known for its remarkable mechanical properties and biocompatibility, it has garnered widespread attention in various engineering applications, particularly in the aerospace and biomedical fields [1]. With the advent of advanced manufacturing techniques, additive manufacturing (AM) has emerged as a promising method for fabricating components with improved design flexibility. However, the surface characteristics of additively manufactured *Ti6Al4V* components have been the subject of many studies due to the inherent complexities associated with layer-wise deposition [2, 3].

The decision to use *Ti6Al4V* samples, in this study, was made because this particular kind of material is employed to make different components in many fields and it can be produced by conventional techniques (i.e. casting, extrusion, etc.) or AM technologies [4].

The differences between wrought and additively manufactured Ti6Al4V arise mainly due to their distinct manufacturing processes, leading to variations in microstructure, mechanical properties, and potential applications [5].

Wrought *Ti6Al4V* is widely used in applications requiring high strength and toughness, such as in aerospace components and biomedical implants. Its application is somewhat limited by the geometric complexity that can be achieved through conventional manufacturing techniques [6].

AM *Ti6Al4V* offers design flexibility, allowing the production of complex geometries that would be difficult or impossible to achieve with traditional manufacturing methods. This makes AM particularly attractive for custom implants in the biomedical field, lightweight structures in aerospace, and components requiring intricate internal features [7].

Thus, while wrought *Ti6Al4V* has a long history of use in demanding applications due to its reliable and well-known properties, additively manufactured *Ti6Al4V* opens new possibilities in design and functionality, even if with better knowledge on their microstructure, mechanical properties, and the demand for post-processing to achieve optimal performance is still needed.

<figure><img src="image_1929.png" /></figure>
Usually, turning process represents a traditional subtractive manufacturing method commonly employed for achieving high precision in components and for improving the surface state [8]. The comparison between wrought and additively manufactured samples, which have undergone turning process, provides valuable insights into the trade-offs and advantages associated with each manufacturing approach, offering a holistic understanding of surface quality and performance [9, 10].

In this study, a deeper investigation into the methodologies employed for both manufacturing processes will be performed, explaining which factors have more impact on the surface characteristics. Moreover, an analysis of the influence of surface features on the mechanical properties of the Ti6Al4V alloy was carried out, considering the implications for applications in demanding environments.

By highlighting the surface characteristics of additively manufactured Ti6Al4V in comparison to its wrought samples, this research aims to evaluate the strengths and limitations of each manufacturing technique. Such insights are crucial for optimizing material performance and focus on the selection of manufacturing processes based on specific application requirements.

## Material and Methods

The workpiece material used for the experiments was the grade 5 titanium alloy in a shape of cylindrical bars with 30 mm of diameter. The experimental machining tests were performed under dry and cryogenic conditions on the additively manufactured and wrought specimens. The material was supplied in the as-built condition (as produced by the metal 3D printer). In detail, the material was used without any post-processing or heat treatment according to the industrial standard. Moreover, to compare and understand the difference regarding the machinability between the additively manufactured materials and the conventionally produced, some cylindrical bars of Ti6Al4V alloys produced via standard processes (the wrought bars were produced by extrusion process) were machined using the same cutting parameters and set-up.

Turning tests have been carried out on a high speed CNC turning centre (Mazak Quick Turn II.) using the turning parameters reported in Table 1.

<table><caption>Table 2. Experimental plan for turning.</caption><thead><tr><td>Turning</td><td>Value</td></tr></thead><tbody><tr><td>Cutting speed v (m/min)</td><td>60 - 75 - 90</td></tr><tr><td>Feed f (mm/rev)</td><td>0.15</td></tr><tr><td>Depth of cut (mm)</td><td>0.05</td></tr><tr><td>Cooling conditions</td><td>DRY - CRYO</td></tr></tbody></table>

A fresh cutting tool has been used at each test to avoid undesired effects of wear. Three repetitions for each parameter set have been executed. Two different lubricant conditions have been considered for the tests namely, dry (DRY) and cryogenic (CRYO). While dry conditions were used without the use of lubricant, the cryogenic setup involved the use of LN2 coolant applied at the pressure of 12 bar through two nozzles with a diameter of 2 mm to the cutting region. PVD coated TiAlN (Sandvik N123H2-0400-RO 1105) inserts with a clearance angle of 7° and a rake angle of 12° have been used.

The overall machining setup for both dry (a) and cryogenic (b) systems is reported in Fig. 1.
Usually, turning process represents a traditional subtractive manufacturing method commonly employed for achieving high precision in components and for improving the surface state [8]. The comparison between wrought and additively manufactured samples, which have undergone turning process, provides valuable insights into the trade-offs and advantages associated with each manufacturing approach, offering a holistic understanding of surface quality and performance [9, 10].

In this study, a deeper investigation into the methodologies employed for both manufacturing processes will be performed, explaining which factors have more impact on the surface characteristics. Moreover, an analysis of the influence of surface features on the mechanical properties of the Ti6Al4V alloy was carried out, considering the implications for applications in demanding environments.

By highlighting the surface characteristics of additively manufactured Ti6Al4V in comparison to its wrought samples, this research aims to evaluate the strengths and limitations of each manufacturing technique. Such insights are crucial for optimizing material performance and focus on the selection of manufacturing processes based on specific application requirements.

## Material and Methods

The workpiece material used for the experiments was the grade 5 titanium alloy in a shape of cylindrical bars with 30 mm of diameter. The experimental machining tests were performed under dry and cryogenic conditions on the additively manufactured and wrought specimens. The material was supplied in the as-built condition (as produced by the metal 3D printer). In detail, the material was used without any post-processing or heat treatment according to the industrial standard. Moreover, to compare and understand the difference regarding the machinability between the additively manufactured materials and the conventionally produced, some cylindrical bars of Ti6Al4V alloys produced via standard processes (the wrought bars were produced by extrusion process) were machined using the same cutting parameters and set-up.

Turning tests have been carried out on a high speed CNC turning centre (Mazak Quick Turn II.) using the turning parameters reported in Table 1.

<table><caption>Table 2. Experimental plan for turning.</caption><thead><tr><td>Turning</td><td>Value</td></tr></thead><tbody><tr><td>Cutting speed v (m/min)</td><td>60 - 75 - 90</td></tr><tr><td>Feed f (mm/rev)</td><td>0.15</td></tr><tr><td>Depth of cut (mm)</td><td>0.05</td></tr><tr><td>Cooling conditions</td><td>DRY - CRYO</td></tr></tbody></table>

A fresh cutting tool has been used at each test to avoid undesired effects of wear. Three repetitions for each parameter set have been executed. Two different lubricant conditions have been considered for the tests namely, dry (DRY) and cryogenic (CRYO). While dry conditions were used without the use of lubricant, the cryogenic setup involved the use of LN2 coolant applied at the pressure of 12 bar through two nozzles with a diameter of 2 mm to the cutting region. PVD coated TiAlN (Sandvik N123H2-0400-RO 1105) inserts with a clearance angle of 7° and a rake angle of 12° have been used.

The overall machining setup for both dry (a) and cryogenic (b) systems is reported in Fig. 1.
<figure><img src="image_1935.png" /><figcaption>Figure - 1. Set up for dry (a) and cryogenic (b) turning tests.</figcaption></figure>

The mean surface roughness Ra, was measured using a non-contact 3D confocal profilometer. Five measurements for each sample were acquired at different area of the turned surface in order to have statistically accurate dataset. Preliminarily, roughness measurements on the as produced AM and wrought samples were performed, finding a values of 9.97±0.78 µm and 1.20±0.50 µm, respectively.

Afterwards, samples have been sectioned in the cross section direction, mechanically polished up to 1 µm and etched using the Kroll's reagent to reveal microstructure. Then, the obtained specimens have been analysed under an optical microscope (Leica©DFC320 1000×) to verify the microstructure and evaluate the grain size. The grain size was measured using ImagePro commercial software able to identify the single grains from boundaries. It was used to calculate the reference equivalent circle diameter of each single grain on the turned surface, in accordance with ASTM E112-12 standard, leading with sufficient approximation, to an equiaxed grain equivalent configuration. Preliminarily, grain size measurements on the as produced AM and wrought samples were performed, finding a mean values of 5 µm for both.

The polished samples have been also used to test the changes in micro-hardness due to the severe plastic deformation process and the recrystallization phenomena. The HV₀.₁ through the section has been tested by an instrumented micro indenter equipped with a Berkovich tip (Anton Paar micro-indenter MHT). Three matrix spots composed by twenty indentations were sampled for each test piece. Preliminarily, the micro-hardness values referred to the as produced AM and wrought samples are, 420 HV and 390 HV were measured respectively.

## Results

Wrought *Ti6Al4V* typically has a more uniform and predictable microstructure. The manufacturing process involves melting, forging, and rolling, which results in a material with well-defined grain structures, as shown in Fig. 2(a). Typically, it has lower porosity and fewer defects, due to the nature of its manufacturing process, which involves melting, casting, and deformation under controlled conditions. This process allows for the control of microstructural features such as grain size and orientation, which can be optimized for specific applications.

Additively Manufactured *Ti6Al4V*, on the other hand, has a more varied microstructure that is strongly influenced by the specific AM process used. These processes involve layer-by-layer melting and solidification of titanium powder, which can lead to rapid cooling rates and the formation of fine, columnar grains and martensitic microstructures. The microstructure can vary
<figure><img src="image_1930.png" /><figcaption>Figure - 1. Set up for dry (a) and cryogenic (b) turning tests.</figcaption></figure>

The mean surface roughness Ra, was measured using a non-contact 3D confocal profilometer. Five measurements for each sample were acquired at different area of the turned surface in order to have statistically accurate dataset. Preliminarily, roughness measurements on the as produced AM and wrought samples were performed, finding a values of 9.97±0.78 µm and 1.20±0.50 µm, respectively.

Afterwards, samples have been sectioned in the cross section direction, mechanically polished up to 1 µm and etched using the Kroll's reagent to reveal microstructure. Then, the obtained specimens have been analysed under an optical microscope (Leica©DFC320 1000×) to verify the microstructure and evaluate the grain size. The grain size was measured using ImagePro commercial software able to identify the single grains from boundaries. It was used to calculate the reference equivalent circle diameter of each single grain on the turned surface, in accordance with ASTM E112-12 standard, leading with sufficient approximation, to an equiaxed grain equivalent configuration. Preliminarily, grain size measurements on the as produced AM and wrought samples were performed, finding a mean values of 5 µm for both.

The polished samples have been also used to test the changes in micro-hardness due to the severe plastic deformation process and the recrystallization phenomena. The HV₀.₁ through the section has been tested by an instrumented micro indenter equipped with a Berkovich tip (Anton Paar micro-indenter MHT). Three matrix spots composed by twenty indentations were sampled for each test piece. Preliminarily, the micro-hardness values referred to the as produced AM and wrought samples are, 420 HV and 390 HV were measured respectively.

## Results

Wrought *Ti6Al4V* typically has a more uniform and predictable microstructure. The manufacturing process involves melting, forging, and rolling, which results in a material with well-defined grain structures, as shown in Fig. 2(a). Typically, it has lower porosity and fewer defects, due to the nature of its manufacturing process, which involves melting, casting, and deformation under controlled conditions. This process allows for the control of microstructural features such as grain size and orientation, which can be optimized for specific applications.

Additively Manufactured *Ti6Al4V*, on the other hand, has a more varied microstructure that is strongly influenced by the specific AM process used. These processes involve layer-by-layer melting and solidification of titanium powder, which can lead to rapid cooling rates and the formation of fine, columnar grains and martensitic microstructures. The microstructure can vary
significantly over small distances due to localized thermal gradients and cooling rates, as shown in Fig. 2(b). Also, AM Ti6Al4V can have higher levels of porosity and may contain defects such as lack of fusion or gas entrapment. These defects are intrinsic to the powder bed fusion processes and can affect mechanical properties such as fatigue life. Efforts in process optimization and post-processing are ongoing to minimize these defects.

<figure><img src="image_1936.png" /><figcaption>Figure 2. Optical micrograph of Ti6Al4V microstructure, (a) wrought and (b) AM samples before turning tests.</figcaption></figure>

After turning tests, as shown in Fig. 3, based on the different cooling conditions (dry or cryo) and considering the manufactured process (conventional or AM) the microstructure presented some significant modifications. In both cases, it exhibits a significant grain refinements especially near the surface and the subsurface. Concerning the AM samples, the properties can be more anisotropic, meaning they vary with direction relative to the build orientation. This is due to the layer-wise manufacturing process and the resultant microstructural differences. Post-processing heat treatments are often necessary to relieve residual stresses and improve ductility.

<figure><img src="image_1937.png" /><figcaption>Figure 3. Optical micrograph of Ti6Al4V microstructure after turning tests at 90 m/min of cutting speed, (a) wrought and dry, (b) AM and dry, (c) wrought and cryogenic, (d) AM and cryogenic.</figcaption></figure>

Moreover, after the optical analysis the grain size was measured. The results are shown in Fig. 4, where at increasing cutting speed the grain size decreases in particular in cryogenic conditions compare to dry either in the case of wrought material as well as in the case of AM. This grain refinement is often the outcome of improvements in wear and corrosion resistance, generating compressive residual stresses enabling fatigue life enhancement.
significantly over small distances due to localized thermal gradients and cooling rates, as shown in Fig. 2(b). Also, AM Ti6Al4V can have higher levels of porosity and may contain defects such as lack of fusion or gas entrapment. These defects are intrinsic to the powder bed fusion processes and can affect mechanical properties such as fatigue life. Efforts in process optimization and post-processing are ongoing to minimize these defects.

<figure><img src="image_1931.png" /><figcaption>Figure 2. Optical micrograph of Ti6Al4V microstructure, (a) wrought and (b) AM samples before turning tests.</figcaption></figure>

After turning tests, as shown in Fig. 3, based on the different cooling conditions (dry or cryo) and considering the manufactured process (conventional or AM) the microstructure presented some significant modifications. In both cases, it exhibits a significant grain refinements especially near the surface and the subsurface. Concerning the AM samples, the properties can be more anisotropic, meaning they vary with direction relative to the build orientation. This is due to the layer-wise manufacturing process and the resultant microstructural differences. Post-processing heat treatments are often necessary to relieve residual stresses and improve ductility.

<figure><img src="image_1932.png" /><figcaption>Figure 3. Optical micrograph of Ti6Al4V microstructure after turning tests at 90 m/min of cutting speed, (a) wrought and dry, (b) AM and dry, (c) wrought and cryogenic, (d) AM and cryogenic.</figcaption></figure>

Moreover, after the optical analysis the grain size was measured. The results are shown in Fig. 4, where at increasing cutting speed the grain size decreases in particular in cryogenic conditions compare to dry either in the case of wrought material as well as in the case of AM. This grain refinement is often the outcome of improvements in wear and corrosion resistance, generating compressive residual stresses enabling fatigue life enhancement.
<figure><img src="image_1938.png" /><figcaption>Figure 4. Grain size evolution at varying turning speed, cooling conditions and manufacturing process.</figcaption></figure>

Concerning the mean surface roughness Ra, turning process significantly improved the surface quality up to a mean value of about 0.30 µm for Ra. Therefore, as shown in Fig. 2, by varying turning process parameters and cooling conditions, the machined surface roughness was significantly improved. In particular, the roughness reduced at increasing of cutting speed. With approximately the same general trend, Ra decrease more when using cryogenic cooling compared to the same process parameters in dry conditions.

<figure><img src="image_1939.png" /><figcaption>Figure 5. Mean roughness Ra evolution at varying turning speed, cooling conditions and manufacturing process.</figcaption></figure>

Finally, in terms of surface micro-hardness, as Fig. 6 shows, for the overall experimental campaign turning process leading a slight change in the surface hardness, by increasing the hardness at stronger process conditions namely by increasing the cutting speed and under cryogenic conditions. Furthermore, the deformed layer was deeper after turning, which proves an increased work hardening on the turned surface and sub surface. As can be observed cryogenic conditions lead to an increase in hardness higher with respect of dry conditions for both wrought and AM samples.
<figure><img src="image_1933.png" /><figcaption>Figure 4. Grain size evolution at varying turning speed, cooling conditions and manufacturing process.</figcaption></figure>

Concerning the mean surface roughness Ra, turning process significantly improved the surface quality up to a mean value of about 0.30 µm for Ra. Therefore, as shown in Fig. 2, by varying turning process parameters and cooling conditions, the machined surface roughness was significantly improved. In particular, the roughness reduced at increasing of cutting speed. With approximately the same general trend, Ra decrease more when using cryogenic cooling compared to the same process parameters in dry conditions.

<figure><img src="image_1934.png" /><figcaption>Figure 5. Mean roughness Ra evolution at varying turning speed, cooling conditions and manufacturing process.</figcaption></figure>

Finally, in terms of surface micro-hardness, as Fig. 6 shows, for the overall experimental campaign turning process leading a slight change in the surface hardness, by increasing the hardness at stronger process conditions namely by increasing the cutting speed and under cryogenic conditions. Furthermore, the deformed layer was deeper after turning, which proves an increased work hardening on the turned surface and sub surface. As can be observed cryogenic conditions lead to an increase in hardness higher with respect of dry conditions for both wrought and AM samples.
<figure><img src="image_1940.png" /><figcaption>Figure 6. Micro-hardness below the turned surface at varying turning speed, cooling conditions and manufacturing process.</figcaption></figure>

## Conclusions

In this study, we have comprehensively explored the behavior of wrought and AM Ti6Al4V alloy under both dry and cryogenic conditions through an extensive experimental campaign. The findings reveal significant distinctions in characteristics and properties between the two material types and the environmental conditions applied. Under dry conditions, both wrought and AM Ti6Al4V samples demonstrated expected wear behavior, with wrought samples exhibiting superior wear resistance attributed to their denser and more homogeneous microstructure. However, the introduction of cryogenic conditions altered the performance landscape. Cryogenic treatment enhanced the wear resistance of both samples significantly. This improvement is primarily due to the transformation of microstructural features and induced residual stresses.

Moreover, changes also in grain size, roughness and hardness were observed for both types of material and conditions.

Overall, this study not only underscores the importance of processing conditions on the performance of Ti6Al4V alloys but also highlights the potential of cryogenic treatment as a method to enhance the properties of AM materials. This could have significant implications for the aerospace, automotive, and biomedical sectors, where the demand for high-performance titanium alloys is continuously growing. Future work will focus on the long-term effects of cryogenic treatment on the microstructure and properties of these materials to further understand the mechanisms behind the observed enhancements.

## References

[1] Jawahir, I.S., Brinksmeier, E., Saoubi, R.M., Aspinwall, D.K., Outeiro, J.C., Meyer, D., Umbrello, D., Jayal, A.D., Manufacturing Technology Surface integrity in material removal processes: Recent advances, CIRP Annals - Manuf. Technol. (2011) 60 603–626. https://doi.org/10.1016/j.cirp.2011.05.002

[2] Abdulhameed, O., Al-Ahmari, A., Ameen, W., & Mian, S.H. Additive manufacturing: Challenges, trends, and applications. Advances in Mechanical Engineering, (2019), 11(2), 1-27. https://doi.org/10.1177/1687814018822880

[3] Sharma S, Meena A. Microstructure attributes and tool wear mechanisms during high-speed machining of Ti-6Al-4V. J Manuf Process, (2020) 50. https://doi.org/10.1016/j.jmapro.2019.12.029
<figure><img src="image_1935.png" /><figcaption>Figure 6. Micro-hardness below the turned surface at varying turning speed, cooling conditions and manufacturing process.</figcaption></figure>

## Conclusions

In this study, we have comprehensively explored the behavior of wrought and AM Ti6Al4V alloy under both dry and cryogenic conditions through an extensive experimental campaign. The findings reveal significant distinctions in characteristics and properties between the two material types and the environmental conditions applied. Under dry conditions, both wrought and AM Ti6Al4V samples demonstrated expected wear behavior, with wrought samples exhibiting superior wear resistance attributed to their denser and more homogeneous microstructure. However, the introduction of cryogenic conditions altered the performance landscape. Cryogenic treatment enhanced the wear resistance of both samples significantly. This improvement is primarily due to the transformation of microstructural features and induced residual stresses.

Moreover, changes also in grain size, roughness and hardness were observed for both types of material and conditions.

Overall, this study not only underscores the importance of processing conditions on the performance of Ti6Al4V alloys but also highlights the potential of cryogenic treatment as a method to enhance the properties of AM materials. This could have significant implications for the aerospace, automotive, and biomedical sectors, where the demand for high-performance titanium alloys is continuously growing. Future work will focus on the long-term effects of cryogenic treatment on the microstructure and properties of these materials to further understand the mechanisms behind the observed enhancements.

## References

[1] Jawahir, I.S., Brinksmeier, E., Saoubi, R.M., Aspinwall, D.K., Outeiro, J.C., Meyer, D., Umbrello, D., Jayal, A.D., Manufacturing Technology Surface integrity in material removal processes: Recent advances, CIRP Annals - Manuf. Technol. (2011) 60 603–626. https://doi.org/10.1016/j.cirp.2011.05.002

[2] Abdulhameed, O., Al-Ahmari, A., Ameen, W., & Mian, S.H. Additive manufacturing: Challenges, trends, and applications. Advances in Mechanical Engineering, (2019), 11(2), 1-27. https://doi.org/10.1177/1687814018822880

[3] Sharma S, Meena A. Microstructure attributes and tool wear mechanisms during high-speed machining of Ti-6Al-4V. J Manuf Process, (2020) 50. https://doi.org/10.1016/j.jmapro.2019.12.029
[4] Saffioti, M. R., Sanguedolce, M., Rotella, G., Umbrello, D., Experimental analysis on machining parameters and cooling conditions affecting surface integrity of Ti6Al4V. (2021), ESAFORM 2021 - 24th International Conference on Material Forming, Liège, Belgium. https://doi.org/10.25518/esaform21.2459

[5] Revuru, R.S., Posinasetti, N.R., Vsn, V.R., Amrita, M., Application of cutting fluids in machining of titanium alloys—a review, Int. J. Adv. Manuf. Technol. (2017) 91 2477–2498. https://doi.org/10.1007/s00170-016-9883-7

[6] Umbrello D., Saffioti M. R., Imbrogno S., Surface modifications induced by turning on additively manufactured Zr-702 and their effects on cell adhesion and proliferation for biomedical applications, CIRP Annals, (2022), Volume 71, Issue 1, Pages 457-460, ISSN 0007-8506, https://doi.org/10.1016/j.cirp.2022.04.079

[7] Gupta, M.K., Song, Q., Liu, Z., Sarikaya, M., Jamil, M., Mia, M., Khanna, N., Krolczyk, G.M., Experimental characterization of the performance of hybrid cryo-lubrication assisted turning of Ti-6Al-4V alloy, Tribol. Int. (2021) 153 106582. https://doi.org/10.1016/j.triboint.2020.106582

[8] Munoz ~ JA, Higuera OF, Benito JA, Bradai D, Khelfa T, Bolmaro RE, et al. Analysis of the micro and substructural evolution during severe plastic deformation of ARMCO iron and consequences in mechanical properties. Mater Sci Eng A (2019):740-1. https://doi.org/10.1016/j.msea.2018.10.100

[9] Saffioti M. R., Rotella G., Umbrello D., Superfinishing processes applied on the biomedical implants surface to improve their performance, Materials Research Proceedings, (2023) Vol. 28, pp 1341-1346.

[10] Axinte D, Guo Y, Liao Z, Shih AJ, M'Saoubi R, Sugita N, Machining of bio compatible materials — Recent advances. CIRP Annals Manufacturing Technology, (2019), 68/2:629–652.
[4] Saffioti, M. R., Sanguedolce, M., Rotella, G., Umbrello, D., Experimental analysis on machining parameters and cooling conditions affecting surface integrity of Ti6Al4V. (2021), ESAFORM 2021 - 24th International Conference on Material Forming, Liège, Belgium. https://doi.org/10.25518/esaform21.2459

[5] Revuru, R.S., Posinasetti, N.R., Vsn, V.R., Amrita, M., Application of cutting fluids in machining of titanium alloys—a review, Int. J. Adv. Manuf. Technol. (2017) 91 2477–2498. https://doi.org/10.1007/s00170-016-9883-7

[6] Umbrello D., Saffioti M. R., Imbrogno S., Surface modifications induced by turning on additively manufactured Zr-702 and their effects on cell adhesion and proliferation for biomedical applications, CIRP Annals, (2022), Volume 71, Issue 1, Pages 457-460, ISSN 0007-8506, https://doi.org/10.1016/j.cirp.2022.04.079

[7] Gupta, M.K., Song, Q., Liu, Z., Sarikaya, M., Jamil, M., Mia, M., Khanna, N., Krolczyk, G.M., Experimental characterization of the performance of hybrid cryo-lubrication assisted turning of Ti-6Al-4V alloy, Tribol. Int. (2021) 153 106582. https://doi.org/10.1016/j.triboint.2020.106582

[8] Munoz ~ JA, Higuera OF, Benito JA, Bradai D, Khelfa T, Bolmaro RE, et al. Analysis of the micro and substructural evolution during severe plastic deformation of ARMCO iron and consequences in mechanical properties. Mater Sci Eng A (2019):740-1. https://doi.org/10.1016/j.msea.2018.10.100

[9] Saffioti M. R., Rotella G., Umbrello D., Superfinishing processes applied on the biomedical implants surface to improve their performance, Materials Research Proceedings, (2023) Vol. 28, pp 1341-1346.

[10] Axinte D, Guo Y, Liao Z, Shih AJ, M'Saoubi R, Sugita N, Machining of bio compatible materials — Recent advances. CIRP Annals Manufacturing Technology, (2019), 68/2:629–652.
# Enhancing interfacial bonding strength in fiber metal laminates through metal surface treatments

LIU Zheng<fnref n="1" />,ᵃ*, SIMONETTO Enrico<fnref n="1" />,ᵇ, UMBRELLO Domenico<fnref n="3" />,ᶜ, ROTELLA Giovanna<fnref n="2" />,ᵈ

**Keywords:** Fiber Metal Laminates, Metal Surface Treatment, Interfacial Bonding Strength

**Abstract** In recent decades, the aircraft industry has experienced a growing need for high-performance, lightweight structures, leading to a significant focus on developing fiber-metal laminates (FMLs). These composites possess various benefits in terms of stiffness, yield stress, fatigue, and high-velocity impact properties due to their hybrid composition. However, certain challenges related to the fabrication, surface treatment, and mechanical properties of these structures require further attention. This paper reviews FML manufacturing with a focus on enhancing the interfacial bonding strength between metal and fiber-reinforced polymer (FRP) sheets by conducting different kinds of surface treatment on metal layers to further enhance the FML mechanical properties.

**Introduction**

The growing demand in the transportation industry for reduced fuel usage and subsequent decrease in CO₂ emissions is driving the development of structural materials having a lightweight still with enough strength. FMLs are composite materials combining metal and FRP sheets, as depicted in Fig.1. Originating from the aviation sector with the primary aim of enhancing fatigue resistance, FMLs have emerged as a highly desirable structural option due to their exceptional strength-to-weight ratio [1].

<figure><img src="image_1941.png" /><figcaption>Fig. 1 Configuration of different FMLs: 2/1, 3/2, and 4/3 lay-ups [2].</figcaption></figure>

Aluminum alloy-based FMLs are extensively used in various industrial applications. The most often used types are aramid fiber-reinforced aluminum laminates (ARALL), carbon fiber-referenced aluminum laminates (CARALL), and glass fiber-referenced aluminum laminates (GLARE) [3]. The growing need for industrial application of lightweight and improved mechanical and thermal properties has led to the development of FMLs based on magnesium and titanium alloys to fulfill these requirements. Thermoset polymers have been extensively utilized

<figure><img src="image_1942.png" /></figure>

<footnote n="1">¹Department of Industrial Engineering, University of Padova, Via Venezia 1, 35131 Padova, Italy</footnote>

<footnote n="2">²Department of Mechanical, Energy and Management Engineering, University of Calabria, CS, Rende, 87036, Italy</footnote>

<footnote n="3">³Department of Engineering, LUM University, S.S. 100 Km 18, BA, Casamassima, 70010, Italy</footnote>

<footnote>ᵃzheng.liu@studenti.unipd.it, ᵇenrico.simonetto.1@unipd.it, ᶜdomenico.umbrello@unical.it,</footnote>

<footnote>ᵈrotella@lum.it</footnote>
# Enhancing interfacial bonding strength in fiber metal laminates through metal surface treatments

LIU Zheng<fnref n="1" />,ᵃ*, SIMONETTO Enrico<fnref n="1" />,ᵇ, UMBRELLO Domenico<fnref n="3" />,ᶜ, ROTELLA Giovanna<fnref n="2" />,ᵈ

**Keywords:** Fiber Metal Laminates, Metal Surface Treatment, Interfacial Bonding Strength

**Abstract** In recent decades, the aircraft industry has experienced a growing need for high-performance, lightweight structures, leading to a significant focus on developing fiber-metal laminates (FMLs). These composites possess various benefits in terms of stiffness, yield stress, fatigue, and high-velocity impact properties due to their hybrid composition. However, certain challenges related to the fabrication, surface treatment, and mechanical properties of these structures require further attention. This paper reviews FML manufacturing with a focus on enhancing the interfacial bonding strength between metal and fiber-reinforced polymer (FRP) sheets by conducting different kinds of surface treatment on metal layers to further enhance the FML mechanical properties.

**Introduction**

The growing demand in the transportation industry for reduced fuel usage and subsequent decrease in CO₂ emissions is driving the development of structural materials having a lightweight still with enough strength. FMLs are composite materials combining metal and FRP sheets, as depicted in Fig.1. Originating from the aviation sector with the primary aim of enhancing fatigue resistance, FMLs have emerged as a highly desirable structural option due to their exceptional strength-to-weight ratio [1].

<figure><img src="image_1936.png" /><figcaption>Fig. 1 Configuration of different FMLs: 2/1, 3/2, and 4/3 lay-ups [2].</figcaption></figure>

Aluminum alloy-based FMLs are extensively used in various industrial applications. The most often used types are aramid fiber-reinforced aluminum laminates (ARALL), carbon fiber-referenced aluminum laminates (CARALL), and glass fiber-referenced aluminum laminates (GLARE) [3]. The growing need for industrial application of lightweight and improved mechanical and thermal properties has led to the development of FMLs based on magnesium and titanium alloys to fulfill these requirements. Thermoset polymers have been extensively utilized

<figure><img src="image_1937.png" /></figure>

<footnote n="1">¹Department of Industrial Engineering, University of Padova, Via Venezia 1, 35131 Padova, Italy</footnote>

<footnote n="2">²Department of Mechanical, Energy and Management Engineering, University of Calabria, CS, Rende, 87036, Italy</footnote>

<footnote n="3">³Department of Engineering, LUM University, S.S. 100 Km 18, BA, Casamassima, 70010, Italy</footnote>

<footnote>ᵃzheng.liu@studenti.unipd.it, ᵇenrico.simonetto.1@unipd.it, ᶜdomenico.umbrello@unical.it,</footnote>

<footnote>ᵈrotella@lum.it</footnote>
as the matrix for CFRPs in the composite core [4-5]. However, the advantages offered by thermoplastic polymers, including decreased cycle time, recyclability, and improved compatibility with existing stamping processes [6], are positioning them as viable alternatives to thermoset ones. As a result, they have gained significant attention from both academic and industrial researchers.

Nevertheless, the utilization of these materials is constrained by several drawbacks. An important issue in the production of FMLs is the long-term strength of the connection between the FRP and metal sheets. Delamination, a primary defect affecting the service lifespan of FML components, significantly impacts their mechanical properties [7]. The phenomenon arises when the bonding strength between the metal and FRP layers is insufficient under various loading situations, such as impact, penetration, blast loading, and drilling processes [8,9]. Consequently, before the fabrication of FMLs, various pretreatments are typically undertaken to enhance the interfacial bonding strength to avoid delamination defects [10] and further enhance the impact [11] and fatigue [12] resistance of the formed components. This paper aims to offer a review of the surface treatment technologies carried out on metal sheets for improving the interfacial bonding strength of FMLs.

## Metal surface treatment methods

Several investigations on the delamination behavior of FMLs focus on various techniques to enhance the interfacial bonding strength of FMLs, primarily through pre-treatments applied to metal sheets. Three main categories of surface treatments are commonly utilized for metal sheets: (i) mechanical treatments such as abrasion [13], sandblasting [14,15] and cutting [16,17], (ii) chemical treatments including anodizing [18,19], etching [20,21], and micro-arc oxidation (MAO) [22,23] (iii) addition of interfacial layer: plasma-spray [24,25] and sol/gel methods [26,27]. The subsequent sections provide comprehensive explanations of these methods.

Mechanical treatment. Mechanical abrasion refers to the process of removing material from a surface through physical friction. This method typically involves the use of abrasive materials such as sandpaper, grinding wheels, or wire brushes, which are applied to the surface with varying levels of pressure to achieve the desired result. It can effectively remove the nonuniform oxide layer on the metal surfaces and change their surface texture. The impact of the surface morphology of the 2024-T3 aluminum alloy-based GLARE was experimentally investigated by employing sandpapers of varying grit grades (#2000, #220, and #100) to achieve different levels of roughness [13]. It was discovered that as the surface roughness increases, so does the surface energy and bonding strength, resulting in significant improvement.

Sandblasting on metal sheets is a common and effective technique for various applications, primarily in the field of surface preparation for large-scale components. When applied in the pretreatment on the metal surface of FMLs sandblasting creates a roughened and clean surface on the metal layers and enhanced surface texture promotes better adhesion between the metal and the adjacent FRP layers. Depending on the specific requirements of the FML application, the degree of roughness can be adjusted by varying the abrasive material and the blasting parameters. The effect of the change in metal surface roughness through sandblasting at different pressures was examined in [14]. Before the fabrication of FMLs via hot pressing, the surfaces of the magnesium alloy sheets underwent sandblasting at various pressures: 1, 3, and 5 bar with Fig.2 illustrating the resulting surface roughness. Metal surface roughness rises as the increase in sandblasting pressure. Interfacial bonding strength along the normal and shear directions was experimentally tested by conducting normal separation tests and single lap shear tests. The study revealed that the bonding strength exhibited an upward trend with the increasing roughness of the metal sheets.
as the matrix for CFRPs in the composite core [4-5]. However, the advantages offered by thermoplastic polymers, including decreased cycle time, recyclability, and improved compatibility with existing stamping processes [6], are positioning them as viable alternatives to thermoset ones. As a result, they have gained significant attention from both academic and industrial researchers.

Nevertheless, the utilization of these materials is constrained by several drawbacks. An important issue in the production of FMLs is the long-term strength of the connection between the FRP and metal sheets. Delamination, a primary defect affecting the service lifespan of FML components, significantly impacts their mechanical properties [7]. The phenomenon arises when the bonding strength between the metal and FRP layers is insufficient under various loading situations, such as impact, penetration, blast loading, and drilling processes [8,9]. Consequently, before the fabrication of FMLs, various pretreatments are typically undertaken to enhance the interfacial bonding strength to avoid delamination defects [10] and further enhance the impact [11] and fatigue [12] resistance of the formed components. This paper aims to offer a review of the surface treatment technologies carried out on metal sheets for improving the interfacial bonding strength of FMLs.

## Metal surface treatment methods

Several investigations on the delamination behavior of FMLs focus on various techniques to enhance the interfacial bonding strength of FMLs, primarily through pre-treatments applied to metal sheets. Three main categories of surface treatments are commonly utilized for metal sheets: (i) mechanical treatments such as abrasion [13], sandblasting [14,15] and cutting [16,17], (ii) chemical treatments including anodizing [18,19], etching [20,21], and micro-arc oxidation (MAO) [22,23] (iii) addition of interfacial layer: plasma-spray [24,25] and sol/gel methods [26,27]. The subsequent sections provide comprehensive explanations of these methods.

Mechanical treatment. Mechanical abrasion refers to the process of removing material from a surface through physical friction. This method typically involves the use of abrasive materials such as sandpaper, grinding wheels, or wire brushes, which are applied to the surface with varying levels of pressure to achieve the desired result. It can effectively remove the nonuniform oxide layer on the metal surfaces and change their surface texture. The impact of the surface morphology of the 2024-T3 aluminum alloy-based GLARE was experimentally investigated by employing sandpapers of varying grit grades (#2000, #220, and #100) to achieve different levels of roughness [13]. It was discovered that as the surface roughness increases, so does the surface energy and bonding strength, resulting in significant improvement.

Sandblasting on metal sheets is a common and effective technique for various applications, primarily in the field of surface preparation for large-scale components. When applied in the pretreatment on the metal surface of FMLs sandblasting creates a roughened and clean surface on the metal layers and enhanced surface texture promotes better adhesion between the metal and the adjacent FRP layers. Depending on the specific requirements of the FML application, the degree of roughness can be adjusted by varying the abrasive material and the blasting parameters. The effect of the change in metal surface roughness through sandblasting at different pressures was examined in [14]. Before the fabrication of FMLs via hot pressing, the surfaces of the magnesium alloy sheets underwent sandblasting at various pressures: 1, 3, and 5 bar with Fig.2 illustrating the resulting surface roughness. Metal surface roughness rises as the increase in sandblasting pressure. Interfacial bonding strength along the normal and shear directions was experimentally tested by conducting normal separation tests and single lap shear tests. The study revealed that the bonding strength exhibited an upward trend with the increasing roughness of the metal sheets.
<figure><img src="image_1943.png" /><figcaption>Fig. 2 Surface roughness of the AZ31B sheets at different pretreatments [14]</figcaption></figure>

While these processes effectively enhance metal surface roughness, it's common to apply additional chemical or electrochemical treatments following grit or sandblasting to achieve a stronger bond. They also investigated how the annealing process conducted on the sandblasted AZ31B sheets affects the interfacial bonding strength as Fig. 3 shows the surface morphology after sandblasting and annealing. The study observed the formation of an oxide layer, primarily composed of MgO/Mg(OH)₂, on the surface of AZ31B, which enhanced the metal surface roughness and facilitated chemical bonding with the polymeric molecules in the matrix. Consequently, the annealing process after sandblasting was found to further enhance the bonding strength.

<figure><img src="image_1944.png" /><figcaption>Fig. 3 SEM images of the sandblasted and annealed AZ31B sheet surfaces [14]</figcaption></figure>

The effective bonding area and mechanical interlocking between the metal and FRP layers also can be improved on a macro scale by producing scratches or grooves on metal surfaces. Several patterns of grooves with 0.1mm depth were cut on the surface of AA6082 T6 aluminum substrates in [16] as shown in Fig. 4, the results illustrate that 45°, 45°-crossed, 90° and 90°-crossed patterns can enhance the shear joint strength by 33% 45% 27% 42% respectively compared to the non-treated substrates.
<figure><img src="image_1938.png" /><figcaption>Fig. 2 Surface roughness of the AZ31B sheets at different pretreatments [14]</figcaption></figure>

While these processes effectively enhance metal surface roughness, it's common to apply additional chemical or electrochemical treatments following grit or sandblasting to achieve a stronger bond. They also investigated how the annealing process conducted on the sandblasted AZ31B sheets affects the interfacial bonding strength as Fig. 3 shows the surface morphology after sandblasting and annealing. The study observed the formation of an oxide layer, primarily composed of MgO/Mg(OH)₂, on the surface of AZ31B, which enhanced the metal surface roughness and facilitated chemical bonding with the polymeric molecules in the matrix. Consequently, the annealing process after sandblasting was found to further enhance the bonding strength.

<figure><img src="image_1939.png" /><figcaption>Fig. 3 SEM images of the sandblasted and annealed AZ31B sheet surfaces [14]</figcaption></figure>

The effective bonding area and mechanical interlocking between the metal and FRP layers also can be improved on a macro scale by producing scratches or grooves on metal surfaces. Several patterns of grooves with 0.1mm depth were cut on the surface of AA6082 T6 aluminum substrates in [16] as shown in Fig. 4, the results illustrate that 45°, 45°-crossed, 90° and 90°-crossed patterns can enhance the shear joint strength by 33% 45% 27% 42% respectively compared to the non-treated substrates.
<figure><img src="image_1945.png" /><figcaption>Fig. 4 Different patterns with a depth of 0.1 mm. (a) 0° pattern; (b) 45° pattern; (c) 45°crossed pattern; (d) 90°pattern and (e) 90°crossed pattern.[16]</figcaption></figure>

Similarly, grooves are wire cut on dual-phase steel DP980 surfaces as shown in Fig.5 to strengthen the shear joint strength between steel sheets and CFRP of the FMLs fabricated through thermoforming process. The results show that the strength of the grooved specimens is more sensitive to the initial pressure compared with the grit-blasted specimens [17].

<figure><img src="image_1946.png" /><figcaption>Fig. 5 Design of the grooves on DP980 surface [17].</figcaption></figure>

Chemical treatment. Anodization, a method employed for treating metal sheets, alters the surface microstructure to facilitate the formation of an oxide layer, which enhances wettability, consequently improving interfacial bonding strength [28]. Various chemical combinations are usually utilized in the anodization process, including chromic acid anodization and hydroxide anodization [29]. The influence of anodizing temperature on the microstructure, composition, and surface profile of Ti6Al4V was experimentally studied in [19], with Fig. 6 depicting the SEM images of Ti6Al4V surfaces after anodization for 15 min at different temperatures. Observations revealed that at 40°C, a uniform honeycomb-like oxide layer with pore diameters ranging from 100 to 200 nm developed on the surface of Ti6Al4V. Comparatively, the apparent shear strengths of specimens anodized at 0°C, 25°C, and 40°C exhibited enhancements of 217.7%, 225.0%, and 317.2% respectively, compared to those without anodization.
<figure><img src="image_1940.png" /><figcaption>Fig. 4 Different patterns with a depth of 0.1 mm. (a) 0° pattern; (b) 45° pattern; (c) 45°crossed pattern; (d) 90°pattern and (e) 90°crossed pattern.[16]</figcaption></figure>

Similarly, grooves are wire cut on dual-phase steel DP980 surfaces as shown in Fig.5 to strengthen the shear joint strength between steel sheets and CFRP of the FMLs fabricated through thermoforming process. The results show that the strength of the grooved specimens is more sensitive to the initial pressure compared with the grit-blasted specimens [17].

<figure><img src="image_1941.png" /><figcaption>Fig. 5 Design of the grooves on DP980 surface [17].</figcaption></figure>

Chemical treatment. Anodization, a method employed for treating metal sheets, alters the surface microstructure to facilitate the formation of an oxide layer, which enhances wettability, consequently improving interfacial bonding strength [28]. Various chemical combinations are usually utilized in the anodization process, including chromic acid anodization and hydroxide anodization [29]. The influence of anodizing temperature on the microstructure, composition, and surface profile of Ti6Al4V was experimentally studied in [19], with Fig. 6 depicting the SEM images of Ti6Al4V surfaces after anodization for 15 min at different temperatures. Observations revealed that at 40°C, a uniform honeycomb-like oxide layer with pore diameters ranging from 100 to 200 nm developed on the surface of Ti6Al4V. Comparatively, the apparent shear strengths of specimens anodized at 0°C, 25°C, and 40°C exhibited enhancements of 217.7%, 225.0%, and 317.2% respectively, compared to those without anodization.
<figure><img src="image_1947.png" /><figcaption>Fig. 6 SEM images of Ti6Al4V surfaces after anodization for 15 min at different temperatures: (a and b) 0 °C, (c and d) 25 °C, (e and f) 40 °C [19]</figcaption></figure>

Another approach to enhance surface roughness involves etching metal sheets with diverse acid mixtures. The degree of surface roughness is mainly dictated by the concentration levels of sodium hydroxide and hydrogen peroxide. Multi-step surface treatment namely sandblasting, anodizing, etching, and annealing was conducted on Ti6Al4V titanium alloy in [20]. The NaOH etching of the anodized sample significantly altered the surface morphology of the titanium alloy by producing nano-structured bumps that can effectively improve the bonding strength and wettability.

Micro arc oxidation (MAO) is a chemical treatment technique that can uniformly create a ceramic-like coating on the surface of metal sheets by generating sparks at high voltages. The metal surface treated with MAO forms an oxide film consisting of two layers: an exterior layer that is loose and porous, and an interior layer that is thick and dense. The outer porous layer facilitates interlocking with the resin, thereby enhancing bonding strength [22]. SEM surface morphology images of the titanium alloy after MAO surface treatment at various anodic voltages are depicted in Fig. 7. It is evident that an increase in anodic voltage leads to the formation of a thicker outer porous layer on the surface. With the anodic voltage elevated from 400 V to 600 V, the pore size escalates from 2 µm to 10 µm, enhancing surface wettability and resulting in stronger mechanical interlocking.

<figure><img src="image_1948.png" /><figcaption>Fig. 7 SEM images of Ti-MAO-400 (a), Ti-MAO-500 (b), Ti-MAO-600 (c), Ti-MAO-Step at 400, 500, and 600 V [22]</figcaption></figure>
<figure><img src="image_1942.png" /><figcaption>Fig. 6 SEM images of Ti6Al4V surfaces after anodization for 15 min at different temperatures: (a and b) 0 °C, (c and d) 25 °C, (e and f) 40 °C [19]</figcaption></figure>

Another approach to enhance surface roughness involves etching metal sheets with diverse acid mixtures. The degree of surface roughness is mainly dictated by the concentration levels of sodium hydroxide and hydrogen peroxide. Multi-step surface treatment namely sandblasting, anodizing, etching, and annealing was conducted on Ti6Al4V titanium alloy in [20]. The NaOH etching of the anodized sample significantly altered the surface morphology of the titanium alloy by producing nano-structured bumps that can effectively improve the bonding strength and wettability.

Micro arc oxidation (MAO) is a chemical treatment technique that can uniformly create a ceramic-like coating on the surface of metal sheets by generating sparks at high voltages. The metal surface treated with MAO forms an oxide film consisting of two layers: an exterior layer that is loose and porous, and an interior layer that is thick and dense. The outer porous layer facilitates interlocking with the resin, thereby enhancing bonding strength [22]. SEM surface morphology images of the titanium alloy after MAO surface treatment at various anodic voltages are depicted in Fig. 7. It is evident that an increase in anodic voltage leads to the formation of a thicker outer porous layer on the surface. With the anodic voltage elevated from 400 V to 600 V, the pore size escalates from 2 µm to 10 µm, enhancing surface wettability and resulting in stronger mechanical interlocking.

<figure><img src="image_1943.png" /><figcaption>Fig. 7 SEM images of Ti-MAO-400 (a), Ti-MAO-500 (b), Ti-MAO-600 (c), Ti-MAO-Step at 400, 500, and 600 V [22]</figcaption></figure>
Addition of interfacial layer. Plasma spraying surface treatment is a metal treatment procedure where high-pressure molten chemical powder is blasted onto the metal surface. The experimental investigation on the effect of plasma surface modification on aluminum alloy AA6061-T6 using different working gases, namely oxygen and nitrogen plasma treatment was researched in [24]. When the aluminum alloy sheet was treated with nitrogen plasma for the same duration as the oxygen plasma treatment, it resulted in a 13.7% increase in average peeling strength, a 9.4% increase in interlaminar shear strength, an 8.5% increase in tensile strength, and a 2.5% increase in flexural strength of the laminates. Another approach involves the fabrication of functional silane coatings on metal surfaces using the sol-gel technique. The sol-gel coatings consist of alkoxy groups capable of bonding with inorganic materials and functional groups that can interact with polymers. These groups can enhance adhesion on the metal-polymer interface of FMLs through the formation of covalent bonds [27].

## Conclusions

The mechanical properties of FMLs are significantly affected by interfacial bonding strength. Therefore, pretreatments should be applied on metal surfaces before their fabrication to achieve a durable bond between FRP and metal alloys. Furthermore, a suitable combination of different kinds working as a multistep method can further improve metal/FRP interfacial bonding strength.

## Acknowledgments

This research was supported by Ministry University Research within the PRIN 2022 program funded by the NextGenerationEU (project ADVANCE - ADhesiVe free Fibre Metal Laminates fabrication for aerospace applications - 2022W9SHCJ).

## References

[1] Sinmazcelik T, Avcu E, Bora MO, Coban O. A review: fibre metal laminates, background, bonding types and applied test methods, Mater. Des. 32 (7) (2011) 3671e3686. https://doi.org/10.1016/j.matdes.2011.03.011

[2] Kazemia ME, Shanmugama L, Yang L, Yang J. A review on the hybrid titanium composite laminates (HTCLs) with focuses on surface treatments, fabrications, and mechanical properties. Composites Part A 128 (2020) 105679. https://doi.org/10.1016/j.compositesa.2019.105679

[3] Sinmazçelik T, Avcu E, Bora MÖ, Çoban O. A Review: Fibre Metal Laminates, Background, Bonding Types and Applied Test Methods. Materials and Design 32 (2011) 3671-3685. https://doi.org/10.1016/j.matdes.2011.03.011

[4] Kim DH, Choi DH, Kim HS. Design optimization of a carbon fiber reinforced composite automotive lower arm. Compos B Eng 58 (2014) 400-7. https://doi.org/10.1016/j.compositesb.2013.10.067

[5] Zhu G, Wang Z, Cheng A, Li G. Design optimisation of composite bumper beam with variable cross-sections for automotive vehicle. Int J Crashworthiness 22 (2017) 365-76. https://doi.org/10.1080/13588265.2016.1267552

[6] El-Dessouky HM, Lawrence CA. Ultra-lightweight carbon fibre/thermoplastic composite material using spread tow technology. Compos B Eng 50 (2013) 91-7. https://doi.org/10.1016/j.compositesb.2013.01.026

[7] Muflikhun MA, Higuchi R, Yokozeki T, Aoki T. Delamination behavior and energy release rate evaluation of CFRP/SPCC hybrid laminates under ENF test: Corrected with residual thermal stresses. Compos Struct 236 (2020) 111890. https://doi.org/10.1016/j.compstruct.2020.111890
Addition of interfacial layer. Plasma spraying surface treatment is a metal treatment procedure where high-pressure molten chemical powder is blasted onto the metal surface. The experimental investigation on the effect of plasma surface modification on aluminum alloy AA6061-T6 using different working gases, namely oxygen and nitrogen plasma treatment was researched in [24]. When the aluminum alloy sheet was treated with nitrogen plasma for the same duration as the oxygen plasma treatment, it resulted in a 13.7% increase in average peeling strength, a 9.4% increase in interlaminar shear strength, an 8.5% increase in tensile strength, and a 2.5% increase in flexural strength of the laminates. Another approach involves the fabrication of functional silane coatings on metal surfaces using the sol-gel technique. The sol-gel coatings consist of alkoxy groups capable of bonding with inorganic materials and functional groups that can interact with polymers. These groups can enhance adhesion on the metal-polymer interface of FMLs through the formation of covalent bonds [27].

## Conclusions

The mechanical properties of FMLs are significantly affected by interfacial bonding strength. Therefore, pretreatments should be applied on metal surfaces before their fabrication to achieve a durable bond between FRP and metal alloys. Furthermore, a suitable combination of different kinds working as a multistep method can further improve metal/FRP interfacial bonding strength.

## Acknowledgments

This research was supported by Ministry University Research within the PRIN 2022 program funded by the NextGenerationEU (project ADVANCE - ADhesiVe free Fibre Metal Laminates fabricationN for aerospaCE applications - 2022W9SHCJ).

## References

[1] Sinmazcelik T, Avcu E, Bora MO, Coban O. A review: fibre metal laminates, background, bonding types and applied test methods, Mater. Des. 32 (7) (2011) 3671e3686. https://doi.org/10.1016/j.matdes.2011.03.011

[2] Kazemia ME, Shanmugama L, Yang L, Yang J. A review on the hybrid titanium composite laminates (HTCLs) with focuses on surface treatments, fabrications, and mechanical properties. Composites Part A 128 (2020) 105679. https://doi.org/10.1016/j.compositesa.2019.105679

[3] Sinmazçelik T, Avcu E, Bora MÖ, Çoban O. A Review: Fibre Metal Laminates, Background, Bonding Types and Applied Test Methods. Materials and Design 32 (2011) 3671-3685. https://doi.org/10.1016/j.matdes.2011.03.011

[4] Kim DH, Choi DH, Kim HS. Design optimization of a carbon fiber reinforced composite automotive lower arm. Compos B Eng 58 (2014) 400-7. https://doi.org/10.1016/j.compositesb.2013.10.067

[5] Zhu G, Wang Z, Cheng A, Li G. Design optimisation of composite bumper beam with variable cross-sections for automotive vehicle. Int J Crashworthiness 22 (2017) 365-76. https://doi.org/10.1080/13588265.2016.1267552

[6] El-Dessouky HM, Lawrence CA. Ultra-lightweight carbon fibre/thermoplastic composite material using spread tow technology. Compos B Eng 50 (2013) 91-7. https://doi.org/10.1016/j.compositesb.2013.01.026

[7] Muflikhun MA, Higuchi R, Yokozeki T, Aoki T. Delamination behavior and energy release rate evaluation of CFRP/SPCC hybrid laminates under ENF test: Corrected with residual thermal stresses. Compos Struct 236 (2020) 111890. https://doi.org/10.1016/j.compstruct.2020.111890
[8] Serubibi A, Hazell P J, Escobedo J P, et al. Fibre-Metal Laminate Structures: High-velocity impact, penetration, and blast loading-A review. Composites Part A 173 (2023) 107674. https://doi.org/10.1016/j.compositesa.2023.107674

[9] Doğan MA, Yazman Ş, Gemi L, Yildiz M, Yapici A. A review on drilling of FML stacks with conventional and unconventional processing methods under different conditions. Composite Structures 297 (2022) 115913. https://doi.org/10.1016/j.compstruct.2022.115913

[10] Prolongo S, Urena A. Effect of surface pre-treatment on the adhesive strength of epoxy-aluminium joints. Int J Adhes Adhes 29 (2029) 23-31. https://doi.org/10.1016/j.ijadhadh.2008.01.001

[11] Chai GB, Manikandan P. Low velocity impact response of fibre-metal laminates-a review. Compos Struct 107 (2014) 363-81. https://doi.org/10.1016/j.compstruct.2013.08.003

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[14] Liu Z, Simonetto E, Ghiotti A, Bruschi S. Experimental and numerical investigation of the effect of metal surface treatments on the delamination behaviour of magnesium alloy-based Fibre Metal Laminates. CIRP J Manuf Sci Technol 38 (2022) 442-56. https://doi.org/10.1016/j.cirpj.2022.05.015

[15] Kim Y-W. Surface modification of Ti dental implants by grit-blasting and micro-arc oxidation. Mater Manuf Proces 25 (2010) 307-10. https://doi.org/10.1080/10426911003747915

[16] Silva LFM, Ferreira NMAJ, Richter-Trummer V, Marques ESA. Effect of grooves on the strength of adhesively bonded joints. International Journal of Adhesion & Adhesives 30 (2010) 735-74. https://doi.org/10.1016/j.ijadhadh.2010.07.005

[17] Guo C, He J, Su YH, Lia SH. Thermo-stamping co-curing process for CFRP/steel hybrid sheets and its interface strength improvement. Composite Structures 241 (2020) 112108. https://doi.org/10.1016/j.compstruct.2020.112108

[18] Gemelli E, Camargo NHA. Low voltage anodization of titanium in nitric acid solution: a new method to bioactivate titanium. Mater Charac 10 (2011) 938-42. https://doi.org/10.1016/j.matchar.2011.07.004

[19] He P, Chen K, Yu B, Yue CY, Yang J. Surface microstructures and epoxy bonded shear strength of Ti6Al4V alloy anodized at various temperatures. Compos Sci Technol 82 (2013) 15-22. https://doi.org/10.1016/j.compscitech.2013.04.007

[20] He P, Chen K, Yang J. Surface modifications of Ti alloy with tunable hierarchical structures and chemistry for improved metal-polymer interface used in deepwater composite riser. Appl Surf Sci 328 (2015) 614-22. https://doi.org/10.1016/j.apsusc.2014.12.081

[21] Man H, Zhao N, Cui Z, Surface morphology of a laser surface nitrided and etched Ti-6Al-4V alloy. Surf Coat Technol 192 (2005) 341-6. https://doi.org/10.1016/j.surfcoat.2004.07.076

[22] Shanmugam L, Kazemi ME, Yang J. Improved bonding strength between thermoplastic resin and Ti alloy with surface treatments by multi-step anodization and singlestep micro-arc oxidation method: a comparative study. ES Mater Manuf 2019;3. https://doi.org/10.30919/esmm5f207
[8] Serubibi A, Hazell P J, Escobedo J P, et al. Fibre-Metal Laminate Structures: High-velocity impact, penetration, and blast loading-A review. Composites Part A 173 (2023) 107674. https://doi.org/10.1016/j.compositesa.2023.107674

[9] Doğan MA, Yazman Ş, Gemi L, Yildiz M, Yapici A. A review on drilling of FML stacks with conventional and unconventional processing methods under different conditions. Composite Structures 297 (2022) 115913. https://doi.org/10.1016/j.compstruct.2022.115913

[10] Prolongo S, Urena A. Effect of surface pre-treatment on the adhesive strength of epoxy-aluminium joints. Int J Adhes Adhes 29 (2029) 23-31. https://doi.org/10.1016/j.ijadhadh.2008.01.001

[11] Chai GB, Manikandan P. Low velocity impact response of fibre-metal laminates-a review. Compos Struct 107 (2014) 363-81. https://doi.org/10.1016/j.compstruct.2013.08.003

[12] Burianek DA, Spearing SM. Fatigue damage in titanium-graphite hybrid laminates. Compos Sci Technol 62 (2002) 607-17. https://doi.org/10.1016/S0266-3538(02)00027-1

[13] Park SY, Choi WJ, Choi HS, Kwon H. Effects of Surface Pre-treatment and Void Content on GLARE Laminate Process Characteristics. Journal of Materials Processing Technology 210 (2010) 1008-1016. https://doi.org/10.1016/j.jmatprotec.2010.01.017

[14] Liu Z, Simonetto E, Ghiotti A, Bruschi S. Experimental and numerical investigation of the effect of metal surface treatments on the delamination behaviour of magnesium alloy-based Fibre Metal Laminates. CIRP J Manuf Sci Technol 38 (2022) 442-56. https://doi.org/10.1016/j.cirpj.2022.05.015

[15] Kim Y-W. Surface modification of Ti dental implants by grit-blasting and micro-arc oxidation. Mater Manuf Proces 25 (2010) 307-10. https://doi.org/10.1080/10426911003747915

[16] Silva LFM, Ferreira NMAJ, Richter-Trummer V, Marques ESA. Effect of grooves on the strength of adhesively bonded joints. International Journal of Adhesion & Adhesives 30 (2010) 735-74. https://doi.org/10.1016/j.ijadhadh.2010.07.005

[17] Guo C, He J, Su YH, Lia SH. Thermo-stamping co-curing process for CFRP/steel hybrid sheets and its interface strength improvement. Composite Structures 241 (2020) 112108. https://doi.org/10.1016/j.compstruct.2020.112108

[18] Gemelli E, Camargo NHA. Low voltage anodization of titanium in nitric acid solution: a new method to bioactivate titanium. Mater Charac 10 (2011) 938-42. https://doi.org/10.1016/j.matchar.2011.07.004

[19] He P, Chen K, Yu B, Yue CY, Yang J. Surface microstructures and epoxy bonded shear strength of Ti6Al4V alloy anodized at various temperatures. Compos Sci Technol 82 (2013) 15-22. https://doi.org/10.1016/j.compscitech.2013.04.007

[20] He P, Chen K, Yang J. Surface modifications of Ti alloy with tunable hierarchical structures and chemistry for improved metal-polymer interface used in deepwater composite riser. Appl Surf Sci 328 (2015) 614-22. https://doi.org/10.1016/j.apsusc.2014.12.081

[21] Man H, Zhao N, Cui Z, Surface morphology of a laser surface nitrided and etched Ti-6Al-4V alloy. Surf Coat Technol 192 (2005) 341-6. https://doi.org/10.1016/j.surfcoat.2004.07.076

[22] Shanmugam L, Kazemi ME, Yang J. Improved bonding strength between thermoplastic resin and Ti alloy with surface treatments by multi-step anodization and singlestep micro-arc oxidation method: a comparative study. ES Mater Manuf 2019;3. https://doi.org/10.30919/esmm5f207
[23] Wang Y, Lei T, Jiang B, Guo L. Growth, microstructure and mechanical properties of microarc oxidation coatings on titanium alloy in phosphate-containing solution. Appl Surf Sci 233 (2004) 258-67. https://doi.org/10.1016/j.apsusc.2004.03.231

[24] Lin Y, Li H, Wang Q, Gong Z, Tao J. Effect of plasma surface treatment of aluminum alloy sheet on the properties of Al/Gf/PP laminates. Applied Surface Science 507 (2020) 145. https://doi.org/10.1016/j.apsusc.2019.145062

[25] Carradó A. Structural, microstructural, and residual stress investigations of plasma-sprayed hydroxyapatite on Ti-6Al-4 V. ACS Appl Mater Interfaces 2 (2010) 561-5. https://doi.org/10.1021/am900763j

[26] Yu C, Zhu S, Wei D, Wang F. Amorphous sol-gel SiO2 film for protection of Ti6Al4V alloy against high temperature oxidation. Surf Coat Technol. 201(2007) 5967-72. https://doi.org/10.1016/j.surfcoat.2006.11.004

[27] Surowska B, Ostapiuk M, Jakubczak P, Droździel M. The Durability of an Organic-Inorganic Sol-Gel Interlayer in Al-GFRP-CFRP Laminates in a Saline Environment, Mater, 2019. https://doi.org/10.3390/ma12152362

[28] Gemelli E, Camargo NHA. Low voltage anodization of titanium in nitric acid solution: a new method to bioactivate titanium. Mater Charact 62 (2011) 938-42. https://doi.org/10.1016/j.matchar.2011.07.004

[29] Molitor P, Barron V, Young T. Surface treatment of titanium for adhesive bonding to polymer composites: a review. Int J Adhes Adhes. 21(2001) 129-36. https://doi.org/10.1016/S0143-7496(00)00044-0
[23] Wang Y, Lei T, Jiang B, Guo L. Growth, microstructure and mechanical properties of microarc oxidation coatings on titanium alloy in phosphate-containing solution. Appl Surf Sci 233 (2004) 258-67. https://doi.org/10.1016/j.apsusc.2004.03.231

[24] Lin Y, Li H, Wang Q, Gong Z, Tao J. Effect of plasma surface treatment of aluminum alloy sheet on the properties of Al/Gf/PP laminates. Applied Surface Science 507 (2020) 145. https://doi.org/10.1016/j.apsusc.2019.145062

[25] Carradó A. Structural, microstructural, and residual stress investigations of plasma-sprayed hydroxyapatite on Ti-6Al-4 V. ACS Appl Mater Interfaces 2 (2010) 561-5. https://doi.org/10.1021/am900763j

[26] Yu C, Zhu S, Wei D, Wang F. Amorphous sol-gel SiO2 film for protection of Ti6Al4V alloy against high temperature oxidation. Surf Coat Technol. 201(2007) 5967-72. https://doi.org/10.1016/j.surfcoat.2006.11.004

[27] Surowska B, Ostapiuk M, Jakubczak P, Droździel M. The Durability of an Organic-Inorganic Sol-Gel Interlayer in Al-GFRP-CFRP Laminates in a Saline Environment, Mater, 2019. https://doi.org/10.3390/ma12152362

[28] Gemelli E, Camargo NHA. Low voltage anodization of titanium in nitric acid solution: a new method to bioactivate titanium. Mater Charact 62 (2011) 938-42. https://doi.org/10.1016/j.matchar.2011.07.004

[29] Molitor P, Barron V, Young T. Surface treatment of titanium for adhesive bonding to polymer composites: a review. Int J Adhes Adhes. 21(2001) 129-36. https://doi.org/10.1016/S0143-7496(00)00044-0
# Experimental analysis of burr formation during Ti6Al4V drilling

DEBARD Benoit¹,ª, REY Pierre-André¹,ᵇ, CHERIF Mehdi¹,ᶜ*, CHIRON Thierry¹,ᵈ, SOMMIER Alain¹,ᵉ, CHAVATTE Theo¹,ᵉ

¹I2M, Bordeaux University, 33405 Talence, France

ªbenoit.debard@ensam.eu, ᵇpierre-andre.rey@ensam.eu, ᶜmehdi.cherif@ensam.eu

ᵈthierry.chiron@u-bordeaux.fr, ᶜalain.sommier@u-bordeaux.fr, ᶜtheo.chavatte@u-bordeaux.fr

**Keywords:** Ti6Al4V, Drilling, Burr, Temperature, Wear

**Abstract.** To improve the assembly process of aeronautical structures by the mean of One-Way Assembly strategies (no deburring of metallic parts before the installation of final fasteners), it is mandatory to monitor the burr size of drilled holes. Indeed, burrs can have a significant effect on the fatigue life of structures. It was shown that cracks are initiated from exit burrs. This paper presents an experimental analysis of the burr geometry through cutting forces and thermal imaging measurements. The effect of the tool wear on burr geometry is also analyzed to identify phenomena occurring during burr formation.

**Introduction**

In order to lighten planes without reducing their mechanical properties, aeronautic industry developed hybrid structures (metal 1 with metal 2 or metal with carbon) composed of low-density material, such as Aluminum, Titanium and Carbon Fiber Reinforced Polymer (CFRP). The mechanical assembly of these structures requires to drill a lot of holes which are made through the entire stack. In metallic parts, the drilling process produces burrs at part interfaces. These burrs can reduce the fatigue life of aeronautic structures depending on their size [1,2]. As shown by Eynian et al. [3], cracks are initiated from exit burrs. So, industrials have to manage them to preserve the mechanical properties of hybrid structures.

To do this, industrials must disassemble structures, clean and deburr interfaces and then re-assemble them before installing the final fasteners. This strategy includes several non-added value phases: disassembling, deburring and re-assembling. The One-Way Assembly is an assembly strategy in which the final fastener is installed just after the hole is drilled, thus avoiding those non-added value phases. But, to deploy this strategy, it is mandatory to monitor the burr size at stack interfaces, particularly with titanium plates, where the largest burrs occur.

In the literature, most of authors only look at the burr geometry, while few of them studied the whole formation of the burr [2,4–6], whereas it is essential to fully understand burr formation phenomena. Gillespie [4] detailed formation mechanisms of both entrance and exit burrs. These burrs have different formation mechanisms, leading to two different burr morphologies [3]. The Fig. 1 shows the exit burr formation scheme during the drilling process proposed by Gillespie [4] and completed by Kim et Dornfeld [7]. According to these authors, burr formation starts at a critical thickness t₀ where the tool tip starts deforming the material of the exit surface (Fig. 1 (a)). A cap is formed in front of the tool during burr formation, and it is removed when the tool corners reach the exit surface (Fig. 1 (f)). The deformed material remaining on the part is called a “burr”.

Most of the studies on burrs are focusing on the exit burr. To understand the formation of exit burrs, several authors investigated the influence of many parameters, such as cutting conditions, material properties and cutting angles [2,4–13]. Patil et al. [12] show that titanium burr height increases with the cutting speed and the feed rate. Kim et al. [2] show that the burr type can be different between two different materials. Dornfeld et al. [6] show that the tip angle influences titanium burr size. Rimpault et al. [13] show that the axial force Fz and the torque Mz have both a

<figure><img src="image_1949.png" /></figure>
# Experimental analysis of burr formation during Ti6Al4V drilling

DEBARD Benoit¹,ª, REY Pierre-André¹,ᵇ, CHERIF Mehdi¹,ᶜ*, CHIRON Thierry¹,ᵈ, SOMMIER Alain¹,ᵉ, CHAVATTE Theo¹,ᵉ

¹I2M, Bordeaux University, 33405 Talence, France

ªbenoit.debard@ensam.eu, ᵇpierre-andre.rey@ensam.eu, ᶜmehdi.cherif@ensam.eu

ᵈthierry.chiron@u-bordeaux.fr, ᶜalain.sommier@u-bordeaux.fr, ᶜtheo.chavatte@u-bordeaux.fr

**Keywords:** Ti6Al4V, Drilling, Burr, Temperature, Wear

**Abstract.** To improve the assembly process of aeronautical structures by the mean of One-Way Assembly strategies (no deburring of metallic parts before the installation of final fasteners), it is mandatory to monitor the burr size of drilled holes. Indeed, burrs can have a significant effect on the fatigue life of structures. It was shown that cracks are initiated from exit burrs. This paper presents an experimental analysis of the burr geometry through cutting forces and thermal imaging measurements. The effect of the tool wear on burr geometry is also analyzed to identify phenomena occurring during burr formation.

**Introduction**

In order to lighten planes without reducing their mechanical properties, aeronautic industry developed hybrid structures (metal 1 with metal 2 or metal with carbon) composed of low-density material, such as Aluminum, Titanium and Carbon Fiber Reinforced Polymer (CFRP). The mechanical assembly of these structures requires to drill a lot of holes which are made through the entire stack. In metallic parts, the drilling process produces burrs at part interfaces. These burrs can reduce the fatigue life of aeronautic structures depending on their size [1,2]. As shown by Eynian et al. [3], cracks are initiated from exit burrs. So, industrials have to manage them to preserve the mechanical properties of hybrid structures.

To do this, industrials must disassemble structures, clean and deburr interfaces and then re-assemble them before installing the final fasteners. This strategy includes several non-added value phases: disassembling, deburring and re-assembling. The One-Way Assembly is an assembly strategy in which the final fastener is installed just after the hole is drilled, thus avoiding those non-added value phases. But, to deploy this strategy, it is mandatory to monitor the burr size at stack interfaces, particularly with titanium plates, where the largest burrs occur.

In the literature, most of authors only look at the burr geometry, while few of them studied the whole formation of the burr [2,4–6], whereas it is essential to fully understand burr formation phenomena. Gillespie [4] detailed formation mechanisms of both entrance and exit burrs. These burrs have different formation mechanisms, leading to two different burr morphologies [3]. The Fig. 1 shows the exit burr formation scheme during the drilling process proposed by Gillespie [4] and completed by Kim et Dornfeld [7]. According to these authors, burr formation starts at a critical thickness t₀ where the tool tip starts deforming the material of the exit surface (Fig. 1 (a)). A cap is formed in front of the tool during burr formation, and it is removed when the tool corners reach the exit surface (Fig. 1 (f)). The deformed material remaining on the part is called a “burr”.

Most of the studies on burrs are focusing on the exit burr. To understand the formation of exit burrs, several authors investigated the influence of many parameters, such as cutting conditions, material properties and cutting angles [2,4–13]. Patil et al. [12] show that titanium burr height increases with the cutting speed and the feed rate. Kim et al. [2] show that the burr type can be different between two different materials. Dornfeld et al. [6] show that the tip angle influences titanium burr size. Rimpault et al. [13] show that the axial force Fz and the torque Mz have both a

<figure><img src="image_1944.png" /></figure>
significant effect on titanium burr height. Only few authors studied the influence of temperature on burr geometry [14], but not on burr formation.

This paper presents the experimental analysis of exit burr geometry during the drilling of the titanium alloy Ti6Al4V. Cutting forces, temperature, burr height and burr width have been measured at different level of tool wear. The objective is to understand and explain the evolution of the burr geometry with tool wear by measuring the cutting forces and the temperature.

<figure><img src="image_1950.png" /><figcaption>Fig. 1: Drilling burr formation [4,7]</figcaption></figure>

## Experimental procedure

Materials. Tests were carried out on a CNC machine, using a two-lip twisted tool with a diameter of 6.35 mm, shown with Fig. 2. It is a TiAlN-coated tool, with a tip angle of 140 degrees. Holes are drilled in samples made of Ti6Al4V titanium alloy, with a thickness of 6.1 mm. All the tests were performed with the same tool.

During the tests, cutting forces were measured using the Kistler tool holder rotating dynamometer 9171A, and exit surface temperature was measured using the thermal camera FLIR A6750 SLS. Burr height and burr width were measured after the tests using the optical 3D microscope Brüker Alicona Infinite Focus G5plus.

Protocol. The experimental setup is shown with Fig. 3. In blue is the CNC spindle, where the Kistler tool holder is mounted in. Forces are measured with a frequency of 2000 Hz.

In green is the optical system that includes the infrared camera. The camera has an integration time of 0.3 s and it was calibrated with a black corpse from 50 °C to 600 °C. The camera is aligned with the tool through a golden mirror inclined at 45 degrees, which has a reflectance of 0.98. The optical path is composed of black tubes to limit as much as possible any noise during thermal measurements. At the end of the optical path, a Potassium Bromide window is placed to avoid any pollution due to chips and lubrication. The window has a transmittance of 0.9.

<figure><img src="image_1951.png" /><figcaption>Fig. 2: Tool used during the tests</figcaption></figure>

To realize an accurate measurement with a thermal camera, it is necessary to be perpendicular to what is measured. Otherwise, the emissivity will decrease and the temperature will be underestimated. But it is still possible to compare measurements with the same angle regarding the camera. So, the analysis of thermal measurements during the burr formation is still possible.

In orange is the sample support, that allows the infrared camera to target the sample exit surface. The support was designed to hold two samples: one with the exit surface painted in black (paint
significant effect on titanium burr height. Only few authors studied the influence of temperature on burr geometry [14], but not on burr formation.

This paper presents the experimental analysis of exit burr geometry during the drilling of the titanium alloy Ti6Al4V. Cutting forces, temperature, burr height and burr width have been measured at different level of tool wear. The objective is to understand and explain the evolution of the burr geometry with tool wear by measuring the cutting forces and the temperature.

<figure><img src="image_1945.png" /><figcaption>Fig. 1: Drilling burr formation [4,7]</figcaption></figure>

## Experimental procedure

Materials. Tests were carried out on a CNC machine, using a two-lip twisted tool with a diameter of 6.35 mm, shown with Fig. 2. It is a TiAlN-coated tool, with a tip angle of 140 degrees. Holes are drilled in samples made of Ti6Al4V titanium alloy, with a thickness of 6.1 mm. All the tests were performed with the same tool.

During the tests, cutting forces were measured using the Kistler tool holder rotating dynamometer 9171A, and exit surface temperature was measured using the thermal camera FLIR A6750 SLS. Burr height and burr width were measured after the tests using the optical 3D microscope Brüker Alicona Infinite Focus G5plus.

Protocol. The experimental setup is shown with Fig. 3. In blue is the CNC spindle, where the Kistler tool holder is mounted in. Forces are measured with a frequency of 2000 Hz.

In green is the optical system that includes the infrared camera. The camera has an integration time of 0.3 s and it was calibrated with a black corpse from 50 °C to 600 °C. The camera is aligned with the tool through a golden mirror inclined at 45 degrees, which has a reflectance of 0.98. The optical path is composed of black tubes to limit as much as possible any noise during thermal measurements. At the end of the optical path, a Potassium Bromide window is placed to avoid any pollution due to chips and lubrication. The window has a transmittance of 0.9.

<figure><img src="image_1946.png" /><figcaption>Fig. 2: Tool used during the tests</figcaption></figure>

To realize an accurate measurement with a thermal camera, it is necessary to be perpendicular to what is measured. Otherwise, the emissivity will decrease and the temperature will be underestimated. But it is still possible to compare measurements with the same angle regarding the camera. So, the analysis of thermal measurements during the burr formation is still possible.

In orange is the sample support, that allows the infrared camera to target the sample exit surface. The support was designed to hold two samples: one with the exit surface painted in black (paint
RAL9005), and the other without paint. The black paint allows a better measurement of the temperature. Indeed, it has a reflectance inferior to 0.1, so an emissivity superior to 0.9 [15]. But, as the black paint has a non-neglectable thickness, it perturbate burr size measurements. To avoid these uncertainties, burr measurements were done only on the virgin sample, and thermal measurements only on the painted sample. Both samples have a thickness of 6.1 mm.

Tests are composed of two main phases: the “Measure” phase and the “Wear” phase. The tool wear is considered neglectable during the “Measure” phase, while the purpose of the “Wear” phase is to wear the tool. During the “Measure” phase, tests are performed at 25 m/min cutting speed and a feed rate of 0.06 mm/rev with MQL lubrication. Eight holes are performed, four on the painted sample and four on the virgin sample. Forces and temperature are measured during this phase. During the “Wear” phase, tests are performed with the same feed rate and lubrication, but at a cutting speed of 40 m/min. The sample used is 20 mm thick and 18 holes are drilled on it. Only forces are recorded during this phase.

The “Measure” phase was performed for four wear levels of the tool. The first level corresponds to a new tool. The wear level increases by 1 for each “Wear” phase performed (wear level 2 = 1 “Wear” phase performed, etc...). The last “Measure” phase was performed after 3 “Wear” phase. As the thickness of the sample is not the same during the “Wear” phase and the “Measure” phase, the wear is represented by the cutting length in the results section. The protocol is resumed with the Fig. 4.

<figure><img src="image_1952.png" /><figcaption>Fig. 3: Experimental setup</figcaption></figure>
RAL9005), and the other without paint. The black paint allows a better measurement of the temperature. Indeed, it has a reflectance inferior to 0.1, so an emissivity superior to 0.9 [15]. But, as the black paint has a non-neglectable thickness, it perturbate burr size measurements. To avoid these uncertainties, burr measurements were done only on the virgin sample, and thermal measurements only on the painted sample. Both samples have a thickness of 6.1 mm.

Tests are composed of two main phases: the “Measure” phase and the “Wear” phase. The tool wear is considered neglectable during the “Measure” phase, while the purpose of the “Wear” phase is to wear the tool. During the “Measure” phase, tests are performed at 25 m/min cutting speed and a feed rate of 0.06 mm/rev with MQL lubrication. Eight holes are performed, four on the painted sample and four on the virgin sample. Forces and temperature are measured during this phase. During the “Wear” phase, tests are performed with the same feed rate and lubrication, but at a cutting speed of 40 m/min. The sample used is 20 mm thick and 18 holes are drilled on it. Only forces are recorded during this phase.

The “Measure” phase was performed for four wear levels of the tool. The first level corresponds to a new tool. The wear level increases by 1 for each “Wear” phase performed (wear level 2 = 1 “Wear” phase performed, etc...). The last “Measure” phase was performed after 3 “Wear” phase. As the thickness of the sample is not the same during the “Wear” phase and the “Measure” phase, the wear is represented by the cutting length in the results section. The protocol is resumed with the Fig. 4.

<figure><img src="image_1947.png" /><figcaption>Fig. 3: Experimental setup</figcaption></figure>
<figure><img src="image_1953.png" /><figcaption>Fig. 4: Tests protocol</figcaption></figure>

## Results and Discussion

In this section, results of tool wear, cutting forces, exit surface temperature and burr geometry measurements are first presented separately. Then, a correlation between all these results is established.

Tool wear. The Fig. 5 shows the tool at each wear level. It shows that the main wear mode is adhesive wear. Indeed, it is possible to notice a lot of material adhesion at tool corners, forming a built-up-edge (marked in red in Fig. 5). This built-up-edge modifies significantly the tool macro-geometry at corners and margins. Indeed, the tip length increases from 1.05 mm (wear level 1) to 1.2 mm (wear level 4). Moreover, the tool wear is not symmetrical, leading to cutting tool imbalance.

This figure shows that the tool tip and the middle of the cutting edges have no significant wear. Indeed, at point M (marked in green in Fig. 5), the edge acuity increases slightly from 6 µm to 8 µm. Moreover, no wear can be observed on the flank or rake face, whatever the wear level. Some material is stuck on the tool tip, but not enough to modify significantly the tool macro-geometry.

Cutting forces. The curves of the axial force $F_z$ and the torque $M_z$ for all wear levels are represented on Fig. 6. The X-axis was converted from time to depth of the tool tip. This new scale gives a better understanding of the cutting process with regard to the position of the tool in the material. This figure shows a good repeatability of the drilling at each wear level. At wear level 1, the torque $M_z$ shows a change after few holes, due to the running-in effect.

The entrance phase shows no evolution at the tool tip between wear levels, but the corners contribution to forces increases a lot with tool wear. The built-up-edge at tool corners changes locally the tool macro-geometry. Thus, the edge acuity and the tool-workpiece contact are modified, leading to an increase of cutting forces.
<figure><img src="image_1948.png" /><figcaption>Fig. 4: Tests protocol</figcaption></figure>

## Results and Discussion

In this section, results of tool wear, cutting forces, exit surface temperature and burr geometry measurements are first presented separately. Then, a correlation between all these results is established.

Tool wear. The Fig. 5 shows the tool at each wear level. It shows that the main wear mode is adhesive wear. Indeed, it is possible to notice a lot of material adhesion at tool corners, forming a built-up-edge (marked in red in Fig. 5). This built-up-edge modifies significantly the tool macro-geometry at corners and margins. Indeed, the tip length increases from 1.05 mm (wear level 1) to 1.2 mm (wear level 4). Moreover, the tool wear is not symmetrical, leading to cutting tool imbalance.

This figure shows that the tool tip and the middle of the cutting edges have no significant wear. Indeed, at point M (marked in green in Fig. 5), the edge acuity increases slightly from 6 µm to 8 µm. Moreover, no wear can be observed on the flank or rake face, whatever the wear level. Some material is stuck on the tool tip, but not enough to modify significantly the tool macro-geometry.

Cutting forces. The curves of the axial force $F_z$ and the torque $M_z$ for all wear levels are represented on Fig. 6. The X-axis was converted from time to depth of the tool tip. This new scale gives a better understanding of the cutting process with regard to the position of the tool in the material. This figure shows a good repeatability of the drilling at each wear level. At wear level 1, the torque $M_z$ shows a change after few holes, due to the running-in effect.

The entrance phase shows no evolution at the tool tip between wear levels, but the corners contribution to forces increases a lot with tool wear. The built-up-edge at tool corners changes locally the tool macro-geometry. Thus, the edge acuity and the tool-workpiece contact are modified, leading to an increase of cutting forces.
<figure><img src="image_1954.png" /><figcaption>Fig. 5: Top view of the tool at wear level 1 (a), 2 (b), 3 (c) and 4 (d)</figcaption></figure>

This figure also shows that the end of the cutting goes deeper with the tool wear. At wear level 1, the cap breakage occurs at 7.2 mm depth, while at wear level 4, it occurs at 8 mm. The increase of exit length (0.8 mm) is superior to the increase of tip length (0.15 mm), thus the modification of the macro-geometry is not enough to explain this phenomenon. This point is discussed in more detail in the temperature results section.

The Fig. 7 shows the evolution of force amplitude with tool wear. The axial force $F_z$ amplitude was multiplied by 2 between wear levels 1 and 4, while the torque $M_z$ amplitude was multiplied by about 2.5. These curves show no significant evolution between wear levels 2 and 3. The axial force $F_z$ amplitude did not change while the torque $M_z$ amplitude increased slightly. The measured torque $M_z$ depends on several phenomena, such as friction or cutting. The Fig. 5 shows that margins get more wear than corners between wear levels 2 and 3. As margins contribute mainly to the torque $M_z$, the torque increases more than the axial force $F_z$.

Temperature. The Fig. 8 (a) shows the evolution with tool wear of the maximal temperature reached during burr formation (square mark), at tool tip (triangle mark) and at tool corners (round mark). This figure shows a good repeatability of measurements for each wear level. The evolution of the temperature is more closed to the torque $M_z$ than the axial force $F_z$, as the temperature in front of the tool is mainly due to friction.

It also shows that the temperature at tool tip does not increase a lot, while the temperature at tool corners increases a lot, as corners wear faster than the tip. Thus, the maximal temperature is firstly reached near the tool tip (wear level 1), and then near tool corners (wear level 4). The maximal temperature at tip increases by 50°C, while it increases by 300°C at corners. The increase of temperature at tool corners could induce a thermal softening of the material during burr
<figure><img src="image_1949.png" /><figcaption>Fig. 5: Top view of the tool at wear level 1 (a), 2 (b), 3 (c) and 4 (d)</figcaption></figure>

This figure also shows that the end of the cutting goes deeper with the tool wear. At wear level 1, the cap breakage occurs at 7.2 mm depth, while at wear level 4, it occurs at 8 mm. The increase of exit length (0.8 mm) is superior to the increase of tip length (0.15 mm), thus the modification of the macro-geometry is not enough to explain this phenomenon. This point is discussed in more detail in the temperature results section.

The Fig. 7 shows the evolution of force amplitude with tool wear. The axial force $F_z$ amplitude was multiplied by 2 between wear levels 1 and 4, while the torque $M_z$ amplitude was multiplied by about 2.5. These curves show no significant evolution between wear levels 2 and 3. The axial force $F_z$ amplitude did not change while the torque $M_z$ amplitude increased slightly. The measured torque $M_z$ depends on several phenomena, such as friction or cutting. The Fig. 5 shows that margins get more wear than corners between wear levels 2 and 3. As margins contribute mainly to the torque $M_z$, the torque increases more than the axial force $F_z$.

Temperature. The Fig. 8 (a) shows the evolution with tool wear of the maximal temperature reached during burr formation (square mark), at tool tip (triangle mark) and at tool corners (round mark). This figure shows a good repeatability of measurements for each wear level. The evolution of the temperature is more closed to the torque $M_z$ than the axial force $F_z$, as the temperature in front of the tool is mainly due to friction.

It also shows that the temperature at tool tip does not increase a lot, while the temperature at tool corners increases a lot, as corners wear faster than the tip. Thus, the maximal temperature is firstly reached near the tool tip (wear level 1), and then near tool corners (wear level 4). The maximal temperature at tip increases by 50°C, while it increases by 300°C at corners. The increase of temperature at tool corners could induce a thermal softening of the material during burr
formation. This softening could explain the increase of exit length observed on cutting force measurements.

<figure><img src="image_1955.png" /><figcaption>Fig. 6: Axial force **F**<sub>z</sub> (a) and Torque **M**<sub>z</sub> (b) with tool tip depth **Z** for all wear levels</figcaption></figure>

<figure><img src="image_1956.png" /><figcaption>Fig. 7: Axial force **F**<sub>z</sub> (a) and Torque **M**<sub>z</sub> (b) with tool wear</figcaption></figure>

For each hole, the evolution of the temperature along the hole diameter (see Fig. 8 (b)) has been plotted. Fig. 8 (c) and (d) show respectively the temperature radial profile at 5.5 mm depth, before burr formation, and just before the cap breakage, at the end of burr formation, at wear level 4. Fig. 9 and Fig. 10 are obtained by plotting the temperature radial profile at each depth step on the same graph for respectively wear level 1 and 4. To facilitate the analysis of these figures, the tool tip is marked in magenta and the corners in red. These figures confirm what was told before: when the tool gets wear, the maximal temperature is reached at tool corners.

On these figures, it is possible to notice the particular shape of the profile during the burr formation. The abrupt decrease of temperature corresponds to the cap breakage, which means the end of burr formation. Just before the breakage (see Fig. 8 (d)), the temperature reaches a maximum at tool corners, and a minimum at tool tip. According to Kim et Dornfeld [7], at the end of burr formation, there is no more cutting, only material deformation. Thus, the heat is only generated by the friction between the tool and the material. As the speed is higher at corners than at tool tip, there is more heat generated at corners. That explains the particular shape of the temperature radial profile before the cap breakage.
formation. This softening could explain the increase of exit length observed on cutting force measurements.

<figure><img src="image_1950.png" /><figcaption>Fig. 6: Axial force **F**<sub>z</sub> (a) and Torque **M**<sub>z</sub> (b) with tool tip depth **Z** for all wear levels</figcaption></figure>

<figure><img src="image_1951.png" /><figcaption>Fig. 7: Axial force **F**<sub>z</sub> (a) and Torque **M**<sub>z</sub> (b) with tool wear</figcaption></figure>

For each hole, the evolution of the temperature along the hole diameter (see Fig. 8 (b)) has been plotted. Fig. 8 (c) and (d) show respectively the temperature radial profile at 5.5 mm depth, before burr formation, and just before the cap breakage, at the end of burr formation, at wear level 4. Fig. 9 and Fig. 10 are obtained by plotting the temperature radial profile at each depth step on the same graph for respectively wear level 1 and 4. To facilitate the analysis of these figures, the tool tip is marked in magenta and the corners in red. These figures confirm what was told before: when the tool gets wear, the maximal temperature is reached at tool corners.

On these figures, it is possible to notice the particular shape of the profile during the burr formation. The abrupt decrease of temperature corresponds to the cap breakage, which means the end of burr formation. Just before the breakage (see Fig. 8 (d)), the temperature reaches a maximum at tool corners, and a minimum at tool tip. According to Kim et Dornfeld [7], at the end of burr formation, there is no more cutting, only material deformation. Thus, the heat is only generated by the friction between the tool and the material. As the speed is higher at corners than at tool tip, there is more heat generated at corners. That explains the particular shape of the temperature radial profile before the cap breakage.
<figure><img src="image_1957.png" /><figcaption>Fig. 8: Maximal temperature reached during burr formation, at tip and at corners with tool wear (a), Radial profile on the exit surface (b), Temperature along the radial profile at 5.5 mm depth (c) and just before the cap breakage (d)</figcaption></figure>

<figure><img src="image_1958.png" /><figcaption>Fig. 9: Temperature along the radial profile with depth at wear level 1</figcaption></figure>
<figure><img src="image_1952.png" /><figcaption>Fig. 8: Maximal temperature reached during burr formation, at tip and at corners with tool wear (a), Radial profile on the exit surface (b), Temperature along the radial profile at 5.5 mm depth (c) and just before the cap breakage (d)</figcaption></figure>

<figure><img src="image_1953.png" /><figcaption>Fig. 9: Temperature along the radial profile with depth at wear level 1</figcaption></figure>
<figure><img src="image_1959.png" /><figcaption>Fig. 10: Temperature along the radial profile with depth at wear level 4</figcaption></figure>

Burr geometry. The Fig. 11 shows the evolution of the burr height and the burr width with tool wear. As burrs are not always regular around the hole, median values are presented on this figure. The error bars correspond to the burr size standard deviation. They are an indicator of the regularity of the burr around the hole.

This figure shows a good repeatability of the results for each wear level. However, the more the burr size goes up, the less it is regular around the hole, especially in terms of width. Because of the non-symmetric wear, the cutting becomes less stable, inducing non regular burrs.

This figure shows that the evolution of the burr height is closer to the evolution of the axial force $F_z$, while the burr width evolution is closer to the torque $M_z$. Indeed, the burr height doesn't change between wear level 2 and 3, while the width increases. That shows that the burr height seems less influenced by the torque $M_z$ than the width. As the tool margins are modified due to material adhesion, the way the tool pushes the material perpendicularly to the feed changes too, and so the burr width. The burr height seems not influenced by the margins. That leads to an increase in torque $M_z$ and radial forces. To verify this hypothesis, it is necessary to measure the radial forces.

<figure><img src="image_1960.png" /><figcaption>Fig. 11: Median burr height (a) and median burr width (b) with tool wear</figcaption></figure>
<figure><img src="image_1954.png" /><figcaption>Fig. 10: Temperature along the radial profile with depth at wear level 4</figcaption></figure>

Burr geometry. The Fig. 11 shows the evolution of the burr height and the burr width with tool wear. As burrs are not always regular around the hole, median values are presented on this figure. The error bars correspond to the burr size standard deviation. They are an indicator of the regularity of the burr around the hole.

This figure shows a good repeatability of the results for each wear level. However, the more the burr size goes up, the less it is regular around the hole, especially in terms of width. Because of the non-symmetric wear, the cutting becomes less stable, inducing non regular burrs.

This figure shows that the evolution of the burr height is closer to the evolution of the axial force $F_z$, while the burr width evolution is closer to the torque $M_z$. Indeed, the burr height doesn't change between wear level 2 and 3, while the width increases. That shows that the burr height seems less influenced by the torque $M_z$ than the width. As the tool margins are modified due to material adhesion, the way the tool pushes the material perpendicularly to the feed changes too, and so the burr width. The burr height seems not influenced by the margins. That leads to an increase in torque $M_z$ and radial forces. To verify this hypothesis, it is necessary to measure the radial forces.

<figure><img src="image_1955.png" /><figcaption>Fig. 11: Median burr height (a) and median burr width (b) with tool wear</figcaption></figure>
## Conclusion

The work presented in this paper shows the influence of the tool wear on burrs by measuring cutting forces and temperature. The main wear mode is material adhesion on the cutting edge, mainly at tool corners. This leads to a modification of the tool macro-geometry. The results are synthetized with the Fig. 12.

The thermal measurements show interesting results. At the end of burr formation, the maximal temperature is located at tool corners rather than the tip. Due to the wear, the temperature at corners increases by more than 150 %. At the same time, the burr height increases by 1125 % and the width by 1134 %. In addition to the modification of the tool macro-geometry, a local thermal softening of the material at tool corners can explain the increase of burr size.

The cutting forces measurements show that the burr width seems to be greatly influenced by the torque $M_z$, and the tool margins. The burr height seems more influenced by the axial force $F_z$, but not by the margins.

<figure><img src="image_1961.png" /><figcaption>Fig. 12: Synthesis of the results</figcaption></figure>

## Acknowledgements

The authors would like to thank the TREFLE department of the laboratory I2M Bordeaux for the supply of the thermal camera, and their help for the preparation of the camera and the treatment of the results.

The authors would also like to thank Airbus Operations SAS for financing the experiments and supplying the tooling and the workpiece material.

## References

[1] A.M. Abdelhafeez, S.L. Soo, D.K. Aspinwall, A. Dowson, D. Arnold, The influence of burr formation and feed rate on the fatigue life of drilled titanium and aluminium alloys used in aircraft manufacture, CIRP Annals 67 (2018) 103–108. https://doi.org/10.1016/j.cirp.2018.03.013

[2] J. Kim, S. Min, D.A. Dornfeld, Optimization and control of drilling burr formation of AISI 304L and AISI 4118 based on drilling burr control charts, International Journal of Machine Tools and Manufacture 41 (2001) 923–936. https://doi.org/10.1016/S0890-6955(00)00131-0

[3] M. Eynian, K. Das, A. Wretland, Effect of tool wear on quality in drilling of titanium alloy Ti6Al4V, Part I: Cutting Forces, Burr Formation, Surface Quality and Defects, High Speed Machining 3 (2017) 1–10. https://doi.org/10.1515/hsm-2017-0001
**Conclusion**

The work presented in this paper shows the influence of the tool wear on burrs by measuring cutting forces and temperature. The main wear mode is material adhesion on the cutting edge, mainly at tool corners. This leads to a modification of the tool macro-geometry. The results are synthetized with the Fig. 12.

The thermal measurements show interesting results. At the end of burr formation, the maximal temperature is located at tool corners rather than the tip. Due to the wear, the temperature at corners increases by more than 150 %. At the same time, the burr height increases by 1125 % and the width by 1134 %. In addition to the modification of the tool macro-geometry, a local thermal softening of the material at tool corners can explain the increase of burr size.

The cutting forces measurements show that the burr width seems to be greatly influenced by the torque Mz, and the tool margins. The burr height seems more influenced by the axial force Fz, but not by the margins.

<figure><img src="image_1956.png" /><figcaption>Fig. 12: Synthesis of the results</figcaption></figure>

**Acknowledgements**

The authors would like to thank the TREFLE department of the laboratory I2M Bordeaux for the supply of the thermal camera, and their help for the preparation of the camera and the treatment of the results.

The authors would also like to thank Airbus Operations SAS for financing the experiments and supplying the tooling and the workpiece material.

**References**

[1] A.M. Abdelhafeez, S.L. Soo, D.K. Aspinwall, A. Dowson, D. Arnold, The influence of burr formation and feed rate on the fatigue life of drilled titanium and aluminium alloys used in aircraft manufacture, CIRP Annals 67 (2018) 103–108. https://doi.org/10.1016/j.cirp.2018.03.013

[2] J. Kim, S. Min, D.A. Dornfeld, Optimization and control of drilling burr formation of AISI 304L and AISI 4118 based on drilling burr control charts, International Journal of Machine Tools and Manufacture 41 (2001) 923–936. https://doi.org/10.1016/S0890-6955(00)00131-0

[3] M. Eynian, K. Das, A. Wretland, Effect of tool wear on quality in drilling of titanium alloy Ti6Al4V, Part I: Cutting Forces, Burr Formation, Surface Quality and Defects, High Speed Machining 3 (2017) 1–10. https://doi.org/10.1515/hsm-2017-0001
[4] L.K. Gillespie, Burrs produced by drilling, Bendix Corp., Kansas City, Mo. (USA), 1976. https://doi.org/10.2172/7351762

[5] S. Min, D.A. Dornfeld, J. Kim, B. Shyu, FINITE ELEMENT MODELING OF BURR FORMATION IN METAL CUTTING, Machining Science and Technology 5 (2001) 307–322. https://doi.org/10.1081/MST-100108617

[6] D.A. Dornfeld, J.S. Kim, H. Dechow, J. Hewson, L.J. Chen, Drilling Burr Formation in Titanium Alloy, Ti-6Al-4V, (1999).

[7] J. Kim, D. Dornfeld, Development of an Analytical Model for Drilling Burr Formation in Ductile Materials, (2002). https://escholarship.org/uc/item/1cq5k23b (accessed April 4, 2022).

[8] R. Hussein, Vibration Assisted Drilling of Carbon Fiber Reinforced Polymer and Titanium Alloy for Aerospace Application, Thesis, 2019. https://macsphere.mcmaster.ca/handle/11375/24730 (accessed April 4, 2022).

[9] L.K. Lauderbaugh, Analysis of the effects of process parameters on exit burrs in drilling using a combined simulation and experimental approach, Journal of Materials Processing Technology 209 (2009) 1909–1919. https://doi.org/10.1016/j.jmatprotec.2008.04.062

[10] R. Link, Gratbildung und Strategien zur Gratreduzierung bei der Zerspanung mit geometrisch bestimmter Schneide, RWTH Aachen, 1992.

[11] Y.B. Guo, D.A. Dornfeld, Finite Element Modeling of Burr Formation Process in Drilling 304 Stainless Steel, Journal of Manufacturing Science and Engineering 122 (2000) 612–619. https://doi.org/10.1115/1.1285885

[12] R. Patil, S. Shinde, D. Marla, S. Joshi, Experimental analysis of burr formation in drilling of Ti-6Al-4V alloy, IJMMS 9 (2016) 237. https://doi.org/10.1504/IJMMS.2016.079591

[13] X. Rimpault, J.-F. Chatelain, J.E. Klemberg-Sapieha, M. Balazinski, Burr height study for drilling carbon epoxy composite / titanium / aluminium stacks, Proceedings of Joint Conference on Mechanical, Design Engineering & Advanced Manufacturing (2014).

[14] I. Rodriguez, P.J. Arrazola, M. Cuesta, F. Pušavec, Hole quality improvement in CFRP/Ti6Al4V stacks using optimised flow rates for LCO2 and MQL sustainable cooling/lubrication, Composite Structures 329 (2024) 117687. https://doi.org/10.1016/j.compstruct.2023.117687

[15] S. Potin, Spectrophotométrie de la matière extra-terrestre, (2020).
[4] L.K. Gillespie, Burrs produced by drilling, Bendix Corp., Kansas City, Mo. (USA), 1976. https://doi.org/10.2172/7351762

[5] S. Min, D.A. Dornfeld, J. Kim, B. Shyu, FINITE ELEMENT MODELING OF BURR FORMATION IN METAL CUTTING, Machining Science and Technology 5 (2001) 307–322. https://doi.org/10.1081/MST-100108617

[6] D.A. Dornfeld, J.S. Kim, H. Dechow, J. Hewson, L.J. Chen, Drilling Burr Formation in Titanium Alloy, Ti-6Al-4V, (1999).

[7] J. Kim, D. Dornfeld, Development of an Analytical Model for Drilling Burr Formation in Ductile Materials, (2002). https://escholarship.org/uc/item/1cq5k23b (accessed April 4, 2022).

[8] R. Hussein, Vibration Assisted Drilling of Carbon Fiber Reinforced Polymer and Titanium Alloy for Aerospace Application, Thesis, 2019. https://macsphere.mcmaster.ca/handle/11375/24730 (accessed April 4, 2022).

[9] L.K. Lauderbaugh, Analysis of the effects of process parameters on exit burrs in drilling using a combined simulation and experimental approach, Journal of Materials Processing Technology 209 (2009) 1909–1919. https://doi.org/10.1016/j.jmatprotec.2008.04.062

[10] R. Link, Gratbildung und Strategien zur Gratreduzierung bei der Zerspanung mit geometrisch bestimmter Schneide, RWTH Aachen, 1992.

[11] Y.B. Guo, D.A. Dornfeld, Finite Element Modeling of Burr Formation Process in Drilling 304 Stainless Steel, Journal of Manufacturing Science and Engineering 122 (2000) 612–619. https://doi.org/10.1115/1.1285885

[12] R. Patil, S. Shinde, D. Marla, S. Joshi, Experimental analysis of burr formation in drilling of Ti-6Al-4V alloy, IJMMS 9 (2016) 237. https://doi.org/10.1504/IJMMS.2016.079591

[13] X. Rimpault, J.-F. Chatelain, J.E. Klemberg-Sapieha, M. Balazinski, Burr height study for drilling carbon epoxy composite / titanium / aluminium stacks, Proceedings of Joint Conference on Mechanical, Design Engineering & Advanced Manufacturing (2014).

[14] I. Rodriguez, P.J. Arrazola, M. Cuesta, F. Pušavec, Hole quality improvement in CFRP/Ti6Al4V stacks using optimised flow rates for LCO2 and MQL sustainable cooling/lubrication, Composite Structures 329 (2024) 117687. https://doi.org/10.1016/j.compstruct.2023.117687

[15] S. Potin, Spectrophotométrie de la matière extra-terrestre, (2020).
# Applying images processing methods for automation measurement of tool-chip contact length in orthogonal cutting

FAVIER Camille¹,ᵃ, LE ROUX Julien¹,ᵇ, CALAMAZ Madalina¹,ᶜ*,
GIRARDOT Jérémie¹,ᵈ and LIMJE Preshit¹,ᵉ

¹Arts et Metiers Institute of Technology, CNRS, Bordeaux INP, Hesam University, I2M, UMR 5295, F-33400, Talence, France

ᵃcamille.favier@ensam.eu, ᵇjulien.le_roux_1@ensam.eu, ᶜmadalina.calamaz@ensam.eu, ᵈjeremie.girardot@ensam.eu, ᵉpreshitlimje24@gmail.com

**Keywords:** Chip Formation, Images Processing, Tool-Chip Contact Length

**Abstract.** The simulation of machining process is an essential tool in the digitalization of the entire production chain. Currently, these simulations are not sufficiently precise to avoid the use of experimental tests in order to optimize machining operations and guarantee the quality of the machined parts. Some parameters, such as tool-chip contact length, are still underestimated, although they are critical for controlling heat transfer into the tool and implicitly its wear. In order to validate a numerical cutting simulation model, the tool-chip contact length experimentally measured should be used as a comparative quantity, in the same way as the cutting forces and the morphology of the chips is currently used. The objective of this paper is to propose an automation of tool-chip contact length measurements using image processing algorithms. The proposed algorithm was able to identify and measure the tool-chip contact length on more that 75% of images. The algorithm accuracy is evaluated by comparing computed and manually measured tool-chip contact length, for different cutting conditions. It was found that it overestimates the contact length, especially in the case where the image quality is lower.

**Introduction**

Machining simulations are pivotal in the digital transformation of the entire production chain. However, current simulations lack the precision to replace experimental tests for optimizing operations and ensuring the quality of machined parts. The use of cutting forces and chips morphology experimentally obtained has become a commonly implemented practice to validate numerical simulation models of orthogonal cutting. Certain parameters, notably measured tool-chip contact lengths, are less often used for comparison in numerical simulations, and when they are used, it turns out to remain underestimated. Particular attention should be paid to this metric, as it plays a critical role in controlling heat transfer into the tool and its consequent wear. To validate numerical cutting simulation models, experimentally measured tool-chip contact length should be utilized as a comparative metric, similar to how cutting forces and chip morphology are presently employed.

Tool-chip contact length (Lc) is measured in two different ways: i) by measuring the imprint left by the chip on the tool rake face or ii) on images from video recording of cutting process. Storchak et al. [1] carried out orthogonal cutting tests on an AISI 1045 steel workpiece using various cutting speeds, uncut chip thicknesses and tool rake angles. Cutting forces and tool-chip contact length were measured and compared to those provided by numerical simulation of cutting. The purpose of their study was to find out the main parameters of the material behaviour law used in numerical simulation of cutting that influence the prediction of Lc. The chip formation during cutting was recorded using a video camera. Contact length was manually measured on images and associated numerical model served as a benchmark for comprehending variations in Lc. Sutter [2] delved into chip geometries during high-speed machining, employing a ballistic device for

<figure><img src="image_1962.png" /></figure>
# Applying images processing methods for automation measurement of tool-chip contact length in orthogonal cutting

FAVIER Camille¹,ᵃ, LE ROUX Julien¹,ᵇ, CALAMAZ Madalina¹,ᶜ*,
GIRARDOT Jérémie¹,ᵈ and LIMJE Preshit¹,ᵉ

¹Arts et Metiers Institute of Technology, CNRS, Bordeaux INP, Hesam University, I2M, UMR 5295, F-33400, Talence, France

ᵃcamille.favier@ensam.eu, ᵇjulien.le_roux_1@ensam.eu, ᶜmadalina.calamaz@ensam.eu, ᵈjeremie.girardot@ensam.eu, ᵉpreshitlimje24@gmail.com

**Keywords:** Chip Formation, Images Processing, Tool-Chip Contact Length

**Abstract.** The simulation of machining process is an essential tool in the digitalization of the entire production chain. Currently, these simulations are not sufficiently precise to avoid the use of experimental tests in order to optimize machining operations and guarantee the quality of the machined parts. Some parameters, such as tool-chip contact length, are still underestimated, although they are critical for controlling heat transfer into the tool and implicitly its wear. In order to validate a numerical cutting simulation model, the tool-chip contact length experimentally measured should be used as a comparative quantity, in the same way as the cutting forces and the morphology of the chips is currently used. The objective of this paper is to propose an automation of tool-chip contact length measurements using image processing algorithms. The proposed algorithm was able to identify and measure the tool-chip contact length on more that 75% of images. The algorithm accuracy is evaluated by comparing computed and manually measured tool-chip contact length, for different cutting conditions. It was found that it overestimates the contact length, especially in the case where the image quality is lower.

**Introduction**

Machining simulations are pivotal in the digital transformation of the entire production chain. However, current simulations lack the precision to replace experimental tests for optimizing operations and ensuring the quality of machined parts. The use of cutting forces and chips morphology experimentally obtained has become a commonly implemented practice to validate numerical simulation models of orthogonal cutting. Certain parameters, notably measured tool-chip contact lengths, are less often used for comparison in numerical simulations, and when they are used, it turns out to remain underestimated. Particular attention should be paid to this metric, as it plays a critical role in controlling heat transfer into the tool and its consequent wear. To validate numerical cutting simulation models, experimentally measured tool-chip contact length should be utilized as a comparative metric, similar to how cutting forces and chip morphology are presently employed.

Tool-chip contact length (Lc) is measured in two different ways: i) by measuring the imprint left by the chip on the tool rake face or ii) on images from video recording of cutting process. Storchak et al. [1] carried out orthogonal cutting tests on an AISI 1045 steel workpiece using various cutting speeds, uncut chip thicknesses and tool rake angles. Cutting forces and tool-chip contact length were measured and compared to those provided by numerical simulation of cutting. The purpose of their study was to find out the main parameters of the material behaviour law used in numerical simulation of cutting that influence the prediction of Lc. The chip formation during cutting was recorded using a video camera. Contact length was manually measured on images and associated numerical model served as a benchmark for comprehending variations in Lc. Sutter [2] delved into chip geometries during high-speed machining, employing a ballistic device for

<figure><img src="image_1957.png" /></figure>
orthogonal cutting conditions. The tool-chip contact length measurements were carried out using the software that controls the camera and enables the enhancement of specific contrasts only. Their experimental scrutiny of tool-chip contact length and shear angle provided insights into machining dynamics across varying speed and depths of cut.

Iqbal et al. [3] executed a comparative study on tool-chip contact length in turning for two engineering alloys (an AISI 1045 steel and a Ti6Al4V titanium alloy), introducing new models tailored for high-speed machining. To measure the contact length, cutting inserts were analysed using a Polyvar optical microscope equipped with image processing software. The contact marks left by the chip on the tool rake face were employed to identify the contact area. The study underscored how machinability influences the prediction of Lc, crucial for understanding heat transfer and wear models.

Ellersiek et al. [4] evaluated methods for measuring tool-chip contact length in wet machining, introducing innovative approaches such as the use of microtextured tools. The micro-textures were implemented on the tool's rake face in the direction of chip flow on the insert. This facilitates the determination of the contact length using laser textures applied at a steadily increasing angle. The initial micro-texturing is positioned 210 µm away from the tool cutting edge, meaning that only contact lengths beyond this distance can be detected. Following that, the undersides of the resulting chips at three distinct locations: beginning, middle, and end of the cutting were examined using topography images to obtain statistical validation. Also, in-situ visualization with high-speed recordings study, knowing the pixel size, was carried out, and compared to the results from the microtextured tool approach. The study accentuates the challenges in determining contact length in wet conditions, essential for comprehending the chip formation process.

Klippel et al. [5] introduced an automated chip characteristics determination method, measuring thickness and curling radius, automatically derived from images of the chips through image processing, encompassing image preparation and contour detection. These automated measurements are then compared to manually obtained data. A Convolutional Neural Network was introduced to automate the decision-making process regarding the application of algorithms for continuous chips (utilizing average chip thickness) or segmented chips (considering minimum and maximum chip thicknesses along with chip segment lengths). The study aimed to determine average chip thickness, curling radii and segmentation lengths, laying the groundwork for automated chip analysis.

Qazani et al. [6] proposed a prediction model of tool-chip contact length using an adaptive network-based fuzzy inference system (ANFIS) in orthogonal cutting of an AISI 4140 steel. To train and test the proposed models, a dataset of tool-chip contact length was created manually, for different machining conditions, using images captured with a microscope and imported in the image processing software *Clemex*. The experiments were conducted three times, and the average value for the tool-chip contact length was chosen during the analysis. The study focused on leveraging machine learning methods to predict Lc, showcasing the potential of ANFIS in enhancing accuracy. Arefin et al. [7] delved into chip formation mechanisms in one-dimensional vibration-assisted machining (VAM), uncovering the intermittent nature of cutting in VAM. Both orthogonal cutting simulations and experiments were carried out. Each test was conducted five times minimum to ensure repeatability and confirmation of the measurements, in terms of forces and chip dimensions (determined with a Scanning Electron Microscope). Their findings reveal a smaller tool-chip contact length in VAM compared to conventional machining, impacting chip thickness.

Gzresik [8] proposed a computer-based technique to determine more accurately the tool-chip contact length, using image processing methods applied to images of tool rake face. The image processing was based on scaling, image contrast improving and colour filtering. As a result, both the tool-chip contact length and the actual area of contact were determined more accurately than
orthogonal cutting conditions. The tool-chip contact length measurements were carried out using the software that controls the camera and enables the enhancement of specific contrasts only. Their experimental scrutiny of tool-chip contact length and shear angle provided insights into machining dynamics across varying speed and depths of cut.

Iqbal et al. [3] executed a comparative study on tool-chip contact length in turning for two engineering alloys (an AISI 1045 steel and a Ti6Al4V titanium alloy), introducing new models tailored for high-speed machining. To measure the contact length, cutting inserts were analysed using a Polyvar optical microscope equipped with image processing software. The contact marks left by the chip on the tool rake face were employed to identify the contact area. The study underscored how machinability influences the prediction of Lc, crucial for understanding heat transfer and wear models.

Ellersiek et al. [4] evaluated methods for measuring tool-chip contact length in wet machining, introducing innovative approaches such as the use of microtextured tools. The micro-textures were implemented on the tool's rake face in the direction of chip flow on the insert. This facilitates the determination of the contact length using laser textures applied at a steadily increasing angle. The initial micro-texturing is positioned 210 µm away from the tool cutting edge, meaning that only contact lengths beyond this distance can be detected. Following that, the undersides of the resulting chips at three distinct locations: beginning, middle, and end of the cutting were examined using topography images to obtain statistical validation. Also, in-situ visualization with high-speed recordings study, knowing the pixel size, was carried out, and compared to the results from the microtextured tool approach. The study accentuates the challenges in determining contact length in wet conditions, essential for comprehending the chip formation process.

Klippel et al. [5] introduced an automated chip characteristics determination method, measuring thickness and curling radius, automatically derived from images of the chips through image processing, encompassing image preparation and contour detection. These automated measurements are then compared to manually obtained data. A Convolutional Neural Network was introduced to automate the decision-making process regarding the application of algorithms for continuous chips (utilizing average chip thickness) or segmented chips (considering minimum and maximum chip thicknesses along with chip segment lengths). The study aimed to determine average chip thickness, curling radii and segmentation lengths, laying the groundwork for automated chip analysis.

Qazani et al. [6] proposed a prediction model of tool-chip contact length using an adaptive network-based fuzzy inference system (ANFIS) in orthogonal cutting of an AISI 4140 steel. To train and test the proposed models, a dataset of tool-chip contact length was created manually, for different machining conditions, using images captured with a microscope and imported in the image processing software *Clemex*. The experiments were conducted three times, and the average value for the tool-chip contact length was chosen during the analysis. The study focused on leveraging machine learning methods to predict Lc, showcasing the potential of ANFIS in enhancing accuracy. Arefin et al. [7] delved into chip formation mechanisms in one-dimensional vibration-assisted machining (VAM), uncovering the intermittent nature of cutting in VAM. Both orthogonal cutting simulations and experiments were carried out. Each test was conducted five times minimum to ensure repeatability and confirmation of the measurements, in terms of forces and chip dimensions (determined with a Scanning Electron Microscope). Their findings reveal a smaller tool-chip contact length in VAM compared to conventional machining, impacting chip thickness.

Gzresik [8] proposed a computer-based technique to determine more accurately the tool-chip contact length, using image processing methods applied to images of tool rake face. The image processing was based on scaling, image contrast improving and colour filtering. As a result, both the tool-chip contact length and the actual area of contact were determined more accurately than
by using an optical microscope in the post-cutting stage. This aims to estimate the real contact area between the rake face and the sliding chip, ensuring a more precise determination of contact loads and heat fluxes. The study revealed a potential difference of approximately 25% between the actual and nominal contact areas.

Barelli et al. [9] proposed an identification of tool-chip contact length in a Ti6Al4V titanium alloy turning, using image processing. An image pre-treatment was first applied, enhancing contrast on raw images, in order to highlight the tool and the chip. A 255 (white colour) grayscale value is attributed to the tool and 0 (black colour) to its edge. Then, by employing image processing techniques, the tool-chip contact length was automatically discerned in a provided pretreated image. This was achieved through the initial identification of the tool tip and cutting edge, utilizing contour coordinates and modifying the grayscale of the image.

This study aims to automate the recognition of the separation point between the tool rake face and the chip, using diverse images recorded during orthogonal cutting of a titanium alloy. Tool-chip contact length is measured on a minimum of 100 images for each machining condition. The accuracy of the algorithm is evaluated by comparing computed and manually measured tool-chip contact lengths. The evolution of tool-chip contact length with respect to uncut chip thickness and cutting speed is then evaluated.

## Experimental setup and cutting conditions

Chip formation during orthogonal cutting was recorded by a high-speed video camera (Photron SA5). A long-distance microscope (Questar QM100) was used to observe the chip-forming zone in front of the cutting tool. The scene is illuminated by a fiber-optic lighting system. The optical setup and machine configuration are illustrated in Fig. 1a.

<figure><img src="image_1963.png" /><figcaption>Fig. 1. a) experimental setup; b) workpiece dimensions</figcaption></figure>

The machined part is a Ti6Al4V titanium alloy plate; its dimensions are shown in Fig. 1b. The cutting tool is made of tungsten carbide (CW-6%Co); it has a cutting angle of 0°, a clearance angle of 7° and an edge radius of about 20µm. The workpiece is mounted in a vice, which is attached on the machine table. The cutting speed is given to the machine table and the tool is held stationary. The cutting speeds (Vc) used for this study are: 30, 45 and 60m/min and the uncut chip thicknesses (h) are of 0.1mm and 0.2mm respectively. The resolution of the camera is of 768x648 pixels for all tests performed with the uncut chip thickness of 0.1mm, and it is of 1024x496 pixels for those with a 0.2mm uncut chip thickness. The acquisition frequency is of 15 000 frames/sec for all machining conditions used in this study.
by using an optical microscope in the post-cutting stage. This aims to estimate the real contact area between the rake face and the sliding chip, ensuring a more precise determination of contact loads and heat fluxes. The study revealed a potential difference of approximately 25% between the actual and nominal contact areas.

Barelli et al. [9] proposed an identification of tool-chip contact length in a Ti6Al4V titanium alloy turning, using image processing. An image pre-treatment was first applied, enhancing contrast on raw images, in order to highlight the tool and the chip. A 255 (white colour) grayscale value is attributed to the tool and 0 (black colour) to its edge. Then, by employing image processing techniques, the tool-chip contact length was automatically discerned in a provided pretreated image. This was achieved through the initial identification of the tool tip and cutting edge, utilizing contour coordinates and modifying the grayscale of the image.

This study aims to automate the recognition of the separation point between the tool rake face and the chip, using diverse images recorded during orthogonal cutting of a titanium alloy. Tool-chip contact length is measured on a minimum of 100 images for each machining condition. The accuracy of the algorithm is evaluated by comparing computed and manually measured tool-chip contact lengths. The evolution of tool-chip contact length with respect to uncut chip thickness and cutting speed is then evaluated.

## Experimental setup and cutting conditions

Chip formation during orthogonal cutting was recorded by a high-speed video camera (Photron SA5). A long-distance microscope (Questar QM100) was used to observe the chip-forming zone in front of the cutting tool. The scene is illuminated by a fiber-optic lighting system. The optical setup and machine configuration are illustrated in Fig. 1a.

<figure><img src="image_1958.png" /><figcaption>Fig. 1. a) experimental setup; b) workpiece dimensions</figcaption></figure>

The machined part is a Ti6Al4V titanium alloy plate; its dimensions are shown in Fig. 1b. The cutting tool is made of tungsten carbide (CW-6%Co); it has a cutting angle of 0°, a clearance angle of 7° and an edge radius of about 20µm. The workpiece is mounted in a vice, which is attached on the machine table. The cutting speed is given to the machine table and the tool is held stationary. The cutting speeds (Vc) used for this study are: 30, 45 and 60m/min and the uncut chip thicknesses (h) are of 0.1mm and 0.2mm respectively. The resolution of the camera is of 768x648 pixels for all tests performed with the uncut chip thickness of 0.1mm, and it is of 1024x496 pixels for those with a 0.2mm uncut chip thickness. The acquisition frequency is of 15 000 frames/sec for all machining conditions used in this study.
## Image processing methodology

In the images depicting the formation of chips during machining, it is challenging to accurately discern the extent of contact between the cutting tool and the chip within the images captured by the camera (Fig. 2a). This difficulty predominantly arises due to the presence of shadows caused by the light position, which was not coaxially placed with respect to the optic axe, and some shadow area is formed in front of the tool. Therefore, to improve images clarity, the image processing methodology illustrated in Fig. 2 was applied.

Initially, a *Gaussian filter* was utilized to reduce noise and smoothen the image. An example of the resulting image is shown in Fig. 2b. A Gaussian blur is applied: it is a low-pass filter that convolves the image with a Gaussian function, smoothing out high-frequency noise while preserving the overall structure and edges. By convolving the image with a Gaussian kernel, the filter reduces the impact of high-frequency components, effectively blurring or smoothing the image. This process helps to remove random variations or noise present in the image, that can interfere with the edge detection process, leading to false edges or erroneous results. This Gaussian blurring helps in suppressing small-scale details or insignificant edges that might not be of interest [10].

Gaussian blur is commonly favoured in various applications requiring noise reduction while preserving crucial details and edges in images. This preference stems from its characteristic gradual decrease in influence away from the kernel's centre. This weighting scheme allows for a better preservation of edges in comparison to average smoothing techniques. However, it can also lead to a loss of sharpness, especially in areas with important details or edges [11].

Following this, a *Sobel filter* was employed to emphasize edges and gradients, aiming to enhance the definition between the tool and the chip (Fig. 2c). The Sobel filter emphasizes edges by accentuating the differences in pixel intensity between neighbouring pixels. It calculates the rate of change of intensity and thereby identifies areas in the image where there is a significant change or sharp transition in intensity values. The Sobel operator consists of two convolution kernels: one for detecting horizontal changes and another for vertical changes. These kernels calculate the gradient approximation in the horizontal and vertical directions, respectively, by performing convolution operations on the image. Two separate gradient images are obtained, which can be combined or used independently to highlight and analyse edges [12].

The Sobel filter can be sensitive to noise in an image. High-frequency noise can lead to false edges detection [13]. To prevent these false edges being detected, a *binary thresholding* process was implemented to segment the image and highlight specific areas of interest, facilitating a clearer detection of the tool-chip contact (Fig. 2d). Binary thresholding involves converting the grayscale image into a binary image by setting a threshold value. Pixels with intensity values above the threshold are assigned one value (white), while pixels below the threshold are assigned another value (black), creating a high contrast representation [14].
## Image processing methodology

In the images depicting the formation of chips during machining, it is challenging to accurately discern the extent of contact between the cutting tool and the chip within the images captured by the camera (Fig. 2a). This difficulty predominantly arises due to the presence of shadows caused by the light position, which was not coaxially placed with respect to the optic axe, and some shadow area is formed in front of the tool. Therefore, to improve images clarity, the image processing methodology illustrated in Fig. 2 was applied.

Initially, a *Gaussian filter* was utilized to reduce noise and smoothen the image. An example of the resulting image is shown in Fig. 2b. A Gaussian blur is applied: it is a low-pass filter that convolves the image with a Gaussian function, smoothing out high-frequency noise while preserving the overall structure and edges. By convolving the image with a Gaussian kernel, the filter reduces the impact of high-frequency components, effectively blurring or smoothing the image. This process helps to remove random variations or noise present in the image, that can interfere with the edge detection process, leading to false edges or erroneous results. This Gaussian blurring helps in suppressing small-scale details or insignificant edges that might not be of interest [10].

Gaussian blur is commonly favoured in various applications requiring noise reduction while preserving crucial details and edges in images. This preference stems from its characteristic gradual decrease in influence away from the kernel's centre. This weighting scheme allows for a better preservation of edges in comparison to average smoothing techniques. However, it can also lead to a loss of sharpness, especially in areas with important details or edges [11].

Following this, a *Sobel filter* was employed to emphasize edges and gradients, aiming to enhance the definition between the tool and the chip (Fig. 2c). The Sobel filter emphasizes edges by accentuating the differences in pixel intensity between neighbouring pixels. It calculates the rate of change of intensity and thereby identifies areas in the image where there is a significant change or sharp transition in intensity values. The Sobel operator consists of two convolution kernels: one for detecting horizontal changes and another for vertical changes. These kernels calculate the gradient approximation in the horizontal and vertical directions, respectively, by performing convolution operations on the image. Two separate gradient images are obtained, which can be combined or used independently to highlight and analyse edges [12].

The Sobel filter can be sensitive to noise in an image. High-frequency noise can lead to false edges detection [13]. To prevent these false edges being detected, a *binary thresholding* process was implemented to segment the image and highlight specific areas of interest, facilitating a clearer detection of the tool-chip contact (Fig. 2d). Binary thresholding involves converting the grayscale image into a binary image by setting a threshold value. Pixels with intensity values above the threshold are assigned one value (white), while pixels below the threshold are assigned another value (black), creating a high contrast representation [14].
<figure><img src="image_1964.png" /><figcaption>Fig. 2. Different steps of the considered image processing: a) raw image; b) Gauss filter c) Sobel filter d) Binary thresholding e) Dilation f) Contour detection g) Last contact point detection</figcaption></figure>
<figure><img src="image_1959.png" /><figcaption>Fig. 2. Different steps of the considered image processing: a) raw image; b) Gauss filter c) Sobel filter d) Binary thresholding e) Dilation f) Contour detection g) Last contact point detection</figcaption></figure>
A sensitivity study was carried out to establish the threshold value to be applied to all images, whatever the machining condition used in this study. A threshold value of 35 enabled to clearly detect the chip, the tool, and the workpiece.

Lastly, a *dilation filter* was used (Fig. 2e). The dilation filter can join or connect disparate elements within an image. It helps in merging separate regions or broken parts of objects present in an image. For each pixel in the image, this operation considers the neighbourhood defined by the structuring element. If the centre pixel of the structuring element overlaps with any part of an object in the image, that pixel is set to the maximum intensity value found within the neighbourhood. As a result, it connects broken parts of an object, fills in gaps, and generally expands the areas identified by the structuring element [15].

Now that a black and white image is obtained, the next step is to apply a function for *edges detection*. The function utilised in this study is “findContours()” from OpenCV python library; it takes in a binary image, where the objects are white and the background is black, and returns a list of contours found in the image. Each contour is represented as a list of points that form the boundary of an object [16].

Contours are represented as lists of coordinate points, delineating the combined boundaries of the tool, the chip and the workpiece. After finding the contour (Fig. 2f), the objective is the automated detection of the last contact point between the tool and the chip. Assuming a closed contour oriented in trigonometric direction, the process involves identifying, for each image (and consequently, each contour), the red point in the top right hand of the contour (visible in Fig. 2g).

Following the determination of this point, a specific contour segment (highlighted in blue in Fig. 2g) is chosen, ensuring it encapsulates the last contact point between the tool and the chip. To accomplish this, an analysis of the ordinate evolution along the contour is conducted, and a set of points containing the targeted breakpoint (representing the last point of contact between the tool and the chip) is selected. Subsequently, a piecewise linear regression method is applied to the identified contour using the Python library *pwlf*, as detailed in [17]. The input comprises the 1-D data representing the coordinates of the relevant contour, along with a specified number of line segments (three in this case). The output yields distinct fitting linear parameters for each segment and the identified breakpoints, indicating the boundaries between the different line segments.

Then, the point of interest is designated as the identified breakpoint with the lowest ordinate, and this point is recorded in a dataset as the conclusive last contact point between the tool and the chip for a given input image. Finally, the actual tool-chip contact length is determined for each image by subtracting the ordinate of the tool basis. This specific point is manually assessed using the image processing software PFV4 for each cutting condition and the corresponding series of images.

# Results and discussion

## Accuracy of the algorithm

In this investigation, the focus is on quantifying the uncertainty associated with measurements automatically conducted by the algorithm. To achieve this, a comparative analysis was conducted by juxtaposing these automated measurements with manually acquired data, as depicted in Fig. 3a and 3b. Manual measurements were made on selected images from a series, using the PFV4 image processing software.

A comparison of measurements was executed for two different uncut thicknesses, revealing an offset in both scenarios: approximately 50 pixels for an uncut chip thickness of 0.2mm (resulting in a 17.8% relative error) and around 100 pixels for an uncut chip thickness of 0.1mm (yielding a 30% relative error), depending on images quality. A prior study by Grzesik [8] concluded that actual and nominal dimensions studied using image processing could exhibit variations of about 25%.
A sensitivity study was carried out to establish the threshold value to be applied to all images, whatever the machining condition used in this study. A threshold value of 35 enabled to clearly detect the chip, the tool, and the workpiece.

Lastly, a *dilation filter* was used (Fig. 2e). The dilation filter can join or connect disparate elements within an image. It helps in merging separate regions or broken parts of objects present in an image. For each pixel in the image, this operation considers the neighbourhood defined by the structuring element. If the centre pixel of the structuring element overlaps with any part of an object in the image, that pixel is set to the maximum intensity value found within the neighbourhood. As a result, it connects broken parts of an object, fills in gaps, and generally expands the areas identified by the structuring element [15].

Now that a black and white image is obtained, the next step is to apply a function for *edges detection*. The function utilised in this study is "findContours()" from OpenCV python library; it takes in a binary image, where the objects are white and the background is black, and returns a list of contours found in the image. Each contour is represented as a list of points that form the boundary of an object [16].

Contours are represented as lists of coordinate points, delineating the combined boundaries of the tool, the chip and the workpiece. After finding the contour (Fig. 2f), the objective is the automated detection of the last contact point between the tool and the chip. Assuming a closed contour oriented in trigonometric direction, the process involves identifying, for each image (and consequently, each contour), the red point in the top right hand of the contour (visible in Fig. 2g).

Following the determination of this point, a specific contour segment (highlighted in blue in Fig. 2g) is chosen, ensuring it encapsulates the last contact point between the tool and the chip. To accomplish this, an analysis of the ordinate evolution along the contour is conducted, and a set of points containing the targeted breakpoint (representing the last point of contact between the tool and the chip) is selected. Subsequently, a piecewise linear regression method is applied to the identified contour using the Python library *pwlf*, as detailed in [17]. The input comprises the 1-D data representing the coordinates of the relevant contour, along with a specified number of line segments (three in this case). The output yields distinct fitting linear parameters for each segment and the identified breakpoints, indicating the boundaries between the different line segments.

Then, the point of interest is designated as the identified breakpoint with the lowest ordinate, and this point is recorded in a dataset as the conclusive last contact point between the tool and the chip for a given input image. Finally, the actual tool-chip contact length is determined for each image by subtracting the ordinate of the tool basis. This specific point is manually assessed using the image processing software PFV4 for each cutting condition and the corresponding series of images.

# Results and discussion

## Accuracy of the algorithm

In this investigation, the focus is on quantifying the uncertainty associated with measurements automatically conducted by the algorithm. To achieve this, a comparative analysis was conducted by juxtaposing these automated measurements with manually acquired data, as depicted in Fig. 3a and 3b. Manual measurements were made on selected images from a series, using the PFV4 image processing software.

A comparison of measurements was executed for two different uncut thicknesses, revealing an offset in both scenarios: approximately 50 pixels for an uncut chip thickness of 0.2mm (resulting in a 17.8% relative error) and around 100 pixels for an uncut chip thickness of 0.1mm (yielding a 30% relative error), depending on images quality. A prior study by Grzesik [8] concluded that actual and nominal dimensions studied using image processing could exhibit variations of about 25%.
In some cases, both human eye and the algorithm detect the last contact point in roughly the same place, as shown in Fig. 4a. To elucidate the observed offset across all cutting conditions, an in-depth study was conducted, analyzing the images at each step of the image processing pipeline. Notably, human-eye assessments differed between raw images (without any processing) and images after applying the Sobel filter. The Sobel filter revealed previously concealed aspects of the tool and the chip, apparently hidden in the shadows in front of the tool in the raw image, effectively filling the black areas surrounding the tool; therefore, the algorithm positions the tool-chip last contact point at higher y-coordinate (visible on Fig. 4b and c). This discrepancy can be partially attributed to the experimental setup. Given the placement of the lamp on the right side of the tool and the potential coverage of the area of interest by its shadow. Manual measures involve some errors due to the difficulty of perceiving the last contact point, whereas the algorithm, employing various filters, could eventually detect and highlight the chip in shadowed area, imperceptible to the human eye. Therefore, the accuracy of the proposed method will be increased on images coaxially illuminated. Another factor could be the insufficient tuning of the filters for the presented problem, causing excessive modification of the images and resulting in a dilation of the tool and chip.

<figure><img src="image_1965.png" /><figcaption>Fig. 3. Y-coordinates of tool-chip last contact point vs image number, automatically and manually measured for a) $V_c = 45m/min$ and $h = 0.2mm$; b) $V_c = 60m/min$ and $h = 0.1mm$.</figcaption></figure>

Once manual measurements are validated for accuracy, an optimization process can be initiated to fine-tune filter parameters, ensuring the reliability of fully automatic measurements. In conclusion, this underscores the substantial impact of various filters, their tuned parameters, and the experimental setup (including light intensity and orientation), as well as image recording parameters (frame rate and resolution), on the observed differences relative to human measures.

## Tool-chip contact length evolution with machining conditions

Fig. 5 illustrates the values of tool-chip contact length automatically measured on sets of 100 images. On some images, the algorithm is unable to consistently identify the last point of contact, resulting in outliers. This is the case for the cutting conditions $V_c = 30m/min$, $h = 0.2mm$, on the first 25 images (Fig. 5a) and for $V_c = 60m/min$, $h = 0.2mm$, on the first 10 images (Fig. 5b).
In some cases, both human eye and the algorithm detect the last contact point in roughly the same place, as shown in Fig. 4a. To elucidate the observed offset across all cutting conditions, an in-depth study was conducted, analyzing the images at each step of the image processing pipeline. Notably, human-eye assessments differed between raw images (without any processing) and images after applying the Sobel filter. The Sobel filter revealed previously concealed aspects of the tool and the chip, apparently hidden in the shadows in front of the tool in the raw image, effectively filling the black areas surrounding the tool; therefore, the algorithm positions the tool-chip last contact point at higher y-coordinate (visible on Fig. 4b and c). This discrepancy can be partially attributed to the experimental setup. Given the placement of the lamp on the right side of the tool and the potential coverage of the area of interest by its shadow. Manual measures involve some errors due to the difficulty of perceiving the last contact point, whereas the algorithm, employing various filters, could eventually detect and highlight the chip in shadowed area, imperceptible to the human eye. Therefore, the accuracy of the proposed method will be increased on images coaxially illuminated. Another factor could be the insufficient tuning of the filters for the presented problem, causing excessive modification of the images and resulting in a dilation of the tool and chip.

<figure><img src="image_1960.png" /><figcaption>Fig. 3. Y-coordinates of tool-chip last contact point vs image number, automatically and manually measured for a) $V_c = 45m/min$ and $h = 0.2mm$; b) $V_c = 60m/min$ and $h = 0.1mm$.</figcaption></figure>

Once manual measurements are validated for accuracy, an optimization process can be initiated to fine-tune filter parameters, ensuring the reliability of fully automatic measurements. In conclusion, this underscores the substantial impact of various filters, their tuned parameters, and the experimental setup (including light intensity and orientation), as well as image recording parameters (frame rate and resolution), on the observed differences relative to human measures.

## Tool-chip contact length evolution with machining conditions

Fig. 5 illustrates the values of tool-chip contact length automatically measured on sets of 100 images. On some images, the algorithm is unable to consistently identify the last point of contact, resulting in outliers. This is the case for the cutting conditions $V_c = 30m/min$, $h = 0.2mm$, on the first 25 images (Fig. 5a) and for $V_c = 60m/min$, $h = 0.2mm$, on the first 10 images (Fig. 5b).
<figure><img src="image_1966.png" /><figcaption>Fig. 4. Examples of the position of the last contact point manually and automatically identified. Contour detection for a) $V_c = 45$m/min; h = 0.2mm; b) $V_c = 60$m/min; h = 0.1mm and c) last contact point detection for $V_c = 60$m/min; h = 0.1mm</figcaption></figure>

For all cutting speed utilised in this study, the tool-chip contact length increases when increasing the uncut chip thickness. Iqbal et al. [3] measured a $L_c$ of 200µm for an h = 0.1mm and a $L_c$ of 400µm for an h = 0.2mm, after machining a titanium alloy with a tool having a rake angle of 0° and with a cutting speed of 60m/min. These values are much lower than those measured in the current study. In the research of Iqbal et al. [3], the $L_c$ was measured on the tool rake faces, which leads to an underestimation of $L_c$. Concerning tool-chip contact evolution when increasing the uncut chip thickness, a similar trend is found.

<figure><img src="image_1967.png" /><figcaption>Fig. 5. Tool-chip contact length vs image number for h of 0.1 and 0.2 mm and a) $V_c = 30$m/min and b) $V_c = 60$m/min.</figcaption></figure>

In Fig. 5, it can also be observed an increase of the tool-chip contact length between the first and last images of each dataset. In the case of machining using an uncut chip thickness of 0.1mm, an increase of about 19% is noted, for both cutting speeds illustrated in Fig. 5. Nevertheless, this falls within the measurement error range and therefore needs to be further investigated, on other sets of images taken at different stages of the machining process.

Fig. 6 shows the tool-chip contact lengths automatically measured on sets of 100 images, for different cutting speeds. For the speed range considered in this study, there is no real change in contact length with respect to cutting speed, for both uncut chip thicknesses. This trend was also found in [3], for machining with and uncut chip thickness of 0.1mm.
<figure><img src="image_1961.png" /><figcaption>Fig. 4. Examples of the position of the last contact point manually and automatically identified. Contour detection for a) $V_c = 45$m/min; h = 0.2mm; b) $V_c = 60$m/min; h = 0.1mm and c) last contact point detection for $V_c = 60$m/min; h = 0.1mm</figcaption></figure>

For all cutting speed utilised in this study, the tool-chip contact length increases when increasing the uncut chip thickness. Iqbal et al. [3] measured a $L_c$ of 200µm for an $h = 0.1$mm and a $L_c$ of 400µm for an $h = 0.2$mm, after machining a titanium alloy with a tool having a rake angle of 0° and with a cutting speed of 60m/min. These values are much lower than those measured in the current study. In the research of Iqbal et al. [3], the $L_c$ was measured on the tool rake faces, which leads to an underestimation of $L_c$. Concerning tool-chip contact evolution when increasing the uncut chip thickness, a similar trend is found.

<figure><img src="image_1962.png" /><figcaption>Fig. 5. Tool-chip contact length vs image number for $h$ of 0.1 and 0.2 mm and a) $V_c = 30$m/min and b) $V_c = 60$m/min.</figcaption></figure>

In Fig. 5, it can also be observed an increase of the tool-chip contact length between the first and last images of each dataset. In the case of machining using an uncut chip thickness of 0.1mm, an increase of about 19% is noted, for both cutting speeds illustrated in Fig. 5. Nevertheless, this falls within the measurement error range and therefore needs to be further investigated, on other sets of images taken at different stages of the machining process.

Fig. 6 shows the tool-chip contact lengths automatically measured on sets of 100 images, for different cutting speeds. For the speed range considered in this study, there is no real change in contact length with respect to cutting speed, for both uncut chip thicknesses. This trend was also found in [3], for machining with and uncut chip thickness of 0.1mm.
<figure><img src="image_1968.png" /><figcaption>Fig. 6. Tool-chip contact length vs image number for a) h=0.1mm and b) h=0.2mm, for different cutting speeds.</figcaption></figure>

## Conclusion

In this study, a method for automatic detection of the separation point between the tool rake face and a titanium alloy chip was implemented. This was applied on diverse images recorded during orthogonal cutting. The identification of the tool-chip separation point then allowed the measurement of the contact length. The accuracy of the algorithm was evaluated by comparing computed and manually measured tool-chip contact lengths. The evolution of tool-chip contact length with respect to uncut chip thickness and cutting speed was also evaluated.

The proposed method was able to detect chip, workpiece and tool contours and therefore the last contact point between the chip and the tool. The uncertainty of the automatic measurements was evaluated in relation to manual measurements on raw images. A maximum 30% relative error was found; this could be attributed to different factors, among which light intensity and its orientation in relation to the cutting tool. The use of a more adapted positioning of the lighting system will enable the recording of images with no shadows in the area of interest. These will be done in a further research work.

For all cutting speed utilised in this study, the tool-chip contact length increases when increasing the uncut chip thickness and no clear evolution was found when increasing the cutting speed.

As the proposed method detects chip contour, automated measurement of its corresponding parameters (thickness - for a ribbon like chip and number of segments in the case of a serrated chip) could be considered in a future work, on higher quality chip formation images.

## References

[1] M. Storchak, K. Drewle, C. Menze, T. Stehle, H.-C. Möhring, Determination of the Tool-Chip Contact Length for the Cutting Processes, Materials 15 (2022) 3264. https://doi.org/10.3390/ma15093264

[2] G. Sutter, Chip geometries during high-speed machining for orthogonal cutting conditions, International Journal of Machine Tools and Manufacture 45 (2005) 719-726. https://doi.org/10.1016/j.ijmachtools.2004.09.018

[3] S. Ahasan Iqbal, P. T. Mativenga, M. A. Sheikh, A comparative study of the tool-chip contact length in turning of two engineering alloys for a wide range of cutting speeds, Int. J. Advanced Manufacturing Technology, 42 (2009) 30-40. https://doi.org/10.1007/s00170-008-1582-6

[4] L. Ellersiek, C. Menze, F. Sauer, B. Denkena, H.-C. Mohring, V. Schulze, Evaluation of methods for measuring tool-chip contact length in wet machining using different approaches
<figure><img src="image_1963.png" /><figcaption>Fig. 6. Tool-chip contact length vs image number for a) h=0.1mm and b) h=0.2mm, for different cutting speeds.</figcaption></figure>

## Conclusion

In this study, a method for automatic detection of the separation point between the tool rake face and a titanium alloy chip was implemented. This was applied on diverse images recorded during orthogonal cutting. The identification of the tool-chip separation point then allowed the measurement of the contact length. The accuracy of the algorithm was evaluated by comparing computed and manually measured tool-chip contact lengths. The evolution of tool-chip contact length with respect to uncut chip thickness and cutting speed was also evaluated.

The proposed method was able to detect chip, workpiece and tool contours and therefore the last contact point between the chip and the tool. The uncertainty of the automatic measurements was evaluated in relation to manual measurements on raw images. A maximum 30% relative error was found; this could be attributed to different factors, among which light intensity and its orientation in relation to the cutting tool. The use of a more adapted positioning of the lighting system will enable the recording of images with no shadows in the area of interest. These will be done in a further research work.

For all cutting speed utilised in this study, the tool-chip contact length increases when increasing the uncut chip thickness and no clear evolution was found when increasing the cutting speed.

As the proposed method detects chip contour, automated measurement of its corresponding parameters (thickness - for a ribbon like chip and number of segments in the case of a serrated chip) could be considered in a future work, on higher quality chip formation images.

## References

[1] M. Storchak, K. Drewle, C. Menze, T. Stehle, H.-C. Möhring, Determination of the Tool-Chip Contact Length for the Cutting Processes, Materials 15 (2022) 3264. https://doi.org/10.3390/ma15093264

[2] G. Sutter, Chip geometries during high-speed machining for orthogonal cutting conditions, International Journal of Machine Tools and Manufacture 45 (2005) 719-726. https://doi.org/10.1016/j.ijmachtools.2004.09.018

[3] S. Ahasan Iqbal, P. T. Mativenga, M. A. Sheikh, A comparative study of the tool-chip contact length in turning of two engineering alloys for a wide range of cutting speeds, Int. J. Advanced Manufacturing Technology, 42 (2009) 30-40. https://doi.org/10.1007/s00170-008-1582-6

[4] L. Ellersiek, C. Menze, F. Sauer, B. Denkena, H.-C. Mohring, V. Schulze, Evaluation of methods for measuring tool-chip contact length in wet machining using different approaches
(microtextured tool, in-situ visualization and restricted contact tool), Production Engineering 16 (2022) 635-646. https://doi.org/10.1007/s11740-022-01127-w

[5] H. Klippel, S. Pflaum, M. Kuffa, K. Wegener, Automated evaluation of continuous and segmented chip geometries based on image processing methods and a convolutional neural network, Journal of Machine Engineering, 22(4) (2022) 115-132. https://doi.org/10.36897/jme/156091

[6] M. R. Chalak Qazani, V. Pourmostraghimi, M. Moayyedian, S. Pedrammehr, Estimation of tool-chip contact length using optimized machine learning in orthogonal cutting, Engineering Applications of Artificial Intelligence 114 (2022) 105118. https://doi.org/10.1016/j.engappai.2022.105118

[7] S. Arefin, X.Q. Zhang, A. Senthil Kumar, D. Wee Keong Neo, Y. Wang, Study of chip formation mechanism in one-dimensional vibration-assisted machining, Journal of Materials Processing Tech. 291 (2021) 117022. https://doi.org/10.1016/j.jmatprotec.2020.117022

[8] W. Grzesik, An integrated approach to evaluating the tribo-contact for coated cutting inserts, Wear 240 (2000) 9-18. https://doi.org/10.1016/S0043-1648(00)00324-0

[9] F. Barelli, V. Wagner, R. Laheurte, G. Dessein, Ph. Darnis, O. Cahuc, M. Mousseigne, Orthogonal cutting of TA6V alloys with chamfered tools: Analysis of tool-chip contact lengths. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 231 (2017) 2384-2395. https://doi.org/10.1177/0954405416629589

[10] Information on https://docs.opencv.org/4.x/d4/d13/tutorialPY_filtering.html

[11] Information on https://pyimagesearch.com/2021/04/28/opencv-smoothing-and-blurring/

[12] Information on https://docs.opencv.org/3.4/d2/d2c/tutorial_sobel_derivatives.html

[13] J. D. Bakos, Chapter 4 - Memory optimization and video processing, in Embedded Systems, 2016, pp. 147-185. https://doi.org/10.1016/B978-0-12-800342-8.00004-3.

[14] Information on https://docs.opencv.org/3.4/d7/d4d/tutorialPy_thresholding.html

[15] Information on https://docs.opencv.org/3.4/db/df6/tutorial_erosion_dilatation.html

[16] Information on https://docs.opencv.org/3.4/d4/d73/tutorialPy_contours_begin.html

[17] C. F. Jekel, G. Venter, pwlf: A Python Library for Fitting 1D Continuous Piecewise Linear Functions, MIT, 2019.
(microtextured tool, in-situ visualization and restricted contact tool), Production Engineering 16 (2022) 635-646. https://doi.org/10.1007/s11740-022-01127-w

[5] H. Klippel, S. Pflaum, M. Kuffa, K. Wegener, Automated evaluation of continuous and segmented chip geometries based on image processing methods and a convolutional neural network, Journal of Machine Engineering, 22(4) (2022) 115-132. https://doi.org/10.36897/jme/156091

[6] M. R. Chalak Qazani, V. Pourmostraghimi, M. Moayyedian, S. Pedrammehr, Estimation of tool-chip contact length using optimized machine learning in orthogonal cutting, Engineering Applications of Artificial Intelligence 114 (2022) 105118. https://doi.org/10.1016/j.engappai.2022.105118

[7] S. Arefin, X.Q. Zhang, A. Senthil Kumar, D. Wee Keong Neo, Y. Wang, Study of chip formation mechanism in one-dimensional vibration-assisted machining, Journal of Materials Processing Tech. 291 (2021) 117022. https://doi.org/10.1016/j.jmatprotec.2020.117022

[8] W. Grzesik, An integrated approach to evaluating the tribo-contact for coated cutting inserts, Wear 240 (2000) 9-18. https://doi.org/10.1016/S0043-1648(00)00324-0

[9] F. Barelli, V. Wagner, R. Laheurte, G. Dessein, Ph. Darnis, O. Cahuc, M. Mousseigne, Orthogonal cutting of TA6V alloys with chamfered tools: Analysis of tool-chip contact lengths. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 231 (2017) 2384-2395. https://doi.org/10.1177/0954405416629589

[10] Information on https://docs.opencv.org/4.x/d4/d13/tutorialPY thresholding.html

[11] Information on https://pyimagesearch.com/2021/04/28/opencv-smoothing-and-blurring/

[12] Information on https://docs.opencv.org/3.4/d2/d2c/tutorial_sobel_derivatives.html

[13] J. D. Bakos, Chapter 4 - Memory optimization and video processing, in Embedded Systems, 2016, pp. 147-185. https://doi.org/10.1016/B978-0-12-800342-8.00004-3.

[14] Information on https://docs.opencv.org/3.4/d7/d4d/tutorialPY thresholding.html

[15] Information on https://docs.opencv.org/3.4/db/df6/tutorial_erosion_dilatation.html

[16] Information on https://docs.opencv.org/3.4/d4/d73/tutorialPY contours_begin.html

[17] C. F. Jekel, G. Venter, pwlf: A Python Library for Fitting 1D Continuous Piecewise Linear Functions, MIT, 2019.