datasets. When the dataset exceeds 10,000 data points, the scalability of SVM decreases, and the inference time significantly increases, rendering it unsuitable for real-time predictions in the cutting process. Despite these constraints, K-NN and SVM can be highly effective in scenarios with limited variations on the cutting conditions, such as the production of similar items in large series.

Lastly, ANN has the potential to overfit the data, which can lead to disastrous results in industrial applications. However, if implemented correctly, ANN is the most scalable among the three models. It can handle large datasets effectively, and its architecture can be adjusted to accommodate different types of signals and complexity. Despite having a significant higher training time than SVM and K-NN, ANN keeps a relatively low inference time independently to the size of the database. This adaptability makes ANN a viable option for a variety of use-cases.

<figure><img src="image_1875.png" /><figcaption>Figure 15 Comparison of Performance for each AI Approach</figcaption></figure>

## Conclusion

This article compares the performance of 3 different AI regressors, namely: ANN, SVM and K-NN. Each approach is optimised to find the best hyperparameters through a GridSearch algorithm and their performance are compared in terms of MSE. In this article and with the database presented above, the best ANN (MSE: 694) is a network with 2 hidden layers and 6 neurons in each of them. The first layer has a Tanh and the second a ReLu activation function. For SVM (MSE: 670), the best approach is reached with the RBF kernel, the parameter C is set to 51 and ε is 0.85. Finally, the K-NN (MSE: 613) approach best parameters are a “distance” weight computation and around 28 neighbours.

The performance comparison of each regressor on actual degradation trajectories shows similar results across all methods, as indicated by the similar MSEs. The best regressor for an industrial use depends on specific case factors. For instance, SVM and K-NN are negatively affected by the amount and quality of data, especially if it is noisy. On the other hand, ANN benefits from larger datasets and can be tailored to problem complexity. Finally, this article suggests that a good regressor for tool condition monitoring in big series industrial application are K-NN and SVM for
datasets. When the dataset exceeds 10,000 data points, the scalability of SVM decreases, and the inference time significantly increases, rendering it unsuitable for real-time predictions in the cutting process. Despite these constraints, K-NN and SVM can be highly effective in scenarios with limited variations on the cutting conditions, such as the production of similar items in large series.

Lastly, ANN has the potential to overfit the data, which can lead to disastrous results in industrial applications. However, if implemented correctly, ANN is the most scalable among the three models. It can handle large datasets effectively, and its architecture can be adjusted to accommodate different types of signals and complexity. Despite having a significant higher training time than SVM and K-NN, ANN keeps a relatively low inference time independently to the size of the database. This adaptability makes ANN a viable option for a variety of use-cases.

<figure><img src="image_1870.png" /><figcaption>Figure 15 Comparison of Performance for each AI Approach</figcaption></figure>

## Conclusion

This article compares the performance of 3 different AI regressors, namely: ANN, SVM and K-NN. Each approach is optimised to find the best hyperparameters through a GridSearch algorithm and their performance are compared in terms of MSE. In this article and with the database presented above, the best ANN (MSE: 694) is a network with 2 hidden layers and 6 neurons in each of them. The first layer has a Tanh and the second a ReLu activation function. For SVM (MSE: 670), the best approach is reached with the RBF kernel, the parameter C is set to 51 and ε is 0.85. Finally, the K-NN (MSE: 613) approach best parameters are a “distance” weight computation and around 28 neighbours.

The performance comparison of each regressor on actual degradation trajectories shows similar results across all methods, as indicated by the similar MSEs. The best regressor for an industrial use depends on specific case factors. For instance, SVM and K-NN are negatively affected by the amount and quality of data, especially if it is noisy. On the other hand, ANN benefits from larger datasets and can be tailored to problem complexity. Finally, this article suggests that a good regressor for tool condition monitoring in big series industrial application are K-NN and SVM for
their ease of implementation and optimisation. For more complex industrial cases with important variations in cutting conditions, machine, tools, and in general amount of data, ANN approaches appear to be more adaptable.

## Conflicts of interest

The authors declare that they have no conflicts of interest.

## References

[1] W. Xu and L. Cao, 'Optimal tool replacement with product quality deterioration and random tool failure', *Int. J. Prod. Res.*, vol. 53, no. 6, pp. 1736-1745, Mar. 2015. https://doi.org/10.1080/00207543.2014.957878

[2] M. Kuntoğlu et al., 'A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends', *Sensors*, vol. 21, no. 1, p. 108, 2020. https://doi.org/10.3390/s21010108

[3] F. Halila, C. Czarnota, and M. Nouari, 'Analytical stochastic modeling and experimental investigation on abrasive wear when turning difficult to cut materials', *Wear*, vol. 302, no. 1-2, pp. 1145-1157, 2013. https://doi.org/10.1016/j.wear.2012.12.055

[4] P. Muthuswamy and S. K. 'Artificial intelligence based tool condition monitoring for digital twins and industry 4.0 applications', *Int. J. Interact. Des. Manuf. IJIDem*, vol. 17, no. 3, pp. 1067-1087, Jun. 2023. https://doi.org/10.1007/s12008-022-01050-5

[5] L. Colantonio, L. Equeter, P. Dehombreux, and F. Ducobu, 'A Systematic Literature Review of Cutting Tool Wear Monitoring in Turning by Using Artificial Intelligence Techniques', *Machines*, vol. 9, no. 12, p. 351, Dec. 2021. https://doi.org/10.3390/machines9120351

[6] R. U. Baig, S. Javed, M. Khaisar, M. Shakoor, and P. Raja, 'Development of an ANN model for prediction of tool wear in turning EN9 and EN24 steel alloy', *Adv. Mech. Eng.*, vol. 13, no. 6, p. 168781402110267, Jun. 2021. https://doi.org/10.1177/16878140211026720

[7] D. Y. Pimenov, A. Bustillo, S. Wojciechowski, V. S. Sharma, M. K. Gupta, and M. Kuntoğlu, 'Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review', *J. Intell. Manuf.*, vol. 34, no. 5, pp. 2079-2121, Jun. 2023. https://doi.org/10.1007/s10845-022-01923-2

[8] L. Colantonio, L. Equeter, P. Dehombreux, and F. Ducobu, 'Comparison of cutting tool wear classification performance with artificial intelligence techniques', pp. 1265-1274, May 25, 2023.

[9] A. J. Bishara and J. B. Hittner, 'Testing the significance of a correlation with nonnormal data: Comparison of Pearson, Spearman, transformation, and resampling approaches.', *Psychol. Methods*, vol. 17, no. 3, pp. 399-417, Sep. 2012. https://doi.org/10.1037/a0028087

[10] T. Rabczuk and K.-J. Bathe, Eds., *Machine Learning in Modeling and Simulation: Methods and Applications*. in Computational Methods in Engineering & the Sciences. Cham: Springer International Publishing, 2023. https://doi.org/10.1007/978-3-031-36644-4

[11] H. Li, *Machine Learning Methods*. Singapore: Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-99-3917-6
their ease of implementation and optimisation. For more complex industrial cases with important variations in cutting conditions, machine, tools, and in general amount of data, ANN approaches appear to be more adaptable.

## Conflicts of interest

The authors declare that they have no conflicts of interest.

## References

[1] W. Xu and L. Cao, 'Optimal tool replacement with product quality deterioration and random tool failure', *Int. J. Prod. Res.*, vol. 53, no. 6, pp. 1736-1745, Mar. 2015. https://doi.org/10.1080/00207543.2014.957878

[2] M. Kuntoğlu et al., 'A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends', *Sensors*, vol. 21, no. 1, p. 108, 2020. https://doi.org/10.3390/s21010108

[3] F. Halila, C. Czarnota, and M. Nouari, 'Analytical stochastic modeling and experimental investigation on abrasive wear when turning difficult to cut materials', *Wear*, vol. 302, no. 1-2, pp. 1145-1157, 2013. https://doi.org/10.1016/j.wear.2012.12.055

[4] P. Muthuswamy and S. K. 'Artificial intelligence based tool condition monitoring for digital twins and industry 4.0 applications', *Int. J. Interact. Des. Manuf. IJIDem*, vol. 17, no. 3, pp. 1067-1087, Jun. 2023. https://doi.org/10.1007/s12008-022-01050-5

[5] L. Colantonio, L. Equeter, P. Dehombreux, and F. Ducobu, 'A Systematic Literature Review of Cutting Tool Wear Monitoring in Turning by Using Artificial Intelligence Techniques', *Machines*, vol. 9, no. 12, p. 351, Dec. 2021. https://doi.org/10.3390/machines9120351

[6] R. U. Baig, S. Javed, M. Khaisar, M. Shakoor, and P. Raja, 'Development of an ANN model for prediction of tool wear in turning EN9 and EN24 steel alloy', *Adv. Mech. Eng.*, vol. 13, no. 6, p. 168781402110267, Jun. 2021. https://doi.org/10.1177/16878140211026720

[7] D. Y. Pimenov, A. Bustillo, S. Wojciechowski, V. S. Sharma, M. K. Gupta, and M. Kuntoğlu, 'Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review', *J. Intell. Manuf.*, vol. 34, no. 5, pp. 2079-2121, Jun. 2023. https://doi.org/10.1007/s10845-022-01923-2

[8] L. Colantonio, L. Equeter, P. Dehombreux, and F. Ducobu, 'Comparison of cutting tool wear classification performance with artificial intelligence techniques', pp. 1265-1274, May 25, 2023.

[9] A. J. Bishara and J. B. Hittner, 'Testing the significance of a correlation with nonnormal data: Comparison of Pearson, Spearman, transformation, and resampling approaches.', *Psychol. Methods*, vol. 17, no. 3, pp. 399-417, Sep. 2012. https://doi.org/10.1037/a0028087

[10] T. Rabczuk and K.-J. Bathe, Eds., *Machine Learning in Modeling and Simulation: Methods and Applications*. in Computational Methods in Engineering & the Sciences. Cham: Springer International Publishing, 2023. https://doi.org/10.1007/978-3-031-36644-4

[11] H. Li, *Machine Learning Methods*. Singapore: Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-99-3917-6
# Combining experimental and FEM approaches to determine the influence of workpiece temperature on fundamental variables in the machining process

ORTIZ-DE-ZARATE Gorka¹,ª*, ARRIETA Iñaki M.¹,ᵇ, SORIANO Denis¹,ᶜ, ORUNA Ainara¹,ᵈ, SOLER Daniel¹,ᵉ, ARRAZOLA Pedro J.¹,ᵉ

¹Mondragon Unibertsitatea, Faculty of Engineering, Loramendi 4, Arrasate-Mondragón, 20500, Spain

ªgortizdezarate@mondragon.edu, ᵇimarrieta@mondragon.edu, ᶜdsoriano@mondragon.edu, ᵈainara.oruna@alumni.mondragon.edu, ᵉdsoler@mondragon.edu, ᵃpjarrazola@mondragon.edu

**Keywords:** Hot Machining, FEM, Forces, Temperature, Orthogonal Cutting

**Abstract.** In machining it is common practice to use lubricants to enhance machinability, resulting in improved part quality and reduced tool wear. However, emerging efforts are centered on mitigating the environmental impact of conventional lubricants by embracing cryogenic (e.g., LN₂, CO₂) and hot machining (e.g., plasma/laser-assisted machining, resistance/induction heating) among others. However, few studies analyse in depth how the initial temperature of the workpiece affects the fundamental variables of the cutting process and consequently the machinability. In this scenario, this research aimed to analyse the influence of the workpiece temperature from 20°C to 500°C, on fundamental process variables, such as forces, chip morphology, Primary Shear Zone length, shear angle, and temperatures, when orthogonal cutting steel AISI 1045 combining experimental and Finite Element Method (FEM) approaches.

**Introduction**

Machining is one of the most complex manufacturing processes due to the high thermomechanical loads that occur during the generation of the new surface. For instance, it was observed that when machining steel AISI 1045 at a cutting speed of 300 m/min and a feed of 0.2 mm, strain rates of 10⁵ 1/s, strains of 4, and a heating rate of 10⁵ K/s are reached in the shearing zones [1]. Consequently, this behaviour generates thermomechanical loads that can produce: i) premature tool wear due to chemical reactivity between tool and workpiece materials promoted by the elevated temperatures, ii) subsurface microstructural alterations, and iii) surface defects in the workpiece, among others [2].

Intending to reduce the thermomechanical loads generated during machining the use of coolants and lubricants is a very widespread action to improve the quality of the component and reduce tool wear [3,4]. These substances, commonly oil-based fluids, are applied in the tool-chip interface, mainly to remove heat from the contact zone and reduce friction, which avoids early tool wear, and improves surface roughness [5]. However, these kinds of fluids are not only harmful to the environment but also toxic for the operator working on the machine, and some alternative methods, such as Minimum Quantity Lubrication (MQL), vegetable oil-based fluids, gaseous cooling or cryogenic cooling have raised in the last years [4,6]. Nonetheless, their cost is still high in comparison to conventional cutting fluids [4].

Another way to reduce the thermomechanical loads generated by the machining process is to heat the workpiece, also known as hot machining [7,8]. The most widely used methods according to literature to heat the workpiece are [7,8]: resistance [9], flame [10-12], plasma arc [13], laser-assisted [13], and induction [14-16]. Each of these heating methods has its advantages and disadvantages and is more suitable for one machining operation or another. For instance, laser,

<figure><img src="image_1876.png" /></figure>
# Combining experimental and FEM approaches to determine the influence of workpiece temperature on fundamental variables in the machining process

ORTIZ-DE-ZARATE Gorka¹,ᵃ*, ARRIETA Iñaki M.¹,ᵇ, SORIANO Denis¹,ᶜ, ORUNA Ainara¹,ᵈ, SOLER Daniel¹,ᵉ, ARRAZOLA Pedro J.¹,ᵉ

¹Mondragon Unibertsitatea, Faculty of Engineering, Loramendi 4, Arrasate-Mondragón, 20500, Spain

ᵃgortizdezarate@mondragon.edu, ᵇimarrieta@mondragon.edu, ᶜdsoriano@mondragon.edu, ᵈainara.oruna@alumni.mondragon.edu, ᵉdsoler@mondragon.edu, ᵃpjarrazola@mondragon.edu

**Keywords:** Hot Machining, FEM, Forces, Temperature, Orthogonal Cutting

**Abstract.** In machining it is common practice to use lubricants to enhance machinability, resulting in improved part quality and reduced tool wear. However, emerging efforts are centered on mitigating the environmental impact of conventional lubricants by embracing cryogenic (e.g., LN₂, CO₂) and hot machining (e.g., plasma/laser-assisted machining, resistance/induction heating) among others. However, few studies analyse in depth how the initial temperature of the workpiece affects the fundamental variables of the cutting process and consequently the machinability. In this scenario, this research aimed to analyse the influence of the workpiece temperature from 20°C to 500°C, on fundamental process variables, such as forces, chip morphology, Primary Shear Zone length, shear angle, and temperatures, when orthogonal cutting steel AISI 1045 combining experimental and Finite Element Method (FEM) approaches.

**Introduction**

Machining is one of the most complex manufacturing processes due to the high thermomechanical loads that occur during the generation of the new surface. For instance, it was observed that when machining steel AISI 1045 at a cutting speed of 300 m/min and a feed of 0.2 mm, strain rates of 10⁵ 1/s, strains of 4, and a heating rate of 10⁵ K/s are reached in the shearing zones [1]. Consequently, this behaviour generates thermomechanical loads that can produce: i) premature tool wear due to chemical reactivity between tool and workpiece materials promoted by the elevated temperatures, ii) subsurface microstructural alterations, and iii) surface defects in the workpiece, among others [2].

Intending to reduce the thermomechanical loads generated during machining the use of coolants and lubricants is a very widespread action to improve the quality of the component and reduce tool wear [3,4]. These substances, commonly oil-based fluids, are applied in the tool-chip interface, mainly to remove heat from the contact zone and reduce friction, which avoids early tool wear, and improves surface roughness [5]. However, these kinds of fluids are not only harmful to the environment but also toxic for the operator working on the machine, and some alternative methods, such as Minimum Quantity Lubrication (MQL), vegetable oil-based fluids, gaseous cooling or cryogenic cooling have raised in the last years [4,6]. Nonetheless, their cost is still high in comparison to conventional cutting fluids [4].

Another way to reduce the thermomechanical loads generated by the machining process is to heat the workpiece, also known as hot machining [7,8]. The most widely used methods according to literature to heat the workpiece are [7,8]: resistance [9], flame [10-12], plasma arc [13], laser-assisted [13], and induction [14-16]. Each of these heating methods has its advantages and disadvantages and is more suitable for one machining operation or another. For instance, laser,

<figure><img src="image_1871.png" /></figure>
plasma, and flame gas produce a very localised heat source, while resistance or induction can generate a more homogeneous heat throughout the workpiece.

Regarding the findings obtained in the field of manufacturing and production, many researchers have investigated optimizing the hot machining process and they have reported a reduction of forces or specific cutting coefficients, improvements in surface finishing, or increase in tool life when machining different metals [9-21]. Moon and Lee [13] observed that plasma-assisted milling yielded superior outcomes when applying to AISI 1045 and Inconel 718 workpieces, showing a decrease in cutting force and enhancement in surface integrity when compared to both laser-assisted and conventional (non-preheating) machining methods. The temperature of the workpiece was calculated by using heat transfer Finite Element Method (FEM) models. Unfortunately, controlling the initial temperature of the workpiece in those heating methods (plasma or laser assisted machining) is difficult.

Therefore, other alternatives, such as induction or resistant heating are more suitable for the precise control of the initial temperature of the workpiece. Baili et al. [14] studied the turning of the Ti-5553 alloy and found that by induction heating the workpieces to 750°C, the specific cutting forces were reduced by up to 34%, improving the surface finish as well as increasing the tool life. Choi and Lee [16] observed that induction assisted milling of AISI 1045 and Inconel 718, improves conventional machining in terms of forces, surface roughness, specific cutting energy, and tool life. Platt et al. [9] used resistance heating using cartridge heaters to increase the temperature of the workpiece (AISI M3:2 and AISI H11). They observed that as the temperature of the workpiece material increases, the yield strength and the hardness are reduced. This way it was possible to reduce the forces and the necessary power for the machining, increasing the process performance in terms of energy efficiency and tool life.

Hence, there are a few hot machining studies on AISI 1045 steel [13,16], and none of them are for turning. Additionally, most of them lack a more in-depth analysis of the influence of the initial temperature of the workpiece on scientific variables (forces, chip morphology, temperature, strain, strain rate, etc.), mainly because of the difficulty of measuring them experimentally.

To overcome that issue, a few studies are using numerical FEM modelling to gain a better understanding of the thermomechanical loads occurring in the hot machining process that are difficult to measure experimentally. The most relevant work was carried out by Xu et al. [22] and Parida and Maity [23]. Xu et al. [22] analyse the influence of the cutting speed, feed rate, depth of cut, and heating current (related to the workpiece temperature) on cutting forces when electric hot machining steel AISI 52100. Parida and Maity [23] studied the influence of nose radius (0.4, 0.8, and 1.2 mm) and initial workpiece temperature (20°C and 600°C) on forces, chip temperature, contact length, and chip morphology, using DEFORM FEM software for gas flame turning Inconel 718. Therefore, few studies develop FEM predictive models for hot machining applications, as also highlighted in the hot machining review paper of Pandey and Datta [1], and none of them are for steel AISI 1045.

To the best of the authors' knowledge, there are no studies in the literature that combine experimental tests and modelling to try to understand the influence of the initial workpiece temperature on fundamental variables when machining AISI 1045 steel. For this purpose, a new experimental setup was developed that enables the precise control of the initial workpiece temperature. Then, orthogonal cutting experimental tests were carried out at a wide range of initial workpiece temperatures (20-500°C) to analyse its influence on fundamental variables (forces, chip morphology, forces, chip morphology, Primary Shear Zone length, shear angle, and temperatures shear angle, and temperatures). Finally, a FEM model was developed for hot machining and validated against experimental outcomes, to understand the experimental results and gain knowledge about the thermomechanical loads variation with initial workpiece temperature.
plasma, and flame gas produce a very localised heat source, while resistance or induction can generate a more homogeneous heat throughout the workpiece.

Regarding the findings obtained in the field of manufacturing and production, many researchers have investigated optimizing the hot machining process and they have reported a reduction of forces or specific cutting coefficients, improvements in surface finishing, or increase in tool life when machining different metals [9-21]. Moon and Lee [13] observed that plasma-assisted milling yielded superior outcomes when applying to AISI 1045 and Inconel 718 workpieces, showing a decrease in cutting force and enhancement in surface integrity when compared to both laser-assisted and conventional (non-preheating) machining methods. The temperature of the workpiece was calculated by using heat transfer Finite Element Method (FEM) models. Unfortunately, controlling the initial temperature of the workpiece in those heating methods (plasma or laser assisted machining) is difficult.

Therefore, other alternatives, such as induction or resistant heating are more suitable for the precise control of the initial temperature of the workpiece. Baili et al. [14] studied the turning of the Ti-5553 alloy and found that by induction heating the workpieces to 750°C, the specific cutting forces were reduced by up to 34%, improving the surface finish as well as increasing the tool life. Choi and Lee [16] observed that induction assisted milling of AISI 1045 and Inconel 718, improves conventional machining in terms of forces, surface roughness, specific cutting energy, and tool life. Platt et al. [9] used resistance heating using cartridge heaters to increase the temperature of the workpiece (AISI M3:2 and AISI H11). They observed that as the temperature of the workpiece material increases, the yield strength and the hardness are reduced. This way it was possible to reduce the forces and the necessary power for the machining, increasing the process performance in terms of energy efficiency and tool life.

Hence, there are a few hot machining studies on AISI 1045 steel [13,16], and none of them are for turning. Additionally, most of them lack a more in-depth analysis of the influence of the initial temperature of the workpiece on scientific variables (forces, chip morphology, temperature, strain, strain rate, etc.), mainly because of the difficulty of measuring them experimentally.

To overcome that issue, a few studies are using numerical FEM modelling to gain a better understanding of the thermomechanical loads occurring in the hot machining process that are difficult to measure experimentally. The most relevant work was carried out by Xu et al. [22] and Parida and Maity [23]. Xu et al. [22] analyse the influence of the cutting speed, feed rate, depth of cut, and heating current (related to the workpiece temperature) on cutting forces when electric hot machining steel AISI 52100. Parida and Maity [23] studied the influence of nose radius (0.4, 0.8, and 1.2 mm) and initial workpiece temperature (20°C and 600°C) on forces, chip temperature, contact length, and chip morphology, using DEFORM FEM software for gas flame turning Inconel 718. Therefore, few studies develop FEM predictive models for hot machining applications, as also highlighted in the hot machining review paper of Pandey and Datta [1], and none of them are for steel AISI 1045.

To the best of the authors' knowledge, there are no studies in the literature that combine experimental tests and modelling to try to understand the influence of the initial workpiece temperature on fundamental variables when machining AISI 1045 steel. For this purpose, a new experimental setup was developed that enables the precise control of the initial workpiece temperature. Then, orthogonal cutting experimental tests were carried out at a wide range of initial workpiece temperatures (20-500°C) to analyse its influence on fundamental variables (forces, chip morphology, forces, chip morphology, Primary Shear Zone length, shear angle, and temperatures shear angle, and temperatures). Finally, a FEM model was developed for hot machining and validated against experimental outcomes, to understand the experimental results and gain knowledge about the thermomechanical loads variation with initial workpiece temperature.
Methodology

The experimental orthogonal cutting tests were carried out with a linear cutting setup on a Lagun GVC 1000-HS CNC milling machine that incorporated a Kistler 9139AA dynamometer to measure the forces (see Fig. 1). The workpiece material was steel AISI 1045 (155 Bhn), which was machined at constant cutting speed (30 m/min) and feed (0.2 mm) in dry conditions.

The initial temperature of the workpiece was varied between room temperature and 500°C (20, 300, 350, 400, 450 and 500°C). Thermoelectric resistors were used to increase the temperature of the workpiece in a controlled manner up to the target temperature. For this purpose, a control system was developed that incorporates a thermocouple clamped to the workpiece to ensure its temperature before the machining process. Additional thermocouples were also incorporated in preliminary tests in different areas of the workpiece (lateral sides, and surface to be machined) to analyse the homogeneity of the temperature across the workpiece for all the temperatures analysed (20-500°C). The difference between the values of the three thermocouples was less than 10°C. Therefore, although there may be a temperature gradient within the cut area due to the interaction of the thin workpiece with the environment, its effect does not seem to be very significant.

<figure><img src="image_1877.png" /><figcaption>Fig. 1. Experimental setup for hot machining tests.</figcaption></figure>

Furthermore, a Finite Element Method (FEM) model was created and validated for the prediction of fundamental variables (forces, chip morphology, temperatures, etc.). This model aimed to enhance understanding of the thermomechanical loads generated during the cutting process and study how they are affected by the initial workpiece temperature. Machining simulations were carried out using AdvantEdge™-2D V8.0, a specialized commercial finite element software for machining applications. The software employs a coupled thermo-elastoplastic Lagrangian code with continuous remeshing and adaptive meshing to avoid mesh distortion.

A 2D orthogonal cutting plane strain model was developed. The applied boundary conditions were categorized into thermal and mechanical. Fig. 2 illustrates all the boundary conditions that were implemented in the model. For the mechanical boundaries, the tool was fixed in two directions (X and Y), while the workpiece moved along the X direction at the cutting speed, and its displacement in the Y direction was restricted at the same nodes. Regarding the thermal boundary conditions, the outer counter of the workpiece and the rake and relief tool faces were treated as adiabatic. On the other hand, the bottom and top faces of the insert were designated as isothermal surfaces. Furthermore, conduction at the tool-chip interface was allowed. The initial temperature of the workpiece was also applied to the model, as in the experimental tests.
Methodology

The experimental orthogonal cutting tests were carried out with a linear cutting setup on a Lagun GVC 1000-HS CNC milling machine that incorporated a Kistler 9139AA dynamometer to measure the forces (see Fig. 1). The workpiece material was steel AISI 1045 (155 Bhn), which was machined at constant cutting speed (30 m/min) and feed (0.2 mm) in dry conditions.

The initial temperature of the workpiece was varied between room temperature and 500°C (20, 300, 350, 400, 450 and 500°C). Thermoelectric resistors were used to increase the temperature of the workpiece in a controlled manner up to the target temperature. For this purpose, a control system was developed that incorporates a thermocouple clamped to the workpiece to ensure its temperature before the machining process. Additional thermocouples were also incorporated in preliminary tests in different areas of the workpiece (lateral sides, and surface to be machined) to analyse the homogeneity of the temperature across the workpiece for all the temperatures analysed (20-500°C). The difference between the values of the three thermocouples was less than 10°C. Therefore, although there may be a temperature gradient within the cut area due to the interaction of the thin workpiece with the environment, its effect does not seem to be very significant.

<figure><img src="image_1872.png" /><figcaption>Fig. 1. Experimental setup for hot machining tests.</figcaption></figure>

Furthermore, a Finite Element Method (FEM) model was created and validated for the prediction of fundamental variables (forces, chip morphology, temperatures, etc.). This model aimed to enhance understanding of the thermomechanical loads generated during the cutting process and study how they are affected by the initial workpiece temperature. Machining simulations were carried out using AdvantEdge™-2D V8.0, a specialized commercial finite element software for machining applications. The software employs a coupled thermo-elastoplastic Lagrangian code with continuous remeshing and adaptive meshing to avoid mesh distortion.

A 2D orthogonal cutting plane strain model was developed. The applied boundary conditions were categorized into thermal and mechanical. Fig. 2 illustrates all the boundary conditions that were implemented in the model. For the mechanical boundaries, the tool was fixed in two directions (X and Y), while the workpiece moved along the X direction at the cutting speed, and its displacement in the Y direction was restricted at the same nodes. Regarding the thermal boundary conditions, the outer counter of the workpiece and the rake and relief tool faces were treated as adiabatic. On the other hand, the bottom and top faces of the insert were designated as isothermal surfaces. Furthermore, conduction at the tool-chip interface was allowed. The initial temperature of the workpiece was also applied to the model, as in the experimental tests.
To achieve accurate results, the simulation employed a minimum element size of 5 µm, as illustrated in Fig. 2. Consequently, with a 4-core parallel processing setup, each simulation required approximately one hour.

The material properties of the tool and workpiece were obtained from the extensive library of the software. Nonetheless, the nominal hardness of the workpiece material of the software database is 200 Bhn, which is significantly higher than the one of the experimental workpiece material (155 Bhn). Therefore, the hardness of the workpiece material was changed in the software to match the experimental one. The software adjusted the material properties and the results obtained were closer to reality. Unfortunately, the software does not give any further information on how this adjustment is made.

The chosen friction law for the tool-chip contact was a sticking-sliding model [24]. This model was initially validated through the Split Tool methodology [25,26] and more recently supported by the Partially Restricted Contact Length Tools (PRCLT) approach [27], establishing as the most representative model for machining applications. A friction coefficient of 1 was specified based on the findings of these research works, which consistently concluded that it is the most suitable value for machining [25-27].

<figure><img src="image_1878.png" /><figcaption>Fig. 2. Boundary conditions and mesh size of the orthogonal cutting FEM model.</figcaption></figure>

The micro-geometry of the insert was assessed using an Alicona IFG4 optical profilometer. The configuration used consisted of a 10x polarised lens with a ring light to improve the quality of the profile acquisition. The vertical resolution was 1.5 µm, and the lateral resolution was 2.3 µm. The average cutting edge radius measured was 25±5 µm. The rake ($γ$) and relief ($α$) angles of the inserts were 0° and 10° respectively, which were finally 6° and 4° respectively when the insert was positioned in the toolholder.

## Results and discussion

Fig. 3 shows the cutting and feed forces ($F_c$ and $F_f$), per unit of the depth of cut, obtained from the FEM model and experimentally. The FEM model accurately predicted the experimental trends and quantitative values. In the cutting forces, the average relative error was 8% and the maximum was
To achieve accurate results, the simulation employed a minimum element size of 5 µm, as illustrated in Fig. 2. Consequently, with a 4-core parallel processing setup, each simulation required approximately one hour.

The material properties of the tool and workpiece were obtained from the extensive library of the software. Nonetheless, the nominal hardness of the workpiece material of the software database is 200 Bhn, which is significantly higher than the one of the experimental workpiece material (155 Bhn). Therefore, the hardness of the workpiece material was changed in the software to match the experimental one. The software adjusted the material properties and the results obtained were closer to reality. Unfortunately, the software does not give any further information on how this adjustment is made.

The chosen friction law for the tool-chip contact was a sticking-sliding model [24]. This model was initially validated through the Split Tool methodology [25,26] and more recently supported by the Partially Restricted Contact Length Tools (PRCLT) approach [27], establishing as the most representative model for machining applications. A friction coefficient of 1 was specified based on the findings of these research works, which consistently concluded that it is the most suitable value for machining [25-27].

<figure><img src="image_1873.png" /><figcaption>Fig. 2. Boundary conditions and mesh size of the orthogonal cutting FEM model.</figcaption></figure>

The micro-geometry of the insert was assessed using an Alicona IFG4 optical profilometer. The configuration used consisted of a 10x polarised lens with a ring light to improve the quality of the profile acquisition. The vertical resolution was 1.5 µm, and the lateral resolution was 2.3 µm. The average cutting edge radius measured was 25±5 µm. The rake (γ) and relief (α) angles of the inserts were 0° and 10° respectively, which were finally 6° and 4° respectively when the insert was positioned in the toolholder.

## Results and discussion

Fig. 3 shows the cutting and feed forces ($F_c$ and $F_f$), per unit of the depth of cut, obtained from the FEM model and experimentally. The FEM model accurately predicted the experimental trends and quantitative values. In the cutting forces, the average relative error was 8% and the maximum was
17% (for the initial workpiece temperature of 20°C). The overprediction of the cutting force at room temperature seems to be related to the flow stress model. This was obtained from the software database without being self-characterized, and there may be variations between the properties of the material used in the experimental tests and the one used to develop the flow stress model of the database. The feed forces were systematically overpredicted with an average relative error of 16% and a maximum of 22% (for the initial workpiece temperature of 450°C).

Regarding the influence of the initial temperature of the workpiece, it can be observed that the forces decrease when increasing the initial temperature from 20°C to 300°C. Then, above 300°C they remain constant, which was not initially expected, as the thermal softening in literature is more evident above 300-400°C than from 20°C to 300°C [28].

<figure><img src="image_1879.png" /><figcaption>Fig. 3. Experimental and predicted forces.</figcaption></figure>

There were also unexpected trends in the chip morphology (see Fig. 4), as the chip thickness changed non-linearly with the initial workpiece temperature. The chip thickness is reduced from 20°C to 350-400°C and then increases significantly from this temperature onwards. The trends observed experimentally were accurately predicted by the FEM model for all the analysed cutting conditions with an average relative error of 6% and a maximum of 14% for the initial workpiece temperature of 300°C. A continuous chip was obtained for all the simulations and experimentally.

To gain further insight into the reasons for the changing trends in forces and chip thickness with initial workpiece temperature, the shear angles ($\phi$) and the Primary Shear Zone (PSZ) length were extracted from the simulations (see Fig. 4). As can be observed, the influence of the initial workpiece temperature on the chip thickness is fully related to the shear angle. The increase of the shear angle up to approximately the initial workpiece temperature of 350-400°C leads to a reduction of the chip thickness, while from that temperature onwards the shear angle decreases and consequently the chip thickness increases. In addition, an increase in chip thickness or reduction of the shear angle means a longer PSZ length, which in turn leads to increased forces, as long as the flow stress of the material in the PSZ is constant. For the flow stress to be constant, the variables affecting it (strain, strain rate, and temperature) must be constant.
17% (for the initial workpiece temperature of 20°C). The overprediction of the cutting force at room temperature seems to be related to the flow stress model. This was obtained from the software database without being self-characterized, and there may be variations between the properties of the material used in the experimental tests and the one used to develop the flow stress model of the database. The feed forces were systematically overpredicted with an average relative error of 16% and a maximum of 22% (for the initial workpiece temperature of 450°C).

Regarding the influence of the initial temperature of the workpiece, it can be observed that the forces decrease when increasing the initial temperature from 20°C to 300°C. Then, above 300°C they remain constant, which was not initially expected, as the thermal softening in literature is more evident above 300-400°C than from 20°C to 300°C [28].

<figure><img src="image_1874.png" /><figcaption>Fig. 3. Experimental and predicted forces.</figcaption></figure>

There were also unexpected trends in the chip morphology (see Fig. 4), as the chip thickness changed non-linearly with the initial workpiece temperature. The chip thickness is reduced from 20°C to 350-400°C and then increases significantly from this temperature onwards. The trends observed experimentally were accurately predicted by the FEM model for all the analysed cutting conditions with an average relative error of 6% and a maximum of 14% for the initial workpiece temperature of 300°C. A continuous chip was obtained for all the simulations and experimentally.

To gain further insight into the reasons for the changing trends in forces and chip thickness with initial workpiece temperature, the shear angles ($\phi$) and the Primary Shear Zone (PSZ) length were extracted from the simulations (see Fig. 4). As can be observed, the influence of the initial workpiece temperature on the chip thickness is fully related to the shear angle. The increase of the shear angle up to approximately the initial workpiece temperature of 350-400°C leads to a reduction of the chip thickness, while from that temperature onwards the shear angle decreases and consequently the chip thickness increases. In addition, an increase in chip thickness or reduction of the shear angle means a longer PSZ length, which in turn leads to increased forces, as long as the flow stress of the material in the PSZ is constant. For the flow stress to be constant, the variables affecting it (strain, strain rate, and temperature) must be constant.
<figure><img src="image_1880.png" /><figcaption>Fig. 4. Experimental and predicted chip thickness, shear angle, and Primary Shear Zone (PSZ) length.</figcaption></figure>

Therefore, to analyse the flow stress behaviour in the PSZ the strain, strain rate, and temperature were extracted. 20 points were measured along the PSZ from each simulation (see Fig. 5). As can be seen, only the temperature is affected by the initial workpiece temperature, while the strain and strain rate are constant. Hence, it seems that there is a trade-off between the increase of the temperature in the PSZ with the increase of the length of the shear zone at temperatures above 300°C which keeps the forces almost constant. In addition, it should be noted that it was reported in the literature coupling between temperature and strain rate in AISI 1045 (higher strain rate hardening at higher temperatures) [28]. Therefore, there may be additional reasons related to material properties that could explain this phenomenon.

Therefore, the combination of the FEM simulation with the experimental tests has allowed to analyse how the initial temperature of the workpiece influences both the forces and the chip morphology and how they are affected by the scientific variables of the PSZ (shear angle, PSZ length, strain, strain rate and temperature).
<figure><img src="image_1875.png" /><figcaption>Fig. 4. Experimental and predicted chip thickness, shear angle, and Primary Shear Zone (PSZ) length.</figcaption></figure>

Therefore, to analyse the flow stress behaviour in the PSZ the strain, strain rate, and temperature were extracted. 20 points were measured along the PSZ from each simulation (see Fig. 5). As can be seen, only the temperature is affected by the initial workpiece temperature, while the strain and strain rate are constant. Hence, it seems that there is a trade-off between the increase of the temperature in the PSZ with the increase of the length of the shear zone at temperatures above 300°C which keeps the forces almost constant. In addition, it should be noted that it was reported in the literature coupling between temperature and strain rate in AISI 1045 (higher strain rate hardening at higher temperatures) [28]. Therefore, there may be additional reasons related to material properties that could explain this phenomenon.

Therefore, the combination of the FEM simulation with the experimental tests has allowed to analyse how the initial temperature of the workpiece influences both the forces and the chip morphology and how they are affected by the scientific variables of the PSZ (shear angle, PSZ length, strain, strain rate and temperature).
<figure><img src="image_1881.png" /><figcaption>Fig. 5. Plastic strain, strain rate, and temperature along the Primary Shear Zone (PSZ).</figcaption></figure>

After validation of the FEM model, the maximum tool temperature obtained for each initial temperature of the workpiece was extracted from the FEM model when the thermal steady state was reached. Fig. 6 shows: i) the maximum tool temperature, ii) the average PSZ temperature obtained from the data of Fig. 5, iii) the difference between the maximum tool temperature and the initial workpiece temperature, and iv) the difference between the maximum tool temperature and the average PSZ temperature. As can be seen in all cases the temperature change in the cutting zone is linearly proportional to the initial workpiece temperature.

<figure><img src="image_1882.png" /><figcaption>Fig. 6. Predicted temperatures.</figcaption></figure>

In addition, it was observed that the difference between the initial workpiece temperature and the maximum tool temperature, is not constant. For instance, if the initial workpiece temperature is 20°C the maximum tool temperature reaches 475°C, resulting in a difference of 455°C. However, if the workpiece temperature is raised to 500°C, the tool temperature reaches 745°C giving a difference of only 245°C. On the contrary, the difference between the maximum tool temperature and the PSZ temperature remains almost constant, with a slight decrease, as the initial workpiece temperature is increased.
<figure><img src="image_1876.png" /><figcaption>Fig. 5. Plastic strain, strain rate, and temperature along the Primary Shear Zone (PSZ).</figcaption></figure>

After validation of the FEM model, the maximum tool temperature obtained for each initial temperature of the workpiece was extracted from the FEM model when the thermal steady state was reached. Fig. 6 shows: i) the maximum tool temperature, ii) the average PSZ temperature obtained from the data of Fig. 5, iii) the difference between the maximum tool temperature and the initial workpiece temperature, and iv) the difference between the maximum tool temperature and the average PSZ temperature. As can be seen in all cases the temperature change in the cutting zone is linearly proportional to the initial workpiece temperature.

<figure><img src="image_1877.png" /><figcaption>Fig. 6. Predicted temperatures.</figcaption></figure>

In addition, it was observed that the difference between the initial workpiece temperature and the maximum tool temperature, is not constant. For instance, if the initial workpiece temperature is 20°C the maximum tool temperature reaches 475°C, resulting in a difference of 455°C. However, if the workpiece temperature is raised to 500°C, the tool temperature reaches 745°C giving a difference of only 245°C. On the contrary, the difference between the maximum tool temperature and the PSZ temperature remains almost constant, with a slight decrease, as the initial workpiece temperature is increased.
This means that an increase in the initial workpiece temperature does not mean an increase of the same magnitude in the maximum tool temperature, and this increase has a minimal effect on the PSZ temperature. This implies that wear can be kept low by selecting appropriate initial workpiece temperatures that allow reduced forces without resulting in increased tool wear due to an excessive increase in tool temperature.

## Summary and conclusions

The influence of the initial workpiece temperature on fundamental variables (forces, chip morphology, shear angle, Primary Shear Zone length, strain, strain rate, and temperature) was investigated through a combination of experimental and Finite Element Method (FEM) approaches. The FEM model demonstrated precise predictions of cutting and feed forces, exhibiting an average relative error of 8% and 16%, respectively. Additionally, it accurately predicted chip thickness with a minimal error of 6%.

In the force analysis, it was observed that the forces were reduced as the initial workpiece temperature was increased from 20°C to 300°C. However, from 300°C up to 500°C, the forces remained constant. Analysing the Primary Shear Zone (PSZ), it was observed that the non-variation of the forces could come from the non-linear change of the shear angle and PSZ length together with the variation of the workpiece material properties (flow stress) in this zone. It was also found a correlation between the initial workpiece temperature and chip thickness, which was also related to the non-linear behaviour of the shear angle and PSZ length.

In addition, it was observed that a rise in the initial workpiece temperature does not proportionally increase the maximum tool temperature. Consequently, by choosing suitable initial workpiece temperatures that enable reduced forces without causing an excessive elevation in tool temperature, tool wear can be effectively minimized.

Taking all into account, hot machining seems to be a good alternative to reduce machining forces as long as there is a detailed knowledge of the influence of the temperature on the material properties (thermal softening, coupling between temperature and strain rate hardening, etc.). Not having sufficient knowledge of material behaviour can lead to: i) unnecessary energy waste by increasing the initial temperature of the workpiece to values that are not beneficial (e.g. going from 300°C to 500°C when machining AISI 1045), and ii) increasing the tool temperature excessively which could promote tool wear.

## Acknowledgments

The authors hereby thank the projects SURFNANOCUT (RTI2018-095463-B-C21 and RTI2018-095463-B-C22), and TAILORSURF (PID2022-139655OB-I00).

## References

[1] P. J. Arrazola, F. Meslin, F. Le Maitre, S. Marya, Material flow stress sensitivity analysis in numerical cutting modeling, in AIP Conference Proceedings. 712/1 (2004) 1408-1413. https://doi.org/10.1063/1.1766726

[2] Z. Liao, A. la Monaca, J. Murray, A. Speidel, D. Ushmaev, A. Clare, D. Axinte, R. M'Saoubi, Surface integrity in metal machining-Part I: Fundamentals of surface characteristics and formation mechanisms, International Journal of Machine Tools and Manufacture. 162 (2021) 103687. https://doi.org/10.1016/j.ijmachtools.2020.103687

[3] G. W. A. Kui, S. Islam, M. M. Reddy, N. Khandoker, V. L. C. Chen, Recent progress and evolution of coolant usages in conventional machining methods: a comprehensive review, International Journal of Advanced Manufacturing Technology. 119/1-2 (2022) 3-40. https://doi.org/10.1007/s00170-021-08182-0
This means that an increase in the initial workpiece temperature does not mean an increase of the same magnitude in the maximum tool temperature, and this increase has a minimal effect on the PSZ temperature. This implies that wear can be kept low by selecting appropriate initial workpiece temperatures that allow reduced forces without resulting in increased tool wear due to an excessive increase in tool temperature.

## Summary and conclusions

The influence of the initial workpiece temperature on fundamental variables (forces, chip morphology, shear angle, Primary Shear Zone length, strain, strain rate, and temperature) was investigated through a combination of experimental and Finite Element Method (FEM) approaches. The FEM model demonstrated precise predictions of cutting and feed forces, exhibiting an average relative error of 8% and 16%, respectively. Additionally, it accurately predicted chip thickness with a minimal error of 6%.

In the force analysis, it was observed that the forces were reduced as the initial workpiece temperature was increased from 20°C to 300°C. However, from 300°C up to 500°C, the forces remained constant. Analysing the Primary Shear Zone (PSZ), it was observed that the non-variation of the forces could come from the non-linear change of the shear angle and PSZ length together with the variation of the workpiece material properties (flow stress) in this zone. It was also found a correlation between the initial workpiece temperature and chip thickness, which was also related to the non-linear behaviour of the shear angle and PSZ length.

In addition, it was observed that a rise in the initial workpiece temperature does not proportionally increase the maximum tool temperature. Consequently, by choosing suitable initial workpiece temperatures that enable reduced forces without causing an excessive elevation in tool temperature, tool wear can be effectively minimized.

Taking all into account, hot machining seems to be a good alternative to reduce machining forces as long as there is a detailed knowledge of the influence of the temperature on the material properties (thermal softening, coupling between temperature and strain rate hardening, etc.). Not having sufficient knowledge of material behaviour can lead to: i) unnecessary energy waste by increasing the initial temperature of the workpiece to values that are not beneficial (e.g. going from 300°C to 500°C when machining AISI 1045), and ii) increasing the tool temperature excessively which could promote tool wear.

## Acknowledgments

The authors hereby thank the projects SURFNANOCUT (RTI2018-095463-B-C21 and RTI2018-095463-B-C22), and TAILORSURF (PID2022-139655OB-I00).

## References

[1] P. J. Arrazola, F. Meslin, F. Le Maitre, S. Marya, Material flow stress sensitivity analysis in numerical cutting modeling, in AIP Conference Proceedings. 712/1 (2004) 1408-1413. https://doi.org/10.1063/1.1766726

[2] Z. Liao, A. la Monaca, J. Murray, A. Speidel, D. Ushmaev, A. Clare, D. Axinte, R. M'Saoubi, Surface integrity in metal machining-Part I: Fundamentals of surface characteristics and formation mechanisms, International Journal of Machine Tools and Manufacture. 162 (2021) 103687. https://doi.org/10.1016/j.ijmachtools.2020.103687

[3] G. W. A. Kui, S. Islam, M. M. Reddy, N. Khandoker, V. L. C. Chen, Recent progress and evolution of coolant usages in conventional machining methods: a comprehensive review, International Journal of Advanced Manufacturing Technology. 119/1-2 (2022) 3-40. https://doi.org/10.1007/s00170-021-08182-0
[4] E. Benedicto, D. Carou, E. M. Rubio, Technical, Economic and Environmental Review of the Lubrication/Cooling Systems Used in Machining Processes, Procedia engineering. 184 (2017) 99-116. https://doi.org/10.1016/j.proeng.2017.04.075

[5] S. H. Musavi, B. Davoodi, B. Eskandari, Evaluation of surface roughness and optimization of cutting parameters in turning of AA2024 alloy under different cooling-lubrication conditions using RSM method, Journal of Central South University. 27 (2020) 1714-1728. https://doi.org/10.1007/s11771-020-4402-2

[6] S. Pervaiz, I. Deiab, A. Rashid, M. Nicolescu, Minimal quantity cooling lubrication in turning of Ti6Al4V: Influence on surface roughness, cutting force and tool wear, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 231/9 (2017) 1542-1558. https://doi.org/10.1177/095440541559994

[7] K. Pandey, S. Datta, Hot machining of difficult-to-cut materials: A review, Materials Today: Proceedings. 44 (2021) 2710-2715. https://doi.org/10.1016/j.matpr.2020.12.687

[8] K. A. Mohammed, J. J. Abdulhameed, E. S. Al-Ameen, The effectiveness of hot machining process for the machinability of hard to cut materials: A review, IOP Conference Series: Materials Science and Engineering. 870/1 (2020) 012140. https://doi.org/10.1088/1757-899X/870/1/012140

[9] T. Platt, A. Meijer, D. Biermann, Conduction-based thermally assisted micromilling process for cutting difficult-to-machine materials, Journal of Manufacturing and Materials Processing. 4/2 (2020) 34. https://doi.org/10.3390/jmmp4020034

[10] N. M. Kamdar, P. V. K. Patel, Experimental Investigation of Machining Parameters of EN 36 Steel using Tungsten Carbide Cutting Tool during Hot Machining, International Journal of Engineering Research and Applications. 2 (2012) 1833-1838.

[11] N. Tosun, L. Ozler, Optimisation for hot turning operations with multiple performance characteristics, International Journal of Advanced Manufacturing Technology. 23 (2004) 777-782. https://doi.org/10.1007/s00170-003-1672-4

[12] R. D. Rajopadhye, M. T. Telsang, N. S. Dhole, Experimental Setup for Hot Machining Process to Increase Tool Life with Torch Flame, Second international conference on emerging trends in engineering (SICETE). (2009) 58-62.

[13] S. H. Moon, C. M. Lee, A study on the machining characteristics using plasma assisted machining of AISI 1045 steel and Inconel 718, International Journal of Mechanical Sciences. 142 (2018) 595-602. https://doi.org/10.1016/j.ijmecsci.2018.05.020

[14] M. Baili, V. Wagner, G. Dessein, J. Sallaberry, D. Lallement, An experimental investigation of hot machining with induction to improve Ti-5553 machinability, Applied mechanics and Materials. 62 (2011) 67-76. https://doi.org/10.4028/www.scientific.net/AMM.62.67

[15] S. Ranganathan, T. Senthilvelan, Multi-response optimization of machining parameters in hot turning using grey analysis, The International Journal of Advanced Manufacturing Technology. 56 (2011) 455-462. https://doi.org/10.1007/s00170-011-3198-5

[16] Y. H. Choi, C. M. Lee, A study on the machining characteristics of AISI 1045 steel and inconel 718 with circular cone shape in induction assisted machining, Journal of Manufacturing Processes. 34 (2018) 463-476. https://doi.org/10.1016/j.jmapro.2018.06.023

[17] O. Çakır, E. Altan, Hot machining of high manganese steel: a review, Trends in the development of machinery and associated technology. (2008) 105-108.
[4] E. Benedicto, D. Carou, E. M. Rubio, Technical, Economic and Environmental Review of the Lubrication/Cooling Systems Used in Machining Processes, Procedia engineering. 184 (2017) 99-116. https://doi.org/10.1016/j.proeng.2017.04.075

[5] S. H. Musavi, B. Davoodi, B. Eskandari, Evaluation of surface roughness and optimization of cutting parameters in turning of AA2024 alloy under different cooling-lubrication conditions using RSM method, Journal of Central South University. 27 (2020) 1714-1728. https://doi.org/10.1007/s11771-020-4402-2

[6] S. Pervaiz, I. Deiab, A. Rashid, M. Nicolescu, Minimal quantity cooling lubrication in turning of Ti6Al4V: Influence on surface roughness, cutting force and tool wear, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture. 231/9 (2017) 1542-1558. https://doi.org/10.1177/095440541559994

[7] K. Pandey, S. Datta, Hot machining of difficult-to-cut materials: A review, Materials Today: Proceedings. 44 (2021) 2710-2715. https://doi.org/10.1016/j.matpr.2020.12.687

[8] K. A. Mohammed, J. J. Abdulhameed, E. S. Al-Ameen, The effectiveness of hot machining process for the machinability of hard to cut materials: A review, IOP Conference Series: Materials Science and Engineering. 870/1 (2020) 012140. https://doi.org/10.1088/1757-899X/870/1/012140

[9] T. Platt, A. Meijer, D. Biermann, Conduction-based thermally assisted micromilling process for cutting difficult-to-machine materials, Journal of Manufacturing and Materials Processing. 4/2 (2020) 34. https://doi.org/10.3390/jmmp4020034

[10] N. M. Kamdar, P. V. K. Patel, Experimental Investigation of Machining Parameters of EN 36 Steel using Tungsten Carbide Cutting Tool during Hot Machining, International Journal of Engineering Research and Applications. 2 (2012) 1833-1838.

[11] N. Tosun, L. Ozler, Optimisation for hot turning operations with multiple performance characteristics, International Journal of Advanced Manufacturing Technology. 23 (2004) 777-782. https://doi.org/10.1007/s00170-003-1672-4

[12] R. D. Rajopadhye, M. T. Telsang, N. S. Dhole, Experimental Setup for Hot Machining Process to Increase Tool Life with Torch Flame, Second international conference on emerging trends in engineering (SICETE). (2009) 58-62.

[13] S. H. Moon, C. M. Lee, A study on the machining characteristics using plasma assisted machining of AISI 1045 steel and Inconel 718, International Journal of Mechanical Sciences. 142 (2018) 595-602. https://doi.org/10.1016/j.ijmecsci.2018.05.020

[14] M. Baili, V. Wagner, G. Dessein, J. Sallaberry, D. Lallement, An experimental investigation of hot machining with induction to improve Ti-5553 machinability, Applied mechanics and Materials. 62 (2011) 67-76. https://doi.org/10.4028/www.scientific.net/AMM.62.67

[15] S. Ranganathan, T. Senthilvelan, Multi-response optimization of machining parameters in hot turning using grey analysis, The International Journal of Advanced Manufacturing Technology. 56 (2011) 455-462. https://doi.org/10.1007/s00170-011-3198-5

[16] Y. H. Choi, C. M. Lee, A study on the machining characteristics of AISI 1045 steel and inconel 718 with circular cone shape in induction assisted machining, Journal of Manufacturing Processes. 34 (2018) 463-476. https://doi.org/10.1016/j.jmapro.2018.06.023

[17] O. Çakır, E. Altan, Hot machining of high manganese steel: a review, Trends in the development of machinery and associated technology. (2008) 105-108.
[18] N. R. Modh, G. D. Mistry, K. B. Rathod, An experimental investigation to optimize the process parameters of AISI 52100 steel in hot machining, International Journal of Engineering Research and Applications. 1/3 (2011) 483-489.

[19] A. K. Kumar, P. Venkataramaiah, Optimization of Process parameters in hot machining of Inconel 718 alloy using FEM, International Journal of Applied Engineering Research. 13/5 (2018) 2158-2162.

[20] S. K. Thandra, S. K. Choudhury, Effect of cutting parameters on cutting force, surface finish and tool wear in hot machining, International Journal of Machining and Machinability of Materials. 7/3-4 (2010) 260-283. https://doi.org/10.1504/IJMMM.2010.03307

[21] A. K. Parida, K. Maity, Hot machining of Ti – 6Al – 4V : FE analysis and experimental validation, Sādhanā. 44 (2019) 1-6. https://doi.org/10.1007/s12046-019-1127-8

[22] W. Xu, X. Liu, J. Sun, L. Zhang, Finite element simulation and experimental research on electric hot machining, International Journal of Advanced Manufacturing Technology. 66 (2013) 407-415. https://doi.org/10.1007/s00170-012-4335-5

[23] A. K. Parida, K. Maity, Effect of nose radius on forces, and process parameters in hot machining of Inconel 718 using finite element analysis, Engineering Science and Technology, an International Journal. 20/2 (2017) 687-693. https://doi.org/10.1016/j.jestch.2016.10.006

[24] N. N. Zorev, Inter-relationship between shear processes occurring along tool face and shear plane in metal cutting, International research in production engineering. 49 (1963) 142-152.

[25] S. Kato, K. Yamaguchi, M. Yamada, Stress distribution at the interface between tool and chip in machining, Journal of Manufacturing Science and Engineering, Transactions of the ASME. 94/2 (1972) 693-689. https://doi.org/10.1115/1.3428229

[26] K. Maekawa, T. Kitagawa, T. H. C. Childs, Friction characteristics at tool-chip interface in steel machining, Tribology Series. 32 (1997) 559-567. https://doi.org/10.1016/S0167-8922(08)70482-6

[27] G. Ortiz de Zarate, A. Madariaga, P. J. Arrazola, T. H. C. Childs, A novel methodology to characterize tool-chip contact in metal cutting using partially restricted contact length tools, CIRP Annals. 70/1 (2021) 61-64. https://doi.org/10.1016/j.cirp.2021.03.002

[28] M. Saez-de-Buruaga, P. Aristimuño, D. Soler, E. D'Eramo, A. Roth, P. J. Arrazola, Microstructure based flow stress model to predict machinability in ferrite-pearlite steels, CIRP Annals. 68/1 (2019) 49-52. https://doi.org/10.1016/j.cirp.2019.03.013
[18] N. R. Modh, G. D. Mistry, K. B. Rathod, An experimental investigation to optimize the process parameters of AISI 52100 steel in hot machining, International Journal of Engineering Research and Applications. 1/3 (2011) 483-489.

[19] A. K. Kumar, P. Venkataramaiah, Optimization of Process parameters in hot machining of Inconel 718 alloy using FEM, International Journal of Applied Engineering Research. 13/5 (2018) 2158-2162.

[20] S. K. Thandra, S. K. Choudhury, Effect of cutting parameters on cutting force, surface finish and tool wear in hot machining, International Journal of Machining and Machinability of Materials. 7/3-4 (2010) 260-283. https://doi.org/10.1504/IJMMM.2010.03307

[21] A. K. Parida, K. Maity, Hot machining of Ti – 6Al – 4V : FE analysis and experimental validation, Sādhanā. 44 (2019) 1-6. https://doi.org/10.1007/s12046-019-1127-8

[22] W. Xu, X. Liu, J. Sun, L. Zhang, Finite element simulation and experimental research on electric hot machining, International Journal of Advanced Manufacturing Technology. 66 (2013) 407-415. https://doi.org/10.1007/s00170-012-4335-5

[23] A. K. Parida, K. Maity, Effect of nose radius on forces, and process parameters in hot machining of Inconel 718 using finite element analysis, Engineering Science and Technology, an International Journal. 20/2 (2017) 687-693. https://doi.org/10.1016/j.jestch.2016.10.006

[24] N. N. Zorev, Inter-relationship between shear processes occurring along tool face and shear plane in metal cutting, International research in production engineering. 49 (1963) 142-152.

[25] S. Kato, K. Yamaguchi, M. Yamada, Stress distribution at the interface between tool and chip in machining, Journal of Manufacturing Science and Engineering, Transactions of the ASME. 94/2 (1972) 693-689. https://doi.org/10.1115/1.3428229

[26] K. Maekawa, T. Kitagawa, T. H. C. Childs, Friction characteristics at tool-chip interface in steel machining, Tribology Series. 32 (1997) 559-567. https://doi.org/10.1016/S0167-8922(08)70482-6

[27] G. Ortiz de Zarate, A. Madariaga, P. J. Arrazola, T. H. C. Childs, A novel methodology to characterize tool-chip contact in metal cutting using partially restricted contact length tools, CIRP Annals. 70/1 (2021) 61-64. https://doi.org/10.1016/j.cirp.2021.03.002

[28] M. Saez-de-Buruaga, P. Aristimuño, D. Soler, E. D'Eramo, A. Roth, P. J. Arrazola, Microstructure based flow stress model to predict machinability in ferrite-pearlite steels, CIRP Annals. 68/1 (2019) 49-52. https://doi.org/10.1016/j.cirp.2019.03.013
# Digital image processing algorithm for industrial on-site roughness evaluation in Ti-alloy machining

RIBEIRO CARVALHO Sílvia Daniela<fnref n="1" />,<fnref n="2" />,a*, ARAUJO Anna Carla<fnref n="2" />,b, HOROVISTIZ Ana<fnref n="1" />,c and DAVIM João Paulo<fnref n="1" />,d

**Keywords:** Machining, Surface quality, Texture, Digital Image Processing, Ti6Al4V

**Abstract.** The surface texture is normally observed after the machining process, but nowadays it is important to use on-site analysis to improve the process automatically via smart processing. This study introduces a contactless roughness inspection method employing digital image processing on Ti6Al4V samples in turning using three different feed. Texture analysis with grey-level co-occurrence matrix (GLCM) extracted features that were correlated with the arithmetic average roughness (Ra), leading to the establishment of predictive models. The study encompassed diverse image testing, incorporating variations in resolution and brightness distributions. It was found that the pixel pair spacing (PPS) in GLCM analysis was influenced by the image resolution and feed rate. The predictive models developed with high-quality images, i.e., higher resolution and better brightness distribution, yielded similar results to those created using lower-quality images.

**Introduction**

Roughness assessment plays a crucial role in evaluating machined surface quality across industries. Traditional methods include mechanical or optical profilometers that estimate well-known amplitude parameters such as the arithmetic average roughness (Ra). While effective, these offline inspection methods prove disadvantageous for on-site analysis. In contrast, employing a computer vision system to capture and process images in real-time enables the integration of decision-making frameworks for Industry 4.0 [1].

In this context, the grey level co-occurrence matrix (GLCM) allows the extraction of texture descriptors from machined surface images, enabling statistical or intelligent modelling for predicting the tool wear [2,3], machining process (e.g., turned, milled, blasted, sanded) [4,5] and surface roughness [6,7]. These descriptors are statistical measures that give insights into the patterns of the machined surfaces. Common descriptors include contrast, energy, homogeneity, and correlation. They are computed from the GLCM, which captures how often different combinations of pixel intensity values occur within the image at a defined spatial relationship (spacing and angle). Indeed, many studies emphasize the strong correlation between the GLCM features and the cutting parameters [8], the surface roughness [9,10] and the generated vibration [11]. However, a substantial gap exists in proposing explicit modelling strategies, for predicting the machining outcomes.

Besides, a challenge in GLCM analysis lies in selecting the pixel pair spacing (PPS) and orientation (PPD), given their large impact on the occurrences within the matrix. This influences the ability to identify and relate textural patterns of the images to machining mechanisms, including roughness. Indeed, many authors [12–14] selected the PPS and PPD by visualizing the GLCM for several spacings and orientations, where optimal values had more occurrences in the principal diagonal of the matrix. Others, such as Dutta *et al.* [2] used power spectral density plots

<figure><img src="image_1883.png" /></figure>

<footnote n="1">¹University of Aveiro, Department of Mechanical Engineering, Centre for Mechanical Engineering and Automation (TEMA), Campus Santiago, 3810-193 Aveiro, Portugal</footnote>

<footnote n="2">²Institute Clément Ader (ICA), CNRS UMR 5312, Toulouse, France</footnote>

<footnote>asdrc@ua.pt, baraujo@insa-toulouse.fr, chorovistiz@ua.pt, dpdavim@ua.pt</footnote>
# Digital image processing algorithm for industrial on-site roughness evaluation in Ti-alloy machining

RIBEIRO CARVALHO Sílvia Daniela<fnref n="1" />,<fnref n="2" />,a*, ARAUJO Anna Carla<fnref n="2" />,b, HOROVISTIZ Ana<fnref n="1" />,c and DAVIM João Paulo<fnref n="1" />,d

**Keywords:** Machining, Surface quality, Texture, Digital Image Processing, Ti6Al4V

**Abstract.** The surface texture is normally observed after the machining process, but nowadays it is important to use on-site analysis to improve the process automatically via smart processing. This study introduces a contactless roughness inspection method employing digital image processing on Ti6Al4V samples in turning using three different feed. Texture analysis with grey-level co-occurrence matrix (GLCM) extracted features that were correlated with the arithmetic average roughness (Ra), leading to the establishment of predictive models. The study encompassed diverse image testing, incorporating variations in resolution and brightness distributions. It was found that the pixel pair spacing (PPS) in GLCM analysis was influenced by the image resolution and feed rate. The predictive models developed with high-quality images, i.e., higher resolution and better brightness distribution, yielded similar results to those created using lower-quality images.

**Introduction**

Roughness assessment plays a crucial role in evaluating machined surface quality across industries. Traditional methods include mechanical or optical profilometers that estimate well-known amplitude parameters such as the arithmetic average roughness (Ra). While effective, these offline inspection methods prove disadvantageous for on-site analysis. In contrast, employing a computer vision system to capture and process images in real-time enables the integration of decision-making frameworks for Industry 4.0 [1].

In this context, the grey level co-occurrence matrix (GLCM) allows the extraction of texture descriptors from machined surface images, enabling statistical or intelligent modelling for predicting the tool wear [2,3], machining process (e.g., turned, milled, blasted, sanded) [4,5] and surface roughness [6,7]. These descriptors are statistical measures that give insights into the patterns of the machined surfaces. Common descriptors include contrast, energy, homogeneity, and correlation. They are computed from the GLCM, which captures how often different combinations of pixel intensity values occur within the image at a defined spatial relationship (spacing and angle). Indeed, many studies emphasize the strong correlation between the GLCM features and the cutting parameters [8], the surface roughness [9,10] and the generated vibration [11]. However, a substantial gap exists in proposing explicit modelling strategies, for predicting the machining outcomes.

Besides, a challenge in GLCM analysis lies in selecting the pixel pair spacing (PPS) and orientation (PPD), given their large impact on the occurrences within the matrix. This influences the ability to identify and relate textural patterns of the images to machining mechanisms, including roughness. Indeed, many authors [12–14] selected the PPS and PPD by visualizing the GLCM for several spacings and orientations, where optimal values had more occurrences in the principal diagonal of the matrix. Others, such as Dutta *et al.* [2] used power spectral density plots

<figure><img src="image_1878.png" /></figure>

<footnote n="1">¹University of Aveiro, Department of Mechanical Engineering, Centre for Mechanical Engineering and Automation (TEMA), Campus Santiago, 3810-193 Aveiro, Portugal</footnote>

<footnote n="2">²Institute Clément Ader (ICA), CNRS UMR 5312, Toulouse, France</footnote>

<footnote>asdrc@ua.pt, baraujo@insa-toulouse.fr, chorovistiz@ua.pt, dpdavim@ua.pt</footnote>
of the GLCM features (contrast and homogeneity) to find the optimal spacing, i.e. maximum frequency peak. Moreover, they found that the GLCM features (contrast and homogeneity) exhibited periodicity concerning spacing. Consequently, the methods traditionally employed to determine the PPS in turned surfaces rely on the calculation of the GLCM for multiple spacings, significantly increasing computational time, which is an obstacle to real-time monitoring.

In his study, a contactless roughness inspection method is proposed, based on texture analysis with GLCM for Ti6Al4V with a novel strategy for selecting PPS, based on the feed and image resolution, which are promptly accessible.

## Materials and Methods

The experimental methodology for applying the surface analysis to the turning specimen is described in Fig 1 : (1) machining Ti6Al4V bar; (2) image acquisition ; (3-5) Image processing; (6) feature extraction with GLCM analysis; and (7) establishment of the roughness-contrast model.

The machining was developed in Huron DX 200 turning centre in flood cooling with a CNMG 120408 MF1 TS2000 insert. The parameters used were: constant cutting speed (Vc=60 m/min), axial depth-of-cut (adoc) of 1 mm and three levels of feed (f): 0.15, 0.2 and 0.25 mm/rev to create different profile patterns. The cutting length is fixed for each test: 35 mm.

<figure><img src="image_1884.png" /><figcaption>Fig. 1. Experimental methodology, including, turning tests (1), image acquisition (2) and processing (3-5), feature extraction with GLCM analysis (6) and modelling (7).</figcaption></figure>
of the GLCM features (contrast and homogeneity) to find the optimal spacing, i.e. maximum frequency peak. Moreover, they found that the GLCM features (contrast and homogeneity) exhibited periodicity concerning spacing. Consequently, the methods traditionally employed to determine the PPS in turned surfaces rely on the calculation of the GLCM for multiple spacings, significantly increasing computational time, which is an obstacle to real-time monitoring.

In his study, a contactless roughness inspection method is proposed, based on texture analysis with GLCM for Ti6Al4V with a novel strategy for selecting PPS, based on the feed and image resolution, which are promptly accessible.

## Materials and Methods

The experimental methodology for applying the surface analysis to the turning specimen is described in Fig 1 : (1) machining Ti6Al4V bar; (2) image acquisition ; (3-5) Image processing; (6) feature extraction with GLCM analysis; and (7) establishment of the roughness-contrast model.

The machining was developed in Huron DX 200 turning centre in flood cooling with a CNMG 120408 MF1 TS2000 insert. The parameters used were: constant cutting speed (Vc=60 m/min), axial depth-of-cut (adoc) of 1 mm and three levels of feed (f): 0.15, 0.2 and 0.25 mm/rev to create different profile patterns. The cutting length is fixed for each test: 35 mm.

<figure><img src="image_1879.png" /><figcaption>Fig. 1. Experimental methodology, including, turning tests (1), image acquisition (2) and processing (3-5), feature extraction with GLCM analysis (6) and modelling (7).</figcaption></figure>
The image acquisition was done through two approaches: an off-site procedure using a 3D measurement to guarantee the profile precision and a potential on-site procedure using a portable optical microscope (OM) to forecast an industrial application.

An Alicona Infinite Focus measurement (IFM) is used for the 3D profile that captured 2040 X 2040 RGB images with a spatial resolution of 1000 pixels/mm. Also, it assessed the arithmetic average roughness ($R_a$) with a cut-off length of 0.8 mm and a tracing length of 4 mm. For each feed, twelve images and roughness values were measured at the start, middle and end of the cutting length. Within each zone, four measurements with a 90-degree spacing were taken.

A histogram matching technique, implemented in MATLAB R2019a, was used to uniformize the brightness distribution for each dataset (Fig. 1e-f). Then, the images were converted to grayscale (Fig. 1g-h). Afterwards, a contrast-limited adaptive histogram equalization (CLAHE) was employed (Fig. 1i-j), as advised by several authors [14,15]. This step was particularly important for the OM images, to improve the brightness distribution since there were regions with varying illumination conditions.

After the pre-processing steps, the grey-level co-occurrence matrix was calculated with the MATLAB function *graycomatrix*. Three inputs were considered for calculating the GLCM. The first (Img) was the pre-processed grey-level image. The second was the number of grey levels ('Numlevels') for discretizing the pre-processed image, that is for spreading the range of pixels (0-255) into 64 levels, to decrease the computation time, as suggested by Datta *et al.* [13]. The third ('offset') specifies the relative position, in terms of spacing and angle of the pixel pairs. For this work, as shown in Fig. 1k, the direction perpendicular to the feed marks was considered, as recommended in [3,14]. The spacing (PPS) was determined with the feed and image resolution, as presented in the results and discussion section. Fig. 1. 1-m illustrates how the GLCM matrix is calculated when a grey-level image (Img) is discretized in 8 levels, for a horizontal direction and a spacing of one pixel. GLCM (Fig. 1m) shows how often each pixel combination appears. For instance, a pixel with intensity 5, with a neighbour 7 in the horizontal direction appears 2 times in the scaled image (Fig. 1l). Using the equations from Table 1, the GLCM features were extracted. Finally, the correlation between the GLCM features and $R_a$ was estimated, and the models were created.
The image acquisition was done through two approaches: an off-site procedure using a 3D measurement to guarantee the profile precision and a potential on-site procedure using a portable optical microscope (OM) to forecast an industrial application.

An Alicona Infinite Focus measurement (IFM) is used for the 3D profile that captured 2040 X 2040 RGB images with a spatial resolution of 1000 pixels/mm. Also, it assessed the arithmetic average roughness ($R_a$) with a cut-off length of 0.8 mm and a tracing length of 4 mm. For each feed, twelve images and roughness values were measured at the start, middle and end of the cutting length. Within each zone, four measurements with a 90-degree spacing were taken.

A histogram matching technique, implemented in MATLAB R2019a, was used to uniformize the brightness distribution for each dataset (Fig. 1e-f). Then, the images were converted to grayscale (Fig. 1g-h). Afterwards, a contrast-limited adaptive histogram equalization (CLAHE) was employed (Fig. 1i-j), as advised by several authors [14,15]. This step was particularly important for the OM images, to improve the brightness distribution since there were regions with varying illumination conditions.

After the pre-processing steps, the grey-level co-occurrence matrix was calculated with the MATLAB function *graycomatrix*. Three inputs were considered for calculating the GLCM. The first (Img) was the pre-processed grey-level image. The second was the number of grey levels ('Numlevels') for discretizing the pre-processed image, that is for spreading the range of pixels (0-255) into 64 levels, to decrease the computation time, as suggested by Datta *et al.* [13]. The third ('offset') specifies the relative position, in terms of spacing and angle of the pixel pairs. For this work, as shown in Fig. 1k, the direction perpendicular to the feed marks was considered, as recommended in [3,14]. The spacing (PPS) was determined with the feed and image resolution, as presented in the results and discussion section. Fig. 1. 1-m illustrates how the GLCM matrix is calculated when a grey-level image (Img) is discretized in 8 levels, for a horizontal direction and a spacing of one pixel. GLCM (Fig. 1m) shows how often each pixel combination appears. For instance, a pixel with intensity 5, with a neighbour 7 in the horizontal direction appears 2 times in the scaled image (Fig. 1l). Using the equations from Table 1, the GLCM features were extracted. Finally, the correlation between the GLCM features and $R_a$ was estimated, and the models were created.
<table><caption>Table 1. Name, description, and equations of the texture features extracted from GLCM.</caption><thead><tr><th></th><th>Name & description</th><th>Equation</th></tr></thead><tbody><tr><td rowspan="3">Features of Contrast</td><td>Contrast (CON): measures local variations in intensity levels between neighbouring pixels.</td><td>Σ<sub>i,j=1</sub><sup>N</sup> P<sub>i,j</sub> (i - j)<sup>2</sup></td></tr><tr><td>Homogeneity (HOM): shows how closeness between the elements of the GLCM and its diagonal. It reflects the homogeneity of the texture.</td><td>Σ<sub>i,j=1</sub><sup>N</sup> (P<sub>i,j</sub> / (1 + (i - j)<sup>2</sup>))</td></tr><tr><td>Dissimilarity (DIS): is the average difference in intensity of pairs of pixels. Highlights non-uniformities.</td><td>Σ<sub>i,j=1</sub><sup>N</sup> P<sub>i,j</sub> |i - j|</td></tr><tr><td rowspan="3">Features of Orderliness</td><td>Angular second moment (ASM)</td><td>Σ<sub>i,j=1</sub><sup>N</sup> P<sub>i,j</sub><sup>2</sup></td></tr><tr><td>Energy</td><td>√ASM</td></tr><tr><td>Maximum probability (MaxGLCM): the maximum probability of occurrence of a specific pixel pair in the GLCM.</td><td></td></tr><tr><td></td><td>Entropy: measures randomness or uncertainty.</td><td>Σ<sub>i,j=1</sub><sup>N</sup> -P<sub>i,j</sub> · ln(P<sub>i,j</sub>)</td></tr><tr><td>Statistics</td><td>GLCM correlation (GLCMCor): shows how the pixel intensities are related spatially.</td><td>Σ<sub>i,j=1</sub><sup>N</sup> P<sub>i,j</sub> <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding: 0 5px;">(i - u<sub>i</sub>)(j - u<sub>j</sub>)</div><div style="padding: 0 5px;">√σ<sub>i</sub><sup>2</sup>σ<sub>j</sub><sup>2</sup></div></div></td></tr></tbody><tfoot><tr><td colspan="3">P<sub>i,j</sub>: is the normalized GLCM; i, j: element row and column; N: number of GLCM elements; μ<sub>i</sub> and μ<sub>j</sub>: mean of every row and column; σ<sub>i</sub> and σ<sub>j</sub>: standard deviation of every row and column.</td></tr></tfoot></table>

## Results and Discussion

In this section, it is presented the results of the energy features, the values of correlation coefficients and the models achieved by the methodology proposed.

Fig. 2 presents the results of the energy feature exhibiting periodic variation across the PPS for all the feeds in both datasets (IFM and OM). Notably, an increase in the feed corresponded to an observed increase in the period, i.e., 150, 200 and 250 pixels for the IFM images (Fig. 2a-c), and 23, 30, 38 pixels for the OM images (Fig. 2d-f). This trend was observed by Dutta *et al.* [2], who identified an optimal PPS of 10 and 16 pixels for f of 0.2 and 0.28 mm/rev, respectively.
<table><caption>Table 1. Name, description, and equations of the texture features extracted from GLCM.</caption><thead><tr><th></th><th>Name & description</th><th>Equation</th></tr></thead><tbody><tr><td rowspan="3">Features of Contrast</td><td>Contrast (CON): measures local variations in intensity levels between neighbouring pixels.</td><td>Σ<sub>i,j=1</sub><sup>N</sup> P<sub>i,j</sub> (i - j)<sup>2</sup></td></tr><tr><td>Homogeneity (HOM): shows how closeness between the elements of the GLCM and its diagonal. It reflects the homogeneity of the texture.</td><td>Σ<sub>i,j=1</sub><sup>N</sup> (P<sub>i,j</sub> / (1 + (i - j)<sup>2</sup>))</td></tr><tr><td>Dissimilarity (DIS): is the average difference in intensity of pairs of pixels. Highlights non-uniformities.</td><td>Σ<sub>i,j=1</sub><sup>N</sup> P<sub>i,j</sub> |i - j|</td></tr><tr><td rowspan="3">Features of Orderliness</td><td>Angular second moment (ASM)</td><td>Σ<sub>i,j=1</sub><sup>N</sup> P<sub>i,j</sub><sup>2</sup></td></tr><tr><td>Energy</td><td>√ASM</td></tr><tr><td>Maximum probability (MaxGLCM): the maximum probability of occurrence of a specific pixel pair in the GLCM.</td><td></td></tr><tr><td></td><td>Entropy: measures randomness or uncertainty.</td><td>Σ<sub>i,j=1</sub><sup>N</sup> -P<sub>i,j</sub> · ln(P<sub>i,j</sub>)</td></tr><tr><td>Statistics</td><td>GLCM correlation (GLCMCor): shows how the pixel intensities are related spatially.</td><td>Σ<sub>i,j=1</sub><sup>N</sup> P<sub>i,j</sub> <div style="display: inline-block; vertical-align: middle; text-align: center;"><div style="border-bottom: 1px solid black; padding: 0 5px;">(i - u<sub>i</sub>)(j - u<sub>j</sub>)</div><div style="padding: 0 5px;">√σ<sub>i</sub><sup>2</sup>σ<sub>j</sub><sup>2</sup></div></div></td></tr></tbody><tfoot><tr><td colspan="3">P<sub>i,j</sub>: is the normalized GLCM; i, j: element row and column; N: number of GLCM elements; μ<sub>i</sub> and μ<sub>j</sub>: mean of every row and column; σ<sub>i</sub> and σ<sub>j</sub>: standard deviation of every row and column.</td></tr></tfoot></table>

## Results and Discussion

In this section, it is presented the results of the energy features, the values of correlation coefficients and the models achieved by the methodology proposed.

Fig. 2 presents the results of the energy feature exhibiting periodic variation across the PPS for all the feeds in both datasets (IFM and OM). Notably, an increase in the feed corresponded to an observed increase in the period, i.e., 150, 200 and 250 pixels for the IFM images (Fig. 2a-c), and 23, 30 38 pixels for the OM images (Fig. 2d-f). This trend was observed by Dutta et al. [2], who identified an optimal PPS of 10 and 16 pixels for f of 0.2 and 0.28 mm/rev, respectively.
<figure><img src="image_1885.png" /><figcaption>Fig. 2. Periodic variation of the GLCM feature (energy – PPS) for both datasets, i.e. IFM and OM</figcaption></figure>

Furthermore, the results revealed that the PPS changed with the image resolution, which was 1000 and 150 pixels/mm for the IFM and OM images, respectively. This finding corroborates a statement from Gadelmawla [8], suggesting that the PPS depends on the resolution of the texture.

Moreover, it was found that the period observed in the plots of the energy along the spacing (black arrows in Fig. 2) could be estimated by multiplying the image resolution by the feed, as shown in Table 2. This promoted a reduction of the computational time when running the algorithm, as in previous articles [2,12–14], the GLCM is calculated for several spacings to find the optimal value (maximum peak). The optimal PPS values from Table 2 were also found for all the other GLCM features listed in Table 1.

<table><caption>Table 2. PPS estimation based on feed and image resolution.</caption><thead><tr><th rowspan="2">f [mm/rev]</th><th colspan="2">IFM</th><th colspan="2">OM</th></tr><tr><th>Resolution [pixel/mm]</th><th>PPS</th><th>Resolution [pixel/mm]</th><th>PPS</th></tr></thead><tbody><tr><td>0.15</td><td rowspan="3">1000</td><td>150</td><td rowspan="3">150</td><td>23</td></tr><tr><td>0.20</td><td>200</td><td>30</td></tr><tr><td>0.25</td><td>250</td><td>38</td></tr></tbody></table>

Table 3 displays the Spearman correlation coefficient between $R_a$ and the GLCM features for both datasets across various feed levels. While this coefficient does not provide information about the rate or magnitude of change, it provides insight into the direction and strength of the relationship between these variables.
<figure><img src="image_1880.png" /><figcaption>Fig. 2. Periodic variation of the GLCM feature (energy – PPS) for both datasets, i.e. IFM and OM</figcaption></figure>

Furthermore, the results revealed that the PPS changed with the image resolution, which was 1000 and 150 pixels/mm for the IFM and OM images, respectively. This finding corroborates a statement from Gadelmawla [8], suggesting that the PPS depends on the resolution of the texture.

Moreover, it was found that the period observed in the plots of the energy along the spacing (black arrows in Fig. 2) could be estimated by multiplying the image resolution by the feed, as shown in Table 2. This promoted a reduction of the computational time when running the algorithm, as in previous articles [2,12–14], the GLCM is calculated for several spacings to find the optimal value (maximum peak). The optimal PPS values from Table 2 were also found for all the other GLCM features listed in Table 1.

<table><caption>Table 2. PPS estimation based on feed and image resolution.</caption><thead><tr><th rowspan="2">f [mm/rev]</th><th colspan="2">IFM</th><th colspan="2">OM</th></tr><tr><th>Resolution [pixel/mm]</th><th>PPS</th><th>Resolution [pixel/mm]</th><th>PPS</th></tr></thead><tbody><tr><td>0.15</td><td rowspan="3">1000</td><td>150</td><td rowspan="3">150</td><td>23</td></tr><tr><td>0.20</td><td>200</td><td>30</td></tr><tr><td>0.25</td><td>250</td><td>38</td></tr></tbody></table>

Table 3 displays the Spearman correlation coefficient between $R_a$ and the GLCM features for both datasets across various feed levels. While this coefficient does not provide information about the rate or magnitude of change, it provides insight into the direction and strength of the relationship between these variables.
<table><caption>Table 3. Correlation between R<sub>a</sub> and the GLCM features for both datasets and feed levels.</caption><thead><tr><th rowspan="2"></th><th>Group (feed)</th><th colspan="3">Contrast</th><th colspan="4">Orderliness</th><th>Statistics</th></tr><tr><th>f (mm/rev)</th><th>CON</th><th>DIS</th><th>HOM</th><th>ASM</th><th>Energy</th><th>MaxGLCM</th><th>Entropy</th><th>GLCMCor</th></tr></thead><tbody><tr><td rowspan="3">IFM</td><td>0.15</td><td>0.88</td><td>0.87</td><td>-0.87</td><td>-0.87</td><td>-0.87</td><td>-0.86</td><td>0.88</td><td>-0.88</td></tr><tr><td>0.20</td><td>0.83</td><td>0.87</td><td>-0.80</td><td>-0.86</td><td>-0.86</td><td>-0.55</td><td>0.89</td><td>-0.79</td></tr><tr><td>0.25</td><td>0.61</td><td>0.59</td><td>-0.61</td><td>-0.61</td><td>-0.61</td><td>-0.67</td><td>0.61</td><td>-0.61</td></tr><tr><td rowspan="3">OM</td><td>0.15</td><td>0.72</td><td>0.70</td><td>-0.70</td><td>-0.79</td><td>-0.79</td><td>-0.62</td><td>0.81</td><td>-0.73</td></tr><tr><td>0.20</td><td>0.63</td><td>0.63</td><td>-0.66</td><td>-0.73</td><td>-0.73</td><td>-0.74</td><td>0.64</td><td>-0.63</td></tr><tr><td>0.25</td><td>0.63</td><td>0.61</td><td>-0.64</td><td>-0.52</td><td>-0.52</td><td>-0.41</td><td>0.62</td><td>-0.64</td></tr></tbody></table>

A positive correlation was observed for contrast (CON), dissimilarity (DIS), and entropy, indicating that an increase in these features corresponds to an increase in R<sub>a</sub>, considering a linear model. In rougher surfaces, higher differences in pixel intensity between the grooves and the ridges are observed, which leads to an increase in the contrast and dissimilarity. On the other hand, rougher surfaces tend to be more heterogeneous, resulting in higher entropy, due to variations in the distribution of pixel intensity.

A negative correlation was found for the other GLCM features, such as homogeneity (HOM), angular second moment (ASM), energy and maximum probability (MaxGLCM). An increase in these features corresponded to a decrease in R<sub>a</sub>. Smoother surfaces tend to be more homogeneous in texture, resulting in less deviation between the diagonal and the elements of the GLCM, thereby increasing the overall homogeneity. Moreover, smoother surfaces exhibit a more ordered distribution of pixel intensities, leading to higher energy (and ASM). Additionally, smoother surfaces promote the dominance of particular pixel pairs, consequently increasing the maximum probability (MaxGLCM).

Compared to the dataset created with the optical microscope, the correlation coefficients tended to be higher for the IFM dataset. This difference could be attributed to the higher resolution and better brightness distribution of the IFM images. These properties likely enabled a better differentiation of the textural elements within the images due to more detail and a wider range of pixel intensities.

Although all the GLCM features present a higher correlation with R<sub>a</sub> (Table 3), the regression models using contrast (CON) and dissimilarity (DIS) exhibited higher R-squared, as shown in Fig. 3. Therefore, they were more effective in predicting R<sub>a</sub> than other GLCM features. Previously, Datta et. al [13] had identified contrast as one of the better parameters to study texture in turned surfaces as it effectively highlights the difference between the lighter ridges and darker grooves. These findings show that a strong correlation between the GLCM feature, and R<sub>a</sub> does not always translate into the development of satisfactory models.

The disparities between the actual and predicted R<sub>a</sub> values for CON and DIS are illustrated in Fig. 3. Overall, the residuals exhibit a heterogeneous distribution around zero, although certain points display greater deviations. Currently, the mean value of five profiles was used to assess the image roughness. However, incorporating more profiles, more spaced along the field, could provide a deeper understanding of roughness variability and possibly increase the prediction accuracy, as the GLCM analysis considers the full field of the image

Besides, in Fig. 3 it is possible to understand that three different models were created, one for each feed. This may pose limitations in terms of flexibility for digital manufacturing. To address this, Dutta et. al [2] have normalized both the dependent (R<sub>a</sub>) and independent variables (contrast and homogeneity), aggregating data from multiple feeds into a single model. However, this method considers different normalization factors for each dataset, which need to be considered when making predictions. Therefore, after all, it is required to understand in which feed the images were
<table><caption>Table 3. Correlation between R<sub>a</sub> and the GLCM features for both datasets and feed levels.</caption><thead><tr><th rowspan="2"></th><th>Group (feed)</th><th colspan="3">Contrast</th><th colspan="4">Orderliness</th><th>Statistics</th></tr><tr><th>f (mm/rev)</th><th>CON</th><th>DIS</th><th>HOM</th><th>ASM</th><th>Energy</th><th>MaxGLCM</th><th>Entropy</th><th>GLCMCor</th></tr></thead><tbody><tr><td rowspan="3">IFM</td><td>0.15</td><td>0.88</td><td>0.87</td><td>-0.87</td><td>-0.87</td><td>-0.87</td><td>-0.86</td><td>0.88</td><td>-0.88</td></tr><tr><td>0.20</td><td>0.83</td><td>0.87</td><td>-0.80</td><td>-0.86</td><td>-0.86</td><td>-0.55</td><td>0.89</td><td>-0.79</td></tr><tr><td>0.25</td><td>0.61</td><td>0.59</td><td>-0.61</td><td>-0.61</td><td>-0.61</td><td>-0.67</td><td>0.61</td><td>-0.61</td></tr><tr><td rowspan="3">OM</td><td>0.15</td><td>0.72</td><td>0.70</td><td>-0.70</td><td>-0.79</td><td>-0.79</td><td>-0.62</td><td>0.81</td><td>-0.73</td></tr><tr><td>0.20</td><td>0.63</td><td>0.63</td><td>-0.66</td><td>-0.73</td><td>-0.73</td><td>-0.74</td><td>0.64</td><td>-0.63</td></tr><tr><td>0.25</td><td>0.63</td><td>0.61</td><td>-0.64</td><td>-0.52</td><td>-0.52</td><td>-0.41</td><td>0.62</td><td>-0.64</td></tr></tbody></table>

A positive correlation was observed for contrast (CON), dissimilarity (DIS), and entropy, indicating that an increase in these features corresponds to an increase in R<sub>a</sub>, considering a linear model. In rougher surfaces, higher differences in pixel intensity between the grooves and the ridges are observed, which leads to an increase in the contrast and dissimilarity. On the other hand, rougher surfaces tend to be more heterogeneous, resulting in higher entropy, due to variations in the distribution of pixel intensity.

A negative correlation was found for the other GLCM features, such as homogeneity (HOM), angular second moment (ASM), energy and maximum probability (MaxGLCM). An increase in these features corresponded to a decrease in R<sub>a</sub>. Smoother surfaces tend to be more homogeneous in texture, resulting in less deviation between the diagonal and the elements of the GLCM, thereby increasing the overall homogeneity. Moreover, smoother surfaces exhibit a more ordered distribution of pixel intensities, leading to higher energy (and ASM). Additionally, smoother surfaces promote the dominance of particular pixel pairs, consequently increasing the maximum probability (MaxGLCM).

Compared to the dataset created with the optical microscope, the correlation coefficients tended to be higher for the IFM dataset. This difference could be attributed to the higher resolution and better brightness distribution of the IFM images. These properties likely enabled a better differentiation of the textural elements within the images due to more detail and a wider range of pixel intensities.

Although all the GLCM features present a higher correlation with R<sub>a</sub> (Table 3), the regression models using contrast (CON) and dissimilarity (DIS) exhibited higher R-squared, as shown in Fig. 3. Therefore, they were more effective in predicting R<sub>a</sub> than other GLCM features. Previously, Datta et. al [13] had identified contrast as one of the better parameters to study texture in turned surfaces as it effectively highlights the difference between the lighter ridges and darker grooves. These findings show that a strong correlation between the GLCM feature, and R<sub>a</sub> does not always translate into the development of satisfactory models.

The disparities between the actual and predicted R<sub>a</sub> values for CON and DIS are illustrated in Fig. 3. Overall, the residuals exhibit a heterogeneous distribution around zero, although certain points display greater deviations. Currently, the mean value of five profiles was used to assess the image roughness. However, incorporating more profiles, more spaced along the field, could provide a deeper understanding of roughness variability and possibly increase the prediction accuracy, as the GLCM analysis considers the full field of the image.

Besides, in Fig. 3 it is possible to understand that three different models were created, one for each feed. This may pose limitations in terms of flexibility for digital manufacturing. To address this, Dutta et. al [2] have normalized both the dependent (R<sub>a</sub>) and independent variables (contrast and homogeneity), aggregating data from multiple feeds into a single model. However, this method considers different normalization factors for each dataset, which need to be considered when making predictions. Therefore, after all, it is required to understand in which feed the images were
attained. In this study, the relationship between the PPS, the feed, and the image resolution was established. As a result, if the feed rate is unknown, it can be estimated using the PPS (estimated from the peaks from Fig. 2) and the image resolution.

<figure><img src="image_1886.png" /><figcaption>Fig. 3. Relationship between GLCM features (contrast and dissimilarity) and $R_a$ with regression models for both image datasets and feed. Disparities between the actual and predicted $R_a$.</figcaption></figure>

## Conclusions

A contactless method for the roughness inspection of machined Ti6Al4V surfaces has been proposed, based on texture analysis with GLCM analysis. The algorithm was applied to images submitted to turning with images acquired by two methods (off-site and in-site). It was found that:

* The determination of the appropriate pixel pair spacing (PPS) in the GLCM analysis of turned surfaces depends on the machining conditions (feed) and the image resolution. This allowed the appropriate spacing to be quickly determined without the need to study the GLCM feature behaviour at multiple spacings, which is time-consuming.

* A high correlation between the GLCM feature and $R_a$ does not always translate into the development of satisfactory models. Therefore, correlation does not imply causation and does not guarantee the fit of a particular predictive model.

* Predictive models $R_a$ as a function of contrast and dissimilarity.

* The predictive models developed using high-quality images from the IFM, which had higher resolution and better brightness distribution, gave similar results to those developed using lower-quality images from the portable optical microscope.

* In future research, it is intended to apply intelligent modelling for establishing predictive models. To attain that, the database will be increased, and the feature extractor (GLCM) will be parameterized according to the guidelines established in this work.

## Acknowledgements

The authors acknowledge the Portuguese Foundation for Science and Technology (FCT) for the PhD grant SFRH/BD/07040/2021 and TiFast S.R.L., from Italy, for providing the Ti-alloy.
attained. In this study, the relationship between the PPS, the feed, and the image resolution was established. As a result, if the feed rate is unknown, it can be estimated using the PPS (estimated from the peaks from Fig. 2) and the image resolution.

<figure><img src="image_1881.png" /><figcaption>Fig. 3. Relationship between GLCM features (contrast and dissimilarity) and $R_a$ with regression models for both image datasets and feed. Disparities between the actual and predicted $R_a$.</figcaption></figure>

## Conclusions

A contactless method for the roughness inspection of machined Ti6Al4V surfaces has been proposed, based on texture analysis with GLCM analysis. The algorithm was applied to images submitted to turning with images acquired by two methods (off-site and in-site). It was found that:

* The determination of the appropriate pixel pair spacing (PPS) in the GLCM analysis of turned surfaces depends on the machining conditions (feed) and the image resolution. This allowed the appropriate spacing to be quickly determined without the need to study the GLCM feature behaviour at multiple spacings, which is time-consuming.

* A high correlation between the GLCM feature and $R_a$ does not always translate into the development of satisfactory models. Therefore, correlation does not imply causation and does not guarantee the fit of a particular predictive model.

* Predictive models $R_a$ as a function of contrast and dissimilarity.

* The predictive models developed using high-quality images from the IFM, which had higher resolution and better brightness distribution, gave similar results to those developed using lower-quality images from the portable optical microscope.

* In future research, it is intended to apply intelligent modelling for establishing predictive models. To attain that, the database will be increased, and the feature extractor (GLCM) will be parameterized according to the guidelines established in this work.

## Acknowledgements

The authors acknowledge the Portuguese Foundation for Science and Technology (FCT) for the PhD grant SFRH/BD/07040/2021 and TiFast S.R.L., from Italy, for providing the Ti-alloy.
## References

[1] S. Jain, M. Pradhan, A. Kumar, Application of Digital Image Processing on Machined Surfaces: A review, in: R. Kumar Nayak, M. Pradhan, J.P. Davim (Eds.), Recent Adv. Mater. Manuf. Technol., Springer, 2023. https://doi.org/10.1007/978-981-99-2921-4

[2] S. Dutta, A. Datta, N. Das Chakladar, S.K. Pal, S. Mukhopadhyay, R. Sen, Detection of tool condition from the turned surface images using an accurate grey level co-occurrence technique, Precis. Eng. 36 (2012) 458-466. https://doi.org/10.1016/j.precisioneng.2012.02.004

[3] S. Dutta, S.K. Pal, R. Sen, On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression, Precis. Eng. 43 (2016) 34-42. https://doi.org/10.1016/J.PRECISIONENG.2015.06.007

[4] D.R. Patel, V. Vakharia, M.B. Kiran, Texture classification of machined surfaces using image processing and machine learning techniques, FME Trans. 47 (2019) 865-872. https://doi.org/10.5937/fmet1904865P

[5] G. Prasad, V.S. Gaddale, R.C. Kamath, V.J. Shekaranaik, S.P. Pai, A Study of Dimensionality Reduction in GLCM Feature-Based Classification of Machined Surface Images, Arab. J. Sci. Eng. (2023). https://doi.org/10.1007/s13369-023-07854-1

[6] E.S. Gadelmawla, Estimation of surface roughness for turning operations using image texture features, Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 225 (2011) 1281-1292. https://doi.org/10.1177/2041297510393643

[7] E. Alegre, J. Barreiro, M. Castejón, S. Suarez, Computer vision and classification techniques on the surface finish control in machining processes, Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics). 5112 LNCS (2008) 1101-1110. https://doi.org/10.1007/978-3-540-69812-8_110

[8] E.S. Gadelmawla, A.E. Eladawi, O.B. Abouelatta, I.M. Elewa, Investigation of the cutting conditions in milling operations using image texture features, Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 222 (2008) 1395-1404. https://doi.org/10.1243/09544054JEM1173

[9] S. Nammi, S.L. Uppu, B. Ramamoorthy, Effect of orientation of workpiece and filter cut-offs in the surface roughness evaluation using machine vision, 11th IMEKO TC14 Symp. Laser Metrol. Precis. Meas. Insp. Ind. LMPMI 2014. (2014) 98-103.

[10] I. Svalina, S. Havrlišan, K. Šimunović, T. Šarić, Investigation of correlation between image features of machined surface and surface roughness, Teh. Vjesn. 27 (2020) 27-36. https://doi.org/10.17559/TV-20191212122953

[11] K. Khalili, M. Danesh, Identification of vibration level in metal cutting using undecimated wavelet transform and gray-level co-occurrence matrix texture features, Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 229 (2015) 205-213. https://doi.org/10.1177/0954405414526577

[12] E.S. Gadelmawla, A vision system for surface roughness characterization using the gray level co-occurrence matrix, 37 (2004) 577-588. https://doi.org/10.1016/j.ndteint.2004.03.004.

[13] A. Datta, S. Dutta, S.K. Pal, R. Sen, Texture Analysis of Turned Surface Images using Grey Level Co-occurrence Technique, (2012). https://doi.org/10.4028/www.scientific.net/AMR.365.38

[14] P.K. Ambadekar, C.M. Choudhari, Application of Gray Level Co-occurrence Matrix as a feature extraction technique to monitor wear of cutting tool, (2018) 21-29.

[15] S. Dutta, S.K. Pal, R. Sen, Tool Condition Monitoring in Turning by Applying Machine Vision, J. Manuf. Sci. Eng. 138 (2015) 051008. https://doi.org/10.1115/1.4031770
## References

[1] S. Jain, M. Pradhan, A. Kumar, Application of Digital Image Processing on Machined Surfaces: A review, in: R. Kumar Nayak, M. Pradhan, J.P. Davim (Eds.), Recent Adv. Mater. Manuf. Technol., Springer, 2023. https://doi.org/10.1007/978-981-99-2921-4

[2] S. Dutta, A. Datta, N. Das Chakladar, S.K. Pal, S. Mukhopadhyay, R. Sen, Detection of tool condition from the turned surface images using an accurate grey level co-occurrence technique, Precis. Eng. 36 (2012) 458-466. https://doi.org/10.1016/j.precisioneng.2012.02.004

[3] S. Dutta, S.K. Pal, R. Sen, On-machine tool prediction of flank wear from machined surface images using texture analyses and support vector regression, Precis. Eng. 43 (2016) 34-42. https://doi.org/10.1016/J.PRECISIONENG.2015.06.007

[4] D.R. Patel, V. Vakharia, M.B. Kiran, Texture classification of machined surfaces using image processing and machine learning techniques, FME Trans. 47 (2019) 865-872. https://doi.org/10.5937/fmet1904865P

[5] G. Prasad, V.S. Gaddale, R.C. Kamath, V.J. Shekaranaik, S.P. Pai, A Study of Dimensionality Reduction in GLCM Feature-Based Classification of Machined Surface Images, Arab. J. Sci. Eng. (2023). https://doi.org/10.1007/s13369-023-07854-1

[6] E.S. Gadelmawla, Estimation of surface roughness for turning operations using image texture features, Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 225 (2011) 1281-1292. https://doi.org/10.1177/2041297510393643

[7] E. Alegre, J. Barreiro, M. Castejón, S. Suarez, Computer vision and classification techniques on the surface finish control in machining processes, Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics). 5112 LNCS (2008) 1101-1110. https://doi.org/10.1007/978-3-540-69812-8_110

[8] E.S. Gadelmawla, A.E. Eladawi, O.B. Abouelatta, I.M. Elewa, Investigation of the cutting conditions in milling operations using image texture features, Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 222 (2008) 1395-1404. https://doi.org/10.1243/09544054JEM1173

[9] S. Nammi, S.L. Uppu, B. Ramamoorthy, Effect of orientation of workpiece and filter cut-offs in the surface roughness evaluation using machine vision, 11th IMEKO TC14 Symp. Laser Metrol. Precis. Meas. Insp. Ind. LMPMI 2014. (2014) 98-103.

[10] I. Svalina, S. Havrlišan, K. Šimunović, T. Šarić, Investigation of correlation between image features of machined surface and surface roughness, Teh. Vjesn. 27 (2020) 27-36. https://doi.org/10.17559/TV-20191212122953

[11] K. Khalili, M. Danesh, Identification of vibration level in metal cutting using undecimated wavelet transform and gray-level co-occurrence matrix texture features, Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 229 (2015) 205-213. https://doi.org/10.1177/0954405414526577

[12] E.S. Gadelmawla, A vision system for surface roughness characterization using the gray level co-occurrence matrix, 37 (2004) 577-588. https://doi.org/10.1016/j.ndteint.2004.03.004.

[13] A. Datta, S. Dutta, S.K. Pal, R. Sen, Texture Analysis of Turned Surface Images using Grey Level Co-occurrence Technique, (2012). https://doi.org/10.4028/www.scientific.net/AMR.365.38

[14] P.K. Ambadekar, C.M. Choudhari, Application of Gray Level Co-occurrence Matrix as a feature extraction technique to monitor wear of cutting tool, (2018) 21-29.

[15] S. Dutta, S.K. Pal, R. Sen, Tool Condition Monitoring in Turning by Applying Machine Vision, J. Manuf. Sci. Eng. 138 (2015) 051008. https://doi.org/10.1115/1.4031770
# Identification of friction coefficient between uncoated carbide tool and Ti-6Al-4V alloy under different lubrication conditions

FERSI Achraf¹,ᵃ*, AYED Yessine¹,ᵇ, LAVISSE Bruno¹,ᶜ and GERMAIN Guénaël¹,ᵈ

¹Arts et Metiers Institute of Technology, LAMPA, HESAM Université, F-49035, Angers, France

ᵃachraf.fersi@ensam.eu, ᵇyessine.ayed@ensam.eu, ᶜbruno.lavisse@ensam.eu,

ᵈguenael.germain@ensam.eu

**Keywords:** Friction Coefficient, Cryogenic Condition, Titanium Alloy, Adhesion, Numerical Simulation

**Abstract.** During machining, the friction between the tool and the workpiece (cutting face and flank face) is a significant tribological phenomenon because it strongly influences the cutting operation. Indeed, higher friction leads to an increase of cutting forces, a greater heat generation, a premature tool wear and a surface degradation. This study focuses on tool (WC/Co)/workpiece (Ti-6Al-4V) friction under different cooling conditions (dry, emulsion, cryogenic). Determining the friction coefficient requires numerical simulations to separate the tribological phenomena. For this purpose, several modeling methods are compared (Lagrangian, CEL, and ALE). Experimental tests revealed that the friction coefficient depends not only on the sliding velocity but also on lubrication modes. Specifically, the lowest friction coefficient is obtained under cryogenic condition. Adhesive phenomena on the WC/Co pin are observed in the friction zone, particularly at high sliding velocities.

**Introduction**

Titanium alloys have garnered significant attention from both industries and researchers due to their remarkable mechanical properties. These properties include good fatigue strength, corrosion resistance and a high strength-to-weight ratio, even at elevated temperatures. Titanium alloys find widespread use in various sectors, including aeronautics, automobile production, and the medical industry. However, machining titanium alloys is a challenging task. They are considered as difficult-to-cut materials. During the cutting process, their low thermal conductivity and high chemical reactivity with tool materials lead to accelerate tool wear. The friction generated at the interfaces between the cutting tool and the workpiece or/chip significantly influences tool wear, ultimately impacting tool life.

Numerous studies, such as those carried out by [1-3], have investigated the tribological behavior during machining. In particular, [4] characterized friction between Ti-6Al-4V alloy and WC/Co. Surprisingly, they found that the friction coefficient remained unchanged for dry contact, as well as under both liquid nitrogen and gaseous nitrogen conditions. Interestingly, the results from [5] revealed that the presence of liquid carbon dioxide (LCO2) during tribology tests led to an increase in the friction coefficient. However, when combined with oil (LCO2+oil), the friction coefficient decreased by a substantial 80%. Researchers have emphasized that not only the choice of cutting tool and workpiece materials affects the friction coefficient [6], but also the nature of the machining process [7] and the cooling/lubrication conditions [8].

The current study aims to investigate the effect of sliding speed and friction conditions (including dry, emulsion, and cryogenic environments) on the tribological behavior of Ti-6Al-4V alloy when using carbide tools. The apparent friction coefficient will be determined under different lubrication conditions and conduct a numerical study to identify the adhesive friction coefficient.

<figure><img src="image_1887.png" /></figure>
# Identification of friction coefficient between uncoated carbide tool and Ti-6Al-4V alloy under different lubrication conditions

FERSI Achraf¹,ᵃ*, AYED Yessine¹,ᵇ, LAVISSE Bruno¹,ᶜ and GERMAIN Guénaël¹,ᵈ

¹Arts et Metiers Institute of Technology, LAMPA, HESAM Université, F-49035, Angers, France

ᵃachraf.fersi@ensam.eu, ᵇyessine.ayed@ensam.eu, ᶜbruno.lavisse@ensam.eu,

ᵈguenael.germain@ensam.eu

**Keywords:** Friction Coefficient, Cryogenic Condition, Titanium Alloy, Adhesion, Numerical Simulation

**Abstract.** During machining, the friction between the tool and the workpiece (cutting face and flank face) is a significant tribological phenomenon because it strongly influences the cutting operation. Indeed, higher friction leads to an increase of cutting forces, a greater heat generation, a premature tool wear and a surface degradation. This study focuses on tool (WC/Co)/workpiece (Ti-6Al-4V) friction under different cooling conditions (dry, emulsion, cryogenic). Determining the friction coefficient requires numerical simulations to separate the tribological phenomena. For this purpose, several modeling methods are compared (Lagrangian, CEL, and ALE). Experimental tests revealed that the friction coefficient depends not only on the sliding velocity but also on lubrication modes. Specifically, the lowest friction coefficient is obtained under cryogenic condition. Adhesive phenomena on the WC/Co pin are observed in the friction zone, particularly at high sliding velocities.

**Introduction**

Titanium alloys have garnered significant attention from both industries and researchers due to their remarkable mechanical properties. These properties include good fatigue strength, corrosion resistance and a high strength-to-weight ratio, even at elevated temperatures. Titanium alloys find widespread use in various sectors, including aeronautics, automobile production, and the medical industry. However, machining titanium alloys is a challenging task. They are considered as difficult-to-cut materials. During the cutting process, their low thermal conductivity and high chemical reactivity with tool materials lead to accelerate tool wear. The friction generated at the interfaces between the cutting tool and the workpiece or/chip significantly influences tool wear, ultimately impacting tool life.

Numerous studies, such as those carried out by [1-3], have investigated the tribological behavior during machining. In particular, [4] characterized friction between Ti-6Al-4V alloy and WC/Co. Surprisingly, they found that the friction coefficient remained unchanged for dry contact, as well as under both liquid nitrogen and gaseous nitrogen conditions. Interestingly, the results from [5] revealed that the presence of liquid carbon dioxide (LCO2) during tribology tests led to an increase in the friction coefficient. However, when combined with oil (LCO2+oil), the friction coefficient decreased by a substantial 80%. Researchers have emphasized that not only the choice of cutting tool and workpiece materials affects the friction coefficient [6], but also the nature of the machining process [7] and the cooling/lubrication conditions [8].

The current study aims to investigate the effect of sliding speed and friction conditions (including dry, emulsion, and cryogenic environments) on the tribological behavior of Ti-6Al-4V alloy when using carbide tools. The apparent friction coefficient will be determined under different lubrication conditions and conduct a numerical study to identify the adhesive friction coefficient.

<figure><img src="image_1882.png" /></figure>
## Tribometer device and test procedure

Friction tests were conducted using an open tribometer, as depicted in Figure 1. The design of this tribometer was inspired from the work of reference [9]. The fundamental principle underlying this device involves the application of a normal force through a spherical-tipped pin made of the same material as the cutting tool (tungsten carbide). The pin presses against a rotating Ti-6Al-4V workpiece maintained in the chuck of a Leadwell CNC LTC25iL lathe. As it moves along the X-axis of the lathe, the pin’s trajectory forms a spiral. The tribometer is considered “open” because the pin does not rub against an already worn surface. A rigid spring applies the normal force ($F_N$). The resulting apparent friction coefficient ($\mu_{app}$) is calculated as the ratio between the tangential and normal forces:

$$ \mu_{app} = \frac{F_T}{F_N} \qquad (1) $$

$F_N$ is the normal force and $F_T$ is the tangential force. They were measured using a Kistler dynamometer (9257B). The tribometer provides macroscopic data that incorporates two phenomena: the adhesion phenomenon and the plastic deformation generated under the pressure of the pin on the material surface. The apparent friction coefficient does not therefore represent the real friction coefficient. Indeed, the real coefficient of friction is due exclusively to the adhesion phenomenon. Bowden and Tabor [10] proposed a decomposition of the apparent friction coefficient ($\mu_{app}$) in two parts as:

$$ \mu_{app} = \frac{F_T}{F_N} = \mu_{adh} + \mu_{plast-def} \qquad (2) $$

With $\mu_{adh}$ is the adhesive friction coefficient (or real coefficient of friction) and $\mu_{plast-def}$ is the plastic deformation coefficient. The determination of the adhesive friction coefficient $\mu_{adh}$ is not possible through experimental tests, it must be calculated by subtracting $\mu_{plast-def}$ from the measured $\mu_{app}$ coefficient. Therefore, it’s necessary to simulate the friction test numerically to decorrelate the two phenomena, and identify the adhesive coefficient $\mu_{adh}$ using an inverse approach.

<figure><img src="image_1888.png" /><figcaption>Fig. 1: Experimental setup</figcaption></figure>
## Tribometer device and test procedure

Friction tests were conducted using an open tribometer, as depicted in Figure 1. The design of this tribometer was inspired from the work of reference [9]. The fundamental principle underlying this device involves the application of a normal force through a spherical-tipped pin made of the same material as the cutting tool (tungsten carbide). The pin presses against a rotating Ti-6Al-4V workpiece maintained in the chuck of a Leadwell CNC LTC25iL lathe. As it moves along the X-axis of the lathe, the pin's trajectory forms a spiral. The tribometer is considered "open" because the pin does not rub against an already worn surface. A rigid spring applies the normal force ($F_N$). The resulting apparent friction coefficient ($\mu_{app}$) is calculated as the ratio between the tangential and normal forces:

$$ \mu_{app} = \frac{F_T}{F_N} \qquad (1) $$

$F_N$ is the normal force and $F_T$ is the tangential force. They were measured using a Kistler dynamometer (9257B). The tribometer provides macroscopic data that incorporates two phenomena: the adhesion phenomenon and the plastic deformation generated under the pressure of the pin on the material surface. The apparent friction coefficient does not therefore represent the real friction coefficient. Indeed, the real coefficient of friction is due exclusively to the adhesion phenomenon. Bowden and Tabor [10] proposed a decomposition of the apparent friction coefficient ($\mu_{app}$) in two parts as:

$$ \mu_{app} = \frac{F_T}{F_N} = \mu_{adh} + \mu_{plast-def} \qquad (2) $$

With $\mu_{adh}$ is the adhesive friction coefficient (or real coefficient of friction) and $\mu_{plast-def}$ is the plastic deformation coefficient. The determination of the adhesive friction coefficient $\mu_{adh}$ is not possible through experimental tests, it must be calculated by subtracting $\mu_{plast-def}$ from the measured $\mu_{app}$ coefficient. Therefore, it's necessary to simulate the friction test numerically to decorrelate the two phenomena, and identify the adhesive coefficient $\mu_{adh}$ using an inverse approach.

<figure><img src="image_1883.png" /><figcaption>Fig. 1: Experimental setup</figcaption></figure>
## Testing conditions

According to the results of [11] and [12], it has been proved that the contact pressure in the cutting zone during the machining of titanium alloy Ti-6Al-4V using carbide tools reaches a value of 1.5 GPa. The magnitude of the normal force ($F_N$) required to maintain this contact pressure level at the pin/workpiece interface, was estimated by numerical simulation. The calculated value is 350 N. Table 1 presents the selected friction conditions. $V_g$ represents the sliding speed between the pin and the workpiece.

<table><caption>Table 1: friction conditions</caption><thead><tr><td>Vg (m/min)</td><td>F<sub>N</sub> (N)</td></tr></thead><tbody><tr><td>15</td><td rowspan="6">350</td></tr><tr><td>30</td></tr><tr><td>45</td></tr><tr><td>60</td></tr><tr><td>75</td></tr><tr><td>90</td></tr></tbody></table>

## Numerical simulation

### Model presentation

To simulate the friction test, ABAQUS/Explicit FEM solver is used. The pin is modeled by a spherical tip (Ø 9 mm) and an upper rigid part. It is meshed using C3D4 tetrahedral elements of a size of 400 µm. A reference point (RP) located on the rigid zone permits to manage its displacement. The workpiece is modeled with a parallelepiped form, and it is meshed using C3D8R hexagonal elements. The mesh size in the contact zone is around 50 µm. Mesh size and type are selected based on literature review (e.g. [9], [14], and [15]) and on a sensitivity study of results to mesh parameters. The model geometry is presented by Fig.2. The mechanical properties of the work material and the pin are reported in table 2.

<figure><img src="image_1889.png" /><figcaption>Fig.2: Geometry of the model</figcaption></figure>

The simulation is carried out in two steps: (i) an indentation step and (ii) a scratching step. First of all, during the indentation step, the pin moves vertically as it indents the material, to reach the
## Testing conditions

According to the results of [11] and [12], it has been proved that the contact pressure in the cutting zone during the machining of titanium alloy Ti-6Al-4V using carbide tools reaches a value of 1.5 GPa. The magnitude of the normal force ($F_N$) required to maintain this contact pressure level at the pin/workpiece interface, was estimated by numerical simulation. The calculated value is 350 N. Table 1 presents the selected friction conditions. $V_g$ represents the sliding speed between the pin and the workpiece.

<table><caption>Table 1: friction conditions</caption><thead><tr><td>Vg (m/min)</td><td>F<sub>N</sub> (N)</td></tr></thead><tbody><tr><td>15</td><td rowspan="6">350</td></tr><tr><td>30</td></tr><tr><td>45</td></tr><tr><td>60</td></tr><tr><td>75</td></tr><tr><td>90</td></tr></tbody></table>

## Numerical simulation

Model presentation

To simulate the friction test, ABAQUS/Explicit FEM solver is used. The pin is modeled by a spherical tip (Ø 9 mm) and an upper rigid part. It is meshed using C3D4 tetrahedral elements of a size of 400 µm. A reference point (RP) located on the rigid zone permits to manage its displacement. The workpiece is modeled with a parallelepiped form, and it is meshed using C3D8R hexagonal elements. The mesh size in the contact zone is around 50 µm. Mesh size and type are selected based on literature review (e.g. [9], [14], and [15]) and on a sensitivity study of results to mesh parameters. The model geometry is presented by Fig.2. The mechanical properties of the work material and the pin are reported in table 2.

<figure><img src="image_1884.png" /><figcaption>Fig.2: Geometry of the model</figcaption></figure>

The simulation is carried out in two steps: (i) an indentation step and (ii) a scratching step. First of all, during the indentation step, the pin moves vertically as it indents the material, to reach the
penetration depth (h) required to maintain the targeted contact pressure. Then, during the scratching step, the pin moves horizontally rubbing on the contact surface with the sliding speed ($V_g$). The workpiece is fixed via the nodes of the bottom surface (Fig.2).

<table><caption>Table 2: Mechanical properties of materials [13]</caption><thead><tr><th>Properties</th><th>WC</th><th>Ti-6Al-4V</th></tr></thead><tbody><tr><td>Density ρ (Kg m<sup>-3</sup>)</td><td>12800</td><td>4420</td></tr><tr><td>Young's modulus E (Gpa)</td><td>630</td><td>114.5</td></tr><tr><td>Poisson's ratio v</td><td>0.22</td><td>0.31</td></tr><tr><td>Specific heat C<sub>p</sub> (J Kg<sup>-1</sup>C<sup>-1</sup>)</td><td>226</td><td>580</td></tr><tr><td>Heat conductivity λ (WC<sup>-1</sup>m<sup>-1</sup>)</td><td>44.6</td><td>6.6</td></tr></tbody></table>

The Johnson-Cook plasticity model is used to describe the mechanical behavior of the Ti-6Al-4V (equation 3). It is based on the Von Mises yield criterion and it considers the effect of strain hardening, strain rate and temperature. The model parameters are reported in table 3:

$$ \bar{\sigma} = (A + B \cdot \bar{\varepsilon}_p^n) \cdot \left[ 1 + C \cdot \ln\left(\frac{\dot{\varepsilon}_p}{\dot{\varepsilon}_{p0}}\right) \right] \cdot \left[ 1 - \left(\frac{T - T_{amb}}{T_{fus} - T_{amb}}\right)^m \right] \quad (3) $$

<table><caption>Table 3: Johnson-Cook parameters of Ti-6Al-4V [13]</caption><thead><tr><th>A (MPa)</th><th>B (MPa)</th><th>n</th><th>m</th><th>T<sub>fus</sub> (°C)</th><th>T<sub>amb</sub> (°C)</th></tr></thead><tbody><tr><td>800</td><td>743</td><td>0,3</td><td>0,7</td><td>1655</td><td>20</td></tr></tbody></table>

## Contact modelling

The contact between the pin and the workpiece is modeled by Coulomb friction law as follows:

$$ \tau_f = \mu_{adh} \sigma_n \quad (4) $$

With $\tau_f$ is the tangential stress, $\sigma_n$ is the normal stress and $\mu_{adh}$ is the adhesive friction coefficient.

## Numerical modeling strategies

According to the literature (e.g. [9], [14] and [15]) three distinct approaches could be used to simulate the friction test: (i) Lagrangian approach, (ii) CEL (Coupled Eulerian-Lagrangian) approach and (iii) ALE approach (Arbitrary Eulerian-Lagrangian) approach. In the present work, these different strategies have been deployed and tested. Their performance has been evaluated in terms of results, accuracy and computation time.

For this study, to determine the best modeling strategies, the models have the same configurations. The adhesive friction coefficient $\mu_{adh}$ introduced into the model to characterize the mechanical contact, was maintained at 0.1. The sliding speed ($V_g$) was fixed at 50 m/min.

## Numerical results

For each simulation, the normal and tangential forces ($F_N$ and $F_T$) were recovered as shown in Fig.3. The apparent numerical friction coefficient is calculated according to equation (1).
penetration depth (h) required to maintain the targeted contact pressure. Then, during the scratching step, the pin moves horizontally rubbing on the contact surface with the sliding speed ($V_g$). The workpiece is fixed via the nodes of the bottom surface (Fig.2).

<table><caption>Table 2: Mechanical properties of materials [13]</caption><thead><tr><th>Properties</th><th>WC</th><th>Ti-6Al-4V</th></tr></thead><tbody><tr><td>Density ρ (Kg m<sup>-3</sup>)</td><td>12800</td><td>4420</td></tr><tr><td>Young's modulus E (Gpa)</td><td>630</td><td>114.5</td></tr><tr><td>Poisson's ratio v</td><td>0.22</td><td>0.31</td></tr><tr><td>Specific heat C<sub>p</sub> (J Kg<sup>-1</sup>C<sup>-1</sup>)</td><td>226</td><td>580</td></tr><tr><td>Heat conductivity λ (WC<sup>-1</sup>m<sup>-1</sup>)</td><td>44.6</td><td>6.6</td></tr></tbody></table>

The Johnson-Cook plasticity model is used to describe the mechanical behavior of the Ti-6Al-4V (equation 3). It is based on the Von Mises yield criterion and it considers the effect of strain hardening, strain rate and temperature. The model parameters are reported in table 3:

$$ \bar{\sigma} = (A + B \cdot \bar{\varepsilon}_p^n) \cdot \left[ 1 + C \cdot \ln\left(\frac{\dot{\varepsilon}_p}{\dot{\varepsilon}_{p0}}\right) \right] \cdot \left[ 1 - \left(\frac{T - T_{amb}}{T_{fus} - T_{amb}}\right)^m \right] \quad (3) $$

<table><caption>Table 3: Johnson-Cook parameters of Ti-6Al-4V [13]</caption><thead><tr><th>A (MPa)</th><th>B (MPa)</th><th>n</th><th>m</th><th>T<sub>fus</sub> (°C)</th><th>T<sub>amb</sub> (°C)</th></tr></thead><tbody><tr><td>800</td><td>743</td><td>0,3</td><td>0,7</td><td>1655</td><td>20</td></tr></tbody></table>

## Contact modelling

The contact between the pin and the workpiece is modeled by Coulomb friction law as follows:

$$ \tau_f = \mu_{adh} \sigma_n \quad (4) $$

With $\tau_f$ is the tangential stress, $\sigma_n$ is the normal stress and $\mu_{adh}$ is the adhesive friction coefficient.

## Numerical modeling strategies

According to the literature (e.g. [9], [14] and [15]) three distinct approaches could be used to simulate the friction test: (i) Lagrangian approach, (ii) CEL (Coupled Eulerian-Lagrangian) approach and (iii) ALE approach (Arbitrary Eulerian-Lagrangian) approach. In the present work, these different strategies have been deployed and tested. Their performance has been evaluated in terms of results, accuracy and computation time.

For this study, to determine the best modeling strategies, the models have the same configurations. The adhesive friction coefficient $\mu_{adh}$ introduced into the model to characterize the mechanical contact, was maintained at 0.1. The sliding speed ($V_g$) was fixed at 50 m/min.

## Numerical results

For each simulation, the normal and tangential forces ($F_N$ and $F_T$) were recovered as shown in Fig.3. The apparent numerical friction coefficient is calculated according to equation (1).
<figure><img src="image_1890.png" /><figcaption>Fig. 3: Evolution of numerical forces; Lagrangian model</figcaption></figure>

Table 5 highlights the results obtained for each modeling strategy. Although the difference in the friction coefficient is less than 3%, the computation time varies considerably. In fact, the "Lagrangian" model is 6 times faster than the "CEL" model, and it is 14 times faster than the "ALE" model. This substantial difference in computation time is an important factor to consider when choosing the appropriate model for a given analysis. The results show that Lagrangian model is the best choice. This model is used in the rest of the study to calculate the coefficient of friction.

<table><caption>Table 4: Simulation parameters</caption><thead><tr><th></th><th>Parameters</th><th>CEL</th><th>ALE</th><th>Lagrangian</th></tr></thead><tbody><tr><td rowspan="4">Workpiece</td><td>Element size (µm)</td><td>Local</td><td>50</td><td>50</td><td>50</td></tr><tr><td></td><td>Global</td><td>500</td><td>500</td><td>500</td></tr><tr><td>Type</td><td></td><td>EC3D8R</td><td>C3D8R</td><td>C3D8R</td></tr><tr><td>Number</td><td></td><td>14 400</td><td>19 200</td><td>19 200</td></tr><tr><td rowspan="3">Pin</td><td>Element size (µm)</td><td></td><td>400</td><td>400</td><td>400</td></tr><tr><td>Type</td><td></td><td>C3D4</td><td>C3D4</td><td>C3D4</td></tr><tr><td>Number</td><td></td><td>819</td><td>819</td><td>819</td></tr><tr><td rowspan="3">Rigid part</td><td>Size (µm)</td><td></td><td>600</td><td>600</td><td>600</td></tr><tr><td>Type</td><td></td><td>C3D4</td><td>C3D4</td><td>C3D4</td></tr><tr><td>Number</td><td></td><td>230</td><td>230</td><td>230</td></tr></tbody></table>

<table><caption>Table 5: Friction coefficients</caption><thead><tr><th>Model</th><th>Apparent friction coefficient</th><th>Computing time</th></tr></thead><tbody><tr><td>Lagrangian</td><td>0.122</td><td>2h and 15 min</td></tr><tr><td>CEL</td><td>0.125</td><td>14h</td></tr><tr><td>ALE</td><td>0.127</td><td>33h</td></tr></tbody></table>

## Adhesive friction coefficient

The determination of the adhesive friction coefficient $\mu_{adh}$ proceeds through several successive steps. Initially, experimental friction tests were conducted to determine the apparent friction
<figure><img src="image_1885.png" /><figcaption>Fig. 3: Evolution of numerical forces; Lagrangian model</figcaption></figure>

Table 5 highlights the results obtained for each modeling strategy. Although the difference in the friction coefficient is less than 3%, the computation time varies considerably. In fact, the "Lagrangian" model is 6 times faster than the "CEL" model, and it is 14 times faster than the "ALE" model. This substantial difference in computation time is an important factor to consider when choosing the appropriate model for a given analysis. The results show that Lagrangian model is the best choice. This model is used in the rest of the study to calculate the coefficient of friction.

<table><caption>Table 4: Simulation parameters</caption><thead><tr><th></th><th>Parameters</th><th>CEL</th><th>ALE</th><th>Lagrangian</th></tr></thead><tbody><tr><td rowspan="4">Workpiece</td><td>Element size (μm)</td><td>Local</td><td>50</td><td>50</td><td>50</td></tr><tr><td></td><td>Global</td><td>500</td><td>500</td><td>500</td></tr><tr><td>Type</td><td></td><td>EC3D8R</td><td>C3D8R</td><td>C3D8R</td></tr><tr><td>Number</td><td></td><td>14 400</td><td>19 200</td><td>19 200</td></tr><tr><td rowspan="3">Pin</td><td>Element size (μm)</td><td></td><td>400</td><td>400</td><td>400</td></tr><tr><td>Type</td><td></td><td>C3D4</td><td>C3D4</td><td>C3D4</td></tr><tr><td>Number</td><td></td><td>819</td><td>819</td><td>819</td></tr><tr><td rowspan="3">Rigid part</td><td>Size (μm)</td><td></td><td>600</td><td>600</td><td>600</td></tr><tr><td>Type</td><td></td><td>C3D4</td><td>C3D4</td><td>C3D4</td></tr><tr><td>Number</td><td></td><td>230</td><td>230</td><td>230</td></tr></tbody></table>

<table><caption>Table 5: Friction coefficients</caption><thead><tr><th>Model</th><th>Apparent friction coefficient</th><th>Computing time</th></tr></thead><tbody><tr><td>Lagrangian</td><td>0.122</td><td>2h and 15 min</td></tr><tr><td>CEL</td><td>0.125</td><td>14h</td></tr><tr><td>ALE</td><td>0.127</td><td>33h</td></tr></tbody></table>

## Adhesive friction coefficient

The determination of the adhesive friction coefficient $\mu_{adh}$ proceeds through several successive steps. Initially, experimental friction tests were conducted to determine the apparent friction
coefficient ($\mu_{app-exp}$). Then, numerical simulations were performed to reach the same experimental normal force ($F_{N_num} = F_{N_exp} = 350$ N). Finally, numerical simulations were conducted to obtain a numerical friction coefficient similar to the experimental friction coefficient by adjusting the adhesive friction coefficient $\mu_{adh}$ introduced in Abaqus ($\mu_{app-num} = \mu_{app-exp}$). The sliding velocities (Vg) are the same values as tested during the experimental approach (table 1).

To separate the two parts of the apparent friction coefficient mentioned in equation (2) and identify the adhesive friction coefficient $\mu_{adh}$, one simply subtracts the adhesive friction coefficient ($\mu_{adh}$) from the numerical friction coefficient ($\mu_{app-num}$).

Fig.4 presents the proportions of the adhesive friction coefficient and the plastic deformation for each sliding speed, it appears that adhesive friction coefficient $\mu_{adh}$ comprises over 95% of the apparent friction, irrespective of the sliding speed. Consequently, the apparent friction coefficient measured with the tribometer corresponds mainly to the adhesive friction coefficient. As a result, throughout the rest of the study, only the apparent coefficient of friction will be considered.

<figure><img src="image_1891.png" /><figcaption>Fig.4 : Proportion of adhesion and plastic deformation; dry friction</figcaption></figure>

## Experimental tests conditions

The friction tests were conducted under two lubrication conditions:

* **Cryogenic assistance:** A nozzle with a 2 mm diameter is used to project liquid nitrogen (LN2) onto the contact zone (pin/workpiece) with a flow rate of 2.95 L/min and under a pressure of 12 bar.

* **Emulsion:** A cutting fluid (ECOCOOL CS+) with a 6% oil concentration in the emulsion is projected onto the pin/workpiece interface using a nozzle of 8 mm diameter and under a pressure of 20 bar.

## Results and discussion

Fig.5 illustrates the evolution of experimental apparent friction coefficient versus the sliding speed for the three friction conditions. The results clearly demonstrate that the experimental apparent friction coefficient ($\mu_{app-exp}$) varies as the sliding speed increases. The friction coefficient exhibits similar trends across all tested conditions, revealing two behaviors. Below a sliding speed of 45 m/min, the friction coefficient decreases until it reaches a minimum value. Conversely, at sliding speeds from 45 m/min to 90 m/min, the friction coefficient starts to increase.
coefficient ($\mu_{app-exp}$). Then, numerical simulations were performed to reach the same experimental normal force ($F_{N_num} = F_{N_exp} = 350$ N). Finally, numerical simulations were conducted to obtain a numerical friction coefficient similar to the experimental friction coefficient by adjusting the adhesive friction coefficient $\mu_{adh}$ introduced in Abaqus ($\mu_{app-num} = \mu_{app-exp}$). The sliding velocities (Vg) are the same values as tested during the experimental approach (table 1).

To separate the two parts of the apparent friction coefficient mentioned in equation (2) and identify the adhesive friction coefficient $\mu_{adh}$, one simply subtracts the adhesive friction coefficient ($\mu_{adh}$) from the numerical friction coefficient ($\mu_{app-num}$).

Fig.4 presents the proportions of the adhesive friction coefficient and the plastic deformation for each sliding speed, it appears that adhesive friction coefficient $\mu_{adh}$ comprises over 95% of the apparent friction, irrespective of the sliding speed. Consequently, the apparent friction coefficient measured with the tribometer corresponds mainly to the adhesive friction coefficient. As a result, throughout the rest of the study, only the apparent coefficient of friction will be considered.

<figure><img src="image_1886.png" /><figcaption>Fig.4 : Proportion of adhesion and plastic deformation; dry friction</figcaption></figure>

## Experimental tests conditions

The friction tests were conducted under two lubrication conditions:

* **Cryogenic assistance:** A nozzle with a 2 mm diameter is used to project liquid nitrogen (LN2) onto the contact zone (pin/workpiece) with a flow rate of 2.95 L/min and under a pressure of 12 bar.

* **Emulsion:** A cutting fluid (ECOCOOL CS+) with a 6% oil concentration in the emulsion is projected onto the pin/workpiece interface using a nozzle of 8 mm diameter and under a pressure of 20 bar.

## Results and discussion

Fig.5 illustrates the evolution of experimental apparent friction coefficient versus the sliding speed for the three friction conditions. The results clearly demonstrate that the experimental apparent friction coefficient ($\mu_{app-exp}$) varies as the sliding speed increases. The friction coefficient exhibits similar trends across all tested conditions, revealing two behaviors. Below a sliding speed of 45 m/min, the friction coefficient decreases until it reaches a minimum value. Conversely, at sliding speeds from 45 m/min to 90 m/min, the friction coefficient starts to increase.
<figure><img src="image_1892.png" /><figcaption>Fig.5: Evolution of friction coefficient in function of the sliding speed, under different lubrication conditions</figcaption></figure>

These results are in line with the works of [16] and [17] concerning the evolution of the friction coefficient in function of the sliding speed.

The introduction of a lubricating fluid influences the tribological behavior of the pin/workpiece interface. Indeed, the fluid forms a film that modifies the temperature and the properties of this interface. These results are consistent with those of the study [16], which reported that lubrication using ester oil, resulted in a lower friction coefficient than dry friction.

The authors of the study [17] noted that the friction coefficient decreased under the effect of liquid nitrogen (LN2) compared to dry friction. However, contradictory results were reported by the authors of studies [4] and [8], who concluded that cryogenic assistance methods with LN2, LCO2, or LCO2+MQL had no effect on the friction coefficient.

Figure 6 illustrates a chemical analysis conducted via Energy-Dispersive X-ray Spectroscopy (EDS) of the pin surface. The results show that titanium which adhered to pin surface is affected by both the lubrication fluid nature and the sliding velocity. The EDS analysis are consistent with the results of [16], which reported that the cryogenic bath limited efficiently the occurrence of adhesion. Similarly, the authors of the study [18] indicated that the introduction of lubrication fluid or Minimum Quantity Lubrication (MQL) reduced the contact area by 35 % compared to dry friction. These studies confirm that the choice of lubrication configuration restricts the phenomenon of adhesion compared to dry friction.
<figure><img src="image_1887.png" /><figcaption>Fig.5: Evolution of friction coefficient in function of the sliding speed, under different lubrication conditions</figcaption></figure>

These results are in line with the works of [16] and [17] concerning the evolution of the friction coefficient in function of the sliding speed.

The introduction of a lubricating fluid influences the tribological behavior of the pin/workpiece interface. Indeed, the fluid forms a film that modifies the temperature and the properties of this interface. These results are consistent with those of the study [16], which reported that lubrication using ester oil, resulted in a lower friction coefficient than dry friction.

The authors of the study [17] noted that the friction coefficient decreased under the effect of liquid nitrogen (LN2) compared to dry friction. However, contradictory results were reported by the authors of studies [4] and [8], who concluded that cryogenic assistance methods with LN2, LCO2, or LCO2+MQL had no effect on the friction coefficient.

Figure 6 illustrates a chemical analysis conducted via Energy-Dispersive X-ray Spectroscopy (EDS) of the pin surface. The results show that titanium which adhered to pin surface is affected by both the lubrication fluid nature and the sliding velocity. The EDS analysis are consistent with the results of [16], which reported that the cryogenic bath limited efficiently the occurrence of adhesion. Similarly, the authors of the study [18] indicated that the introduction of lubrication fluid or Minimum Quantity Lubrication (MQL) reduced the contact area by 35 % compared to dry friction. These studies confirm that the choice of lubrication configuration restricts the phenomenon of adhesion compared to dry friction.
<figure><img src="image_1893.png" /><figcaption>Fig. 6: EDS analysis</figcaption></figure>

According to the previous results, the extreme contact conditions (high pressure and temperature) resulted in the phenomenon of adhesion. Figure 6 shows that adhesion zones are relatively similar for sliding speeds between 15 and 45 m/min. However, the size of adhesion zone increases significantly at higher sliding speed. This is more pronounced for the cryogenic configuration. These results are consistent with those of previous researches such as [4] and [19]. The high temperature in the contact zone leads to rise the temperature of the deformed region of the material, resulting in a change of its mechanical properties. The adhered layer of titanium transformed the nature of contact from carbide/titanium to titanium/titanium. The shearing between the adhered titanium and the titanium of the work material increases the tangential forces, resulting in an increase in the friction coefficient. The presence of a lubrication fluid has a notable impact on the tool/workpiece interface behavior by reducing the friction coefficient. Additionally, the injection of liquid nitrogen into the contact zone leads to restrict the adhesion phenomena. Consequently, the shear forces between the adhered portion and the workpiece decrease, resulting in a reduction of the friction coefficient, of the order of 5 % compared to emulsion and 12 % compared to dry machining.

## Conclusion

This study investigated the frictional behavior of Ti-6Al-4V using tungsten carbide tools under different conditions: dry, lubricated, and cryogenic (LN2). Numerical modeling was employed to determine the coefficient of adherent friction (real friction coefficient). The tests were conducted at sliding speeds ranging from 15 m/min to 90 m/min, with a force of 350 N, corresponding to an estimated contact pressure of 1.5 GPa. Under these frictional conditions, titanium exhibits strong adhesion, significantly impacting the contact behavior. The results highlighted the influence of varying sliding speed on the friction coefficient. In the emulsion condition the friction coefficient is reduced by 5% compared to dry condition. It is further reduced by 12% under cryogenic cooling.

## Acknowledgments

The authors express their gratitude to the Cetim, and all the esteemed partners involved in the ScCRYO2 project, as well as the Carnot ARTS Institute, the Pays de la Loire Region and Angers Loire Métropole for their financial support.
<figure><img src="image_1888.png" /><figcaption>Fig. 6: EDS analysis</figcaption></figure>

According to the previous results, the extreme contact conditions (high pressure and temperature) resulted in the phenomenon of adhesion. Figure 6 shows that adhesion zones are relatively similar for sliding speeds between 15 and 45 m/min. However, the size of adhesion zone increases significantly at higher sliding speed. This is more pronounced for the cryogenic configuration. These results are consistent with those of previous researches such as [4] and [19]. The high temperature in the contact zone leads to rise the temperature of the deformed region of the material, resulting in a change of its mechanical properties. The adhered layer of titanium transformed the nature of contact from carbide/titanium to titanium/titanium. The shearing between the adhered titanium and the titanium of the work material increases the tangential forces, resulting in an increase in the friction coefficient. The presence of a lubrication fluid has a notable impact on the tool/workpiece interface behavior by reducing the friction coefficient. Additionally, the injection of liquid nitrogen into the contact zone leads to restrict the adhesion phenomena. Consequently, the shear forces between the adhered portion and the workpiece decrease, resulting in a reduction of the friction coefficient, of the order of 5 % compared to emulsion and 12 % compared to dry machining.

## Conclusion

This study investigated the frictional behavior of Ti-6Al-4V using tungsten carbide tools under different conditions: dry, lubricated, and cryogenic (LN2). Numerical modeling was employed to determine the coefficient of adherent friction (real friction coefficient). The tests were conducted at sliding speeds ranging from 15 m/min to 90 m/min, with a force of 350 N, corresponding to an estimated contact pressure of 1.5 GPa. Under these frictional conditions, titanium exhibits strong adhesion, significantly impacting the contact behavior. The results highlighted the influence of varying sliding speed on the friction coefficient. In the emulsion condition the friction coefficient is reduced by 5% compared to dry condition. It is further reduced by 12% under cryogenic cooling.

## Acknowledgments

The authors express their gratitude to the Cetim, and all the esteemed partners involved in the ScCRYO2 project, as well as the Carnot ARTS Institute, the Pays de la Loire Region and Angers Loire Métropole for their financial support.
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[1] M. Bogdan-Chudy et al., « Tribological and thermal behavior with wear identification in contact interaction of the Ti6Al4V-sintered carbide with AlTiN coatings pair », Tribol. Int., vol. 167, p. 107394, mars 2022. https://doi.org/10.1016/j.triboint.2021.107394

[2] N. Uçak, J. Outeiro, K. Aslantas, A. Çiçek, et B. Çetin, « Determination of the friction coefficients between uncoated WC-Co tools and L-PBF and wrought Ti-6Al-4V alloys for micro-milling simulations », Procedia CIRP, vol. 117, p. 281-286, 2023. https://doi.org/10.1016/j.procir.2023.03.048

[3] S. Basten, L. Seis, M. Oehler, B. Kirsch, H. Hasse, et J. C. Aurich, « Tribological behaviour of AISI 4140 and WC-Co carbides during dry condition, using cryogenic media, and sub-zero metalworking fluids at high contact stresses », Wear, vol. 512-513, p. 204525, janv. 2023. https://doi.org/10.1016/j.wear.2022.204525

[4] C. Courbon, F. Pusavec, F. Dumont, J. Rech, et J. Kopac, « Tribological behaviour of Ti6Al4V and Inconel718 under dry and cryogenic conditions—Application to the context of machining with carbide tools », Tribol. Int., vol. 66, p. 72-82, oct. 2013. https://doi.org/10.1016/j.triboint.2013.04.010

[5] F. Pušavec, L. Sterle, M. Kalin, D. Mallipeddi, et P. Krajnik, « Tribology of solid-lubricated liquid carbon dioxide assisted machining », CIRP Ann., vol. 69, no 1, p. 69-72, 2020. https://doi.org/10.1016/j.cirp.2020.04.033

[6] D. Smolenicki, J. Boos, F. Kuster, H. Roelofs, et C. F. Wyen, « In-process measurement of friction coefficient in orthogonal cutting », CIRP Ann., vol. 63, no 1, p. 97-100, 2014. https://doi.org/10.1016/j.cirp.2014.03.083

[7] C. Bonnet, J. Rech, et G. Poulachon, « Characterization of friction coefficient for simulating drilling contact for titanium TiAl6V4 alloy », CIRP J. Manuf. Sci. Technol., vol. 29, p. 130-137, mai 2020. https://doi.org/10.1016/j.cirpj.2020.03.003

[8] C. Courbon, L. Sterle, M. Cici, et F. Pusavec, « Tribological Effect of Lubricated Liquid Carbon Dioxide on TiAl6V4 and AISI1045 under Extreme Contact Conditions », Procedia Manuf., vol. 47, p. 511-516, 2020. https://doi.org/10.1016/j.promfg.2020.04.139

[9] F. Zemzemi, J. Rech, W. Ben Salem, A. Dogui, et P. Kapsa, « Identification of a friction model at tool/chip/workpiece interfaces in dry machining of AISI4142 treated steels », J. Mater. Process. Technol., vol. 209, no 8, p. 3978-3990, avr. 2009. https://doi.org/10.1016/j.jmatprotec.2008.09.019

[10] F. P. Bowden et D. Tabor, The Friction and Lubrication of Solids. Clarendon Press, 2001.

[11] M. Sima et T. Özel, « Modified material constitutive models for serrated chip formation simulations and experimental validation in machining of titanium alloy Ti-6Al-4V », Int. J. Mach. Tools Manuf., vol. 50, no 11, p. 943-960, nov. 2010. https://doi.org/10.1016/j.ijmachtools.2010.08.004

[12] Y. Karpat, « Temperature dependent flow softening of titanium alloy Ti6Al4V: An investigation using finite element simulation of machining », J. Mater. Process. Technol., vol. 211, no 4, p. 737-749, avr. 2011. https://doi.org/10.1016/j.jmatprotec.2010.12.008

[13] C. Ramirez, « Critères d'optimisation des alliages de TITane pour améliorer leur USinabilité », p. 253, 2009.
[14] H. Ben Abdelali, C. Claudin, J. Rech, W. Ben Salem, Ph. Kapsa, et A. Dogui, « Experimental characterization of friction coefficient at the tool-chip-workpiece interface during dry cutting of AISI 1045 », Wear, vol. 286-287, p. 108-115, mai 2012. https://doi.org/10.1016/j.wear.2011.05.030

[15] N. Fezai et al., « Characterization of friction for the simulation of multi-pass orthogonal micro-cutting of 316L stainless steel », Procedia CIRP, vol. 108, p. 845-850, 2022. https://doi.org/10.1016/j.procir.2022.03.130

[16] L. Meier, N. Schaal, et K. Wegener, « In-process Measurement of the Coefficient of Friction on Titanium », Procedia CIRP, vol. 58, p. 163-168, 2017. https://doi.org/10.1016/j.procir.2017.03.212

[17] E. M. Skalante, H. Makich, et M. Nouari, « Effect of cryogenic friction conditions on surface quality », Procedia CIRP, vol. 108, p. 675-680, 2022. https://doi.org/10.1016/j.procir.2022.03.105

[18] L. Sterle, F. Pušavec, et M. Kalin, « Determination of friction coefficient in cutting processes: comparison between open and closed tribometers », Procedia CIRP, vol. 82, p. 101-106, 2019. https://doi.org/10.1016/j.procir.2019.04.159

[19] A. Egaña, J. Rech, et P. J. Arrazola, « Characterization of Friction and Heat Partition Coefficients during Machining of a TiAl6V4 Titanium Alloy and a Cemented Carbide », Tribol. Trans., vol. 55, no 5, p. 665-676, sept. 2012. https://doi.org/10.1080/10402004.2012.692007
[14] H. Ben Abdelali, C. Claudin, J. Rech, W. Ben Salem, Ph. Kapsa, et A. Dogui, « Experimental characterization of friction coefficient at the tool-chip-workpiece interface during dry cutting of AISI 1045 », Wear, vol. 286-287, p. 108-115, mai 2012. https://doi.org/10.1016/j.wear.2011.05.030

[15] N. Fezai et al., « Characterization of friction for the simulation of multi-pass orthogonal micro-cutting of 316L stainless steel », Procedia CIRP, vol. 108, p. 845-850, 2022. https://doi.org/10.1016/j.procir.2022.03.130

[16] L. Meier, N. Schaal, et K. Wegener, « In-process Measurement of the Coefficient of Friction on Titanium », Procedia CIRP, vol. 58, p. 163-168, 2017. https://doi.org/10.1016/j.procir.2017.03.212

[17] E. M. Skalante, H. Makich, et M. Nouari, « Effect of cryogenic friction conditions on surface quality », Procedia CIRP, vol. 108, p. 675-680, 2022. https://doi.org/10.1016/j.procir.2022.03.105

[18] L. Sterle, F. Pušavec, et M. Kalin, « Determination of friction coefficient in cutting processes: comparison between open and closed tribometers », Procedia CIRP, vol. 82, p. 101-106, 2019. https://doi.org/10.1016/j.procir.2019.04.159

[19] A. Egaña, J. Rech, et P. J. Arrazola, « Characterization of Friction and Heat Partition Coefficients during Machining of a TiAl6V4 Titanium Alloy and a Cemented Carbide », Tribol. Trans., vol. 55, no 5, p. 665-676, sept. 2012. https://doi.org/10.1080/10402004.2012.692007
# Artificial Intelligence techniques and Internet of things sensors for tool condition monitoring in milling: A review

FERRISI Stefania¹,ᵃ*, AMBROGIO Giuseppina¹,ᵇ, GUIDO Rosita¹,ᶜ and UMBRELLO Domenico¹,ᵈ

¹Dep. of Mechanical, Energy and Management Engineering, University of Calabria, 87036 Rende (CS) - Italy

ªstefania.ferrisi@unical.it, ᵇgiuseppina.ambrogio@unical.it, ᶜrosita.guido@unical.it, ᵈdomenico.umbrello@unical.it

**Keywords:** Tool Condition Monitoring, Artificial Intelligence, Milling Process

**Abstract.** Milling is a machining process that involves removing material from a workpiece using a rotating cutting tool. During the milling process, the cutter is affected by a progressive degradation due to the grinding of the workpiece which results in a decline in product quality and a sharp increase in energy consumption and production costs. For these reasons, Tool Condition Monitoring has emerged as the essential approach in the machining industry. The application of Artificial Intelligence (AI) systems that incorporate the use of various Internet of Things sensors (IoT) to support the tool condition monitoring in the milling process has recently been a subject of great interest to researchers. It helps to achieve goals required in the modern manufacturing industries in terms of sustainability, cost reduction, and quality improvement. This review article focuses on the application of IoT sensors for recording acoustic emissions, to conduct tool condition monitoring of the milling cutting tools based on AI techniques. The discussion includes an analysis of the principal sensory systems and their main advantages and disadvantages for the milling process. Moreover, trends and problems of applied AI techniques for tool condition monitoring are highlighted.

**Introduction**

Milling process is a machining method in which a tool, called milling cutter, advances into a workpiece to remove materials. Due to friction between the cutting tool and the workpiece, degradation of the cutting tool occurs, resulting in tool surface wear, deformation, and even tool breakage [1]. Monitoring the tool condition is crucial to making timely decisions about replacing the tool before the failure condition, avoiding premature replacement, which reduces tool life and increases machine downtime, or delayed replacement, risking product quality and escalating production costs. In modern industry, Tool Condition Monitoring (TCM) systems can be constructed with the use of sensors that can do tasks such as "seeing, hearing, smelling, and touching" [2]. These sensors can be classified as direct or indirect [3]. The sensor is defined as direct if data are collected directly from the tool response to the contact with the workpiece, while it is defined as indirect if data are collected from a response external to the cutting interface. Examples of indirect TCM systems include cutting force, vibration, acoustic emission (AE), image, thermal, and other sensors [4]. Among them, AE exhibits high sensitivity to the sequence of events during tool contact, chip formation, and disengagement, as evidenced by the increasing trend in the averaged root mean squared (RMS) energy with flank wear growth [5-7]. Also, the variation of the AE frequency with various forms of tool wear was demonstrated [8].

The integration of AI techniques in the TCM is a hot research topic [9]. It allows to predict the state of wear of the tools and thus determine the best process parameters for increasing their useful life as well as for identifying the best time for replacement, also preventing damage to the processed materials. This can lead to huge savings in terms of materials consumed and therefore

<figure><img src="image_1894.png" /></figure>
# Artificial Intelligence techniques and Internet of things sensors for tool condition monitoring in milling: A review

FERRISI Stefania¹,ᵃ*, AMBROGIO Giuseppina¹,ᵇ, GUIDO Rosita¹,ᶜ and UMBRELLO Domenico¹,ᵈ

¹Dep. of Mechanical, Energy and Management Engineering, University of Calabria, 87036 Rende (CS) - Italy

ªstefania.ferrisi@unical.it, ᵇgiuseppina.ambrogio@unical.it, ᶜrosita.guido@unical.it, ᵈdomenico.umbrello@unical.it

**Keywords:** Tool Condition Monitoring, Artificial Intelligence, Milling Process

**Abstract.** Milling is a machining process that involves removing material from a workpiece using a rotating cutting tool. During the milling process, the cutter is affected by a progressive degradation due to the grinding of the workpiece which results in a decline in product quality and a sharp increase in energy consumption and production costs. For these reasons, Tool Condition Monitoring has emerged as the essential approach in the machining industry. The application of Artificial Intelligence (AI) systems that incorporate the use of various Internet of Things sensors (IoT) to support the tool condition monitoring in the milling process has recently been a subject of great interest to researchers. It helps to achieve goals required in the modern manufacturing industries in terms of sustainability, cost reduction, and quality improvement. This review article focuses on the application of IoT sensors for recording acoustic emissions, to conduct tool condition monitoring of the milling cutting tools based on AI techniques. The discussion includes an analysis of the principal sensory systems and their main advantages and disadvantages for the milling process. Moreover, trends and problems of applied AI techniques for tool condition monitoring are highlighted.

**Introduction**

Milling process is a machining method in which a tool, called milling cutter, advances into a workpiece to remove materials. Due to friction between the cutting tool and the workpiece, degradation of the cutting tool occurs, resulting in tool surface wear, deformation, and even tool breakage [1]. Monitoring the tool condition is crucial to making timely decisions about replacing the tool before the failure condition, avoiding premature replacement, which reduces tool life and increases machine downtime, or delayed replacement, risking product quality and escalating production costs. In modern industry, Tool Condition Monitoring (TCM) systems can be constructed with the use of sensors that can do tasks such as "seeing, hearing, smelling, and touching" [2]. These sensors can be classified as direct or indirect [3]. The sensor is defined as direct if data are collected directly from the tool response to the contact with the workpiece, while it is defined as indirect if data are collected from a response external to the cutting interface. Examples of indirect TCM systems include cutting force, vibration, acoustic emission (AE), image, thermal, and other sensors [4]. Among them, AE exhibits high sensitivity to the sequence of events during tool contact, chip formation, and disengagement, as evidenced by the increasing trend in the averaged root mean squared (RMS) energy with flank wear growth [5-7]. Also, the variation of the AE frequency with various forms of tool wear was demonstrated [8].

The integration of AI techniques in the TCM is a hot research topic [9]. It allows to predict the state of wear of the tools and thus determine the best process parameters for increasing their useful life as well as for identifying the best time for replacement, also preventing damage to the processed materials. This can lead to huge savings in terms of materials consumed and therefore

<figure><img src="image_1889.png" /></figure>
production waste, as well as the reduction of resources (time, energy, materials, etc.) necessary to re-manufacture the damaged products.

Given the heightened interest in this topic, several literature reviews have emerged, offering insights into commonly employed sensor monitoring signals and diverse signal processing methods across machining processes like drilling, turning, and milling [10-14]. Still, the artificial intelligence techniques applied to detect faults and monitor equipment status in real-time are delved [15]. Furthermore, considerations regarding the estimation of the Remaining Useful Life (RUL) of the tool are explored in the reviewed literature [14].

The purpose of this review is to study and analyze the use of AE sensor systems, investigating their advantages and disadvantages, for TCM in the milling process. Special focus is placed on the different resolution approaches adopted for the TCM problem. A thorough literature search to identify relevant papers in the last ten years related to the topic of AI techniques for AE in the milling process is conducted on Scopus and Web of Science databases. The keywords used for the search are: "artificial intelligence", "machine learning", "deep learning", "milling process", "audio signal", "sound signal", "acoustic emission", "tool condition monitoring", "tool wear" and "remaining useful life". The outline of the survey goes as follows. In Section AE sensors in the Milling process the use of AE signals to monitor the tool conditions is explained, highlighting advantages and disadvantages. Section Resolution approach surveys the different ways in which the TCM problem can be addressed. In Section Artificial Intelligence models the popular decision-making algorithms used for monitoring and prediction are discussed. Section Multi-sensor approach reviews some works that have combined the AE signals with others, and the subsection Benchmark dataset is dedicated to surveying few papers that have used public datasets. Finally, Section Conclusion and Future Work provides recommendations for future work and gives the conclusion of the review paper.

## AE sensors in the Milling process

Milling is considered the most versatile machining process [16]. The input machine parameters provided are essential for describing the milling process and determining the quality and efficiency of the operation. The three parameters that have the greatest effect on the success of a machining operation are:

* Depth of Cut (p): it represents the thickness of material being removed in a single pass;

* Feed Rate (a): it determines how fast the milling cutter advances into the workpiece;

* Cutting Speed (V): it is the linear speed of the tool through the workpiece, based on the spindle speed and cutter diameter. A higher cutting speed can lead to more efficient material removal [17].

Analyzing the tool condition has a crucial role in the milling process to optimize the cutting parameters and replace the damaged tool at the appropriate moment. The modern industry requires an online method that continually observes tool conditions while the process continues. In the indirect online TCM, three phases must be executed, as is shown in Figure 1: acquisition of signal, extraction and selection of features from the acquired signal, and construction of a predictive model for the tool wear [18]. After the signal acquisition, the extraction and selection phases are critical for identifying features relevant to the tool conditions [19]. Different types of sensors have been used in the literature to assess the tool conditions. Among them, AE sensors have demonstrated their relationship with the increase in tool wear. A similar sensor is microphone for the acquisition of sound during the process that can provide sensitive detection of tool breakage [20]. The AE states the acquisition of ultrasonic signals in a material subjected to an external load. The tool breakage generates high-amplitude AE signals, making the AE method useful in constructing a warning system to prevent failure. Instead, the sound is generated by the contact
production waste, as well as the reduction of resources (time, energy, materials, etc.) necessary to re-manufacture the damaged products.

Given the heightened interest in this topic, several literature reviews have emerged, offering insights into commonly employed sensor monitoring signals and diverse signal processing methods across machining processes like drilling, turning, and milling [10-14]. Still, the artificial intelligence techniques applied to detect faults and monitor equipment status in real-time are delved [15]. Furthermore, considerations regarding the estimation of the Remaining Useful Life (RUL) of the tool are explored in the reviewed literature [14].

The purpose of this review is to study and analyze the use of AE sensor systems, investigating their advantages and disadvantages, for TCM in the milling process. Special focus is placed on the different resolution approaches adopted for the TCM problem. A thorough literature search to identify relevant papers in the last ten years related to the topic of AI techniques for AE in the milling process is conducted on Scopus and Web of Science databases. The keywords used for the search are: "artificial intelligence", "machine learning", "deep learning", "milling process", "audio signal", "sound signal", "acoustic emission", "tool condition monitoring", "tool wear" and "remaining useful life". The outline of the survey goes as follows. In Section AE sensors in the Milling process the use of AE signals to monitor the tool conditions is explained, highlighting advantages and disadvantages. Section Resolution approach surveys the different ways in which the TCM problem can be addressed. In Section Artificial Intelligence models the popular decision-making algorithms used for monitoring and prediction are discussed. Section Multi-sensor approach reviews some works that have combined the AE signals with others, and the subsection Benchmark dataset is dedicated to surveying few papers that have used public datasets. Finally, Section Conclusion and Future Work provides recommendations for future work and gives the conclusion of the review paper.

## AE sensors in the Milling process

Milling is considered the most versatile machining process [16]. The input machine parameters provided are essential for describing the milling process and determining the quality and efficiency of the operation. The three parameters that have the greatest effect on the success of a machining operation are:

* Depth of Cut (p): it represents the thickness of material being removed in a single pass;

* Feed Rate (a): it determines how fast the milling cutter advances into the workpiece;

* Cutting Speed (V): it is the linear speed of the tool through the workpiece, based on the spindle speed and cutter diameter. A higher cutting speed can lead to more efficient material removal [17].

Analyzing the tool condition has a crucial role in the milling process to optimize the cutting parameters and replace the damaged tool at the appropriate moment. The modern industry requires an online method that continually observes tool conditions while the process continues. In the indirect online TCM, three phases must be executed, as is shown in Figure 1: acquisition of signal, extraction and selection of features from the acquired signal, and construction of a predictive model for the tool wear [18]. After the signal acquisition, the extraction and selection phases are critical for identifying features relevant to the tool conditions [19]. Different types of sensors have been used in the literature to assess the tool conditions. Among them, AE sensors have demonstrated their relationship with the increase in tool wear. A similar sensor is microphone for the acquisition of sound during the process that can provide sensitive detection of tool breakage [20]. The AE states the acquisition of ultrasonic signals in a material subjected to an external load. The tool breakage generates high-amplitude AE signals, making the AE method useful in constructing a warning system to prevent failure. Instead, the sound is generated by the contact
between the tool that proceeds with a high speed on the workpiece. In milling, a discontinuous sound is provided, due to the consecutive entry and exit of the tool on the workpiece.

<figure><img src="image_1895.png" /><figcaption>Figure 1 Framework of TCM</figcaption></figure>

Advantages and disadvantages. An AE or sound method can reduce costs during manufacturing processes, increase tool life, and consequently improve the sustainability of the process. These types of sensors have demonstrated two advantages: measurement of the sound signal itself belongs to the non-contact measurement, data acquisition and measurement do not affect processing; formation of a sound signal measurement system is relatively simple and does not need to change the machine's structure and no need for more equipment [21]. However, the application of AE signal analysis to the milling process presents difficulties because of the magnitude of shock pulses during the entry and exit of each tooth from the workpiece, which may be equivalent to that generated during tooth fracture [22]. A practical method aimed at overcoming this difficulty is to utilize a multi-sensor approach, as discussed in the subsequent **Multi-sensor approach** Section. A disadvantage of both AE and sound sensors is that the capacity of identified changes in the tool wear is correlated to the position of these sensors in the machine. Kohuru et al. [23] acquired sound signals with the use of three microphones, positioned at different distances and angles from the cutting zone. They showed that the position of the microphone influences the prediction accuracy but by having more training data coming from different locations this positional error can be minimized. In addition, compared with an AE sensor, the reaction time of sound signals is very long, which makes the microphone less reliable.

## Resolution approach

Regression and classification are the most commonly used approaches to solve the problem of TCM. In the case of a regression problem, the outcome variable can assume numerical real value. For TCM it can be the value of wear registered or the RUL [24-33]. A particular version is proposed, considering the tool wear as a percentage: if the tool is healthy the percentage is about 100%, working the percentage is reduced [34-35]. Another version considers the tool wear as a value from 1 to 3, if the predicted value exceeds three the tool should be changing [36].

Considering the classification problem, AI models were implemented to predict the tool wear status, classified into a certain number of classes. More research studies considered only two classes: a Good tool and a Worn tool [37-43]. The cutting tool during the milling process is considered a Good tool until wear reaches a maximum threshold, beyond which the tool must be replaced and thus is labeled as Worn. Other studies consider three classes [44-54], associated with the typical tool wear pattern that can be divided into three stages: rapid initial wear stage, normal wear stage, and severe wear stage [55]. In the initial stage, the rapid wear results from the impact of the cutting tool on the workpiece. In the second stage, the wear assumes a uniform pattern whose duration depends strongly on the cutting rate. Finally, in the last stage, tool wear increases rapidly, indicating that the cutting tool has reached the end of its effective lifespan and must be replaced. Most research, instead, defines multiple classes (up to six) based on thresholds on the recorded wear value [23,56-63].
between the tool that proceeds with a high speed on the workpiece. In milling, a discontinuous sound is provided, due to the consecutive entry and exit of the tool on the workpiece.

<figure><img src="image_1890.png" /><figcaption>Figure 1 Framework of TCM</figcaption></figure>

Advantages and disadvantages. An AE or sound method can reduce costs during manufacturing processes, increase tool life, and consequently improve the sustainability of the process. These types of sensors have demonstrated two advantages: measurement of the sound signal itself belongs to the non-contact measurement, data acquisition and measurement do not affect processing; formation of a sound signal measurement system is relatively simple and does not need to change the machine's structure and no need for more equipment [21]. However, the application of AE signal analysis to the milling process presents difficulties because of the magnitude of shock pulses during the entry and exit of each tooth from the workpiece, which may be equivalent to that generated during tooth fracture [22]. A practical method aimed at overcoming this difficulty is to utilize a multi-sensor approach, as discussed in the subsequent **Multi-sensor approach** Section. A disadvantage of both AE and sound sensors is that the capacity of identified changes in the tool wear is correlated to the position of these sensors in the machine. Kohuru et al. [23] acquired sound signals with the use of three microphones, positioned at different distances and angles from the cutting zone. They showed that the position of the microphone influences the prediction accuracy but by having more training data coming from different locations this positional error can be minimized. In addition, compared with an AE sensor, the reaction time of sound signals is very long, which makes the microphone less reliable.

## Resolution approach

Regression and classification are the most commonly used approaches to solve the problem of TCM. In the case of a regression problem, the outcome variable can assume numerical real value. For TCM it can be the value of wear registered or the RUL [24-33]. A particular version is proposed, considering the tool wear as a percentage: if the tool is healthy the percentage is about 100%, working the percentage is reduced [34-35]. Another version considers the tool wear as a value from 1 to 3, if the predicted value exceeds three the tool should be changing [36].

Considering the classification problem, AI models were implemented to predict the tool wear status, classified into a certain number of classes. More research studies considered only two classes: a Good tool and a Worn tool [37-43]. The cutting tool during the milling process is considered a Good tool until wear reaches a maximum threshold, beyond which the tool must be replaced and thus is labeled as Worn. Other studies consider three classes [44-54], associated with the typical tool wear pattern that can be divided into three stages: rapid initial wear stage, normal wear stage, and severe wear stage [55]. In the initial stage, the rapid wear results from the impact of the cutting tool on the workpiece. In the second stage, the wear assumes a uniform pattern whose duration depends strongly on the cutting rate. Finally, in the last stage, tool wear increases rapidly, indicating that the cutting tool has reached the end of its effective lifespan and must be replaced. Most research, instead, defines multiple classes (up to six) based on thresholds on the recorded wear value [23,56-63].
## Artificial Intelligence models

The use of AI in industrial processes has seen rapid growth over the past two decades and has become a major contributor to Industry 4.0 [64]. AI combined with the use of sensors can help, among other things, the achievement of sustainable industry by reducing waste and energy consumption in production processes with the introduction of technological solutions. AI application known as machine learning (ML) enables machines to learn from data without explicit programming. Many ML methods can be applied, and the correct selection is more important to achieve better performance. From the prominent methods, there are:

* Support Vector Machines (SVMs);

* Decision Trees (DTs);

* K-Nearest Neighbors (KNN);

* Artificial Neural Networks (ANN).

Support Vector Machines are supervised learning models with the primary goal of identifying a hyperplane that classifies the data points, maximizing the margin (i.e. the maximum distance between data points of different classes). In regression problems, it is called Support Vector Regressor (SVR). Kothuru et al. obtained good performance by applying an SVM model with input data features generated from the frequency domains of sound signals, acquired with the use of three microphones positioned in three different places [23,44,59]. In a comparative study, the SVM model outperformed the deep learning model of Convolutional Neural Networks (CNN) [60]. Li et al. [56] compared four different models (i.e. Classification and regression tree (CART), Random Forest, KNN, and SVM), concluding that SVM gave better performance. Krishnakumar et al. [46] tested the SVM model with different kernel functions to solve the classification problem, obtaining better performance with the Radial Basis Function (RBF).

Decision Trees are non-parametric supervised learning models that generate a tree with decision nodes, two or more branches from the decision nodes representative of the value of the attributes, and leaf nodes representative of a decision in the target. Twardowski et al. [38] compared three ML techniques (CART, multi-layer perceptron (MLP), and KNN) to solve the binary tool wear prediction problem, obtaining better performance with the CART. Other studies have demonstrated the efficiency of using DT model to solve the classification problem of tool conditions [45,62].

K-Nearest Neighbors is a popular algorithm used for both regression and classification problems, based on the nearest k neighbors. The principle behind the algorithm is that similar data points tend to have similar values. Yuan et al. [39] trained a KNN to predict the tool breakage conditions. The results showed good performance of the model (with an accuracy of 96.21%), demonstrating that the proposed method can effectively identify damaged cutters in milling operations.

Artificial Neural Networks are a series of algorithms that identify relationships between data points of datasets, inspired by the human brain. Their architecture comprises three layers: input, hidden, and output. In each layer, several neurons are present. The communication between nodes of different layers is permitted by patterns, characterized by weights that excite the signals during the communication. The output layer is characterized by one neuron in the case of binary classification or regression, and more neurons in the case of multi-class classification (one for each class). CNNs are the most widely used type of ANNs in TCM. They are a type of deep learning model used to process data with a grid pattern, such as images. Some studies convert sound signals into images, training CNNs for classification, showing high efficacy in detecting worn tools [37,47,63]. Khan et al [54] solve the classification problem with a Long Short-Term Memory Network (LSTM), achieving a prediction accuracy of over 98%. Zhou et al. [25] introduced the Two-Layer Angle Kernel Extreme Learning Machine (TAKELM) for tool wear regression. This
## Artificial Intelligence models

The use of AI in industrial processes has seen rapid growth over the past two decades and has become a major contributor to Industry 4.0 [64]. AI combined with the use of sensors can help, among other things, the achievement of sustainable industry by reducing waste and energy consumption in production processes with the introduction of technological solutions. AI application known as machine learning (ML) enables machines to learn from data without explicit programming. Many ML methods can be applied, and the correct selection is more important to achieve better performance. From the prominent methods, there are:

* Support Vector Machines (SVMs);

* Decision Trees (DTs);

* K-Nearest Neighbors (KNN);

* Artificial Neural Networks (ANN).

Support Vector Machines are supervised learning models with the primary goal of identifying a hyperplane that classifies the data points, maximizing the margin (i.e. the maximum distance between data points of different classes). In regression problems, it is called Support Vector Regressor (SVR). Kothuru et al. obtained good performance by applying an SVM model with input data features generated from the frequency domains of sound signals, acquired with the use of three microphones positioned in three different places [23,44,59]. In a comparative study, the SVM model outperformed the deep learning model of Convolutional Neural Networks (CNN) [60]. Li et al. [56] compared four different models (i.e. Classification and regression tree (CART), Random Forest, KNN, and SVM), concluding that SVM gave better performance. Krishnakumar et al. [46] tested the SVM model with different kernel functions to solve the classification problem, obtaining better performance with the Radial Basis Function (RBF).

Decision Trees are non-parametric supervised learning models that generate a tree with decision nodes, two or more branches from the decision nodes representative of the value of the attributes, and leaf nodes representative of a decision in the target. Twardowski et al. [38] compared three ML techniques (CART, multi-layer perceptron (MLP), and KNN) to solve the binary tool wear prediction problem, obtaining better performance with the CART. Other studies have demonstrated the efficiency of using DT model to solve the classification problem of tool conditions [45,62].

K-Nearest Neighbors is a popular algorithm used for both regression and classification problems, based on the nearest k neighbors. The principle behind the algorithm is that similar data points tend to have similar values. Yuan et al. [39] trained a KNN to predict the tool breakage conditions. The results showed good performance of the model (with an accuracy of 96.21%), demonstrating that the proposed method can effectively identify damaged cutters in milling operations.

Artificial Neural Networks are a series of algorithms that identify relationships between data points of datasets, inspired by the human brain. Their architecture comprises three layers: input, hidden, and output. In each layer, several neurons are present. The communication between nodes of different layers is permitted by patterns, characterized by weights that excite the signals during the communication. The output layer is characterized by one neuron in the case of binary classification or regression, and more neurons in the case of multi-class classification (one for each class). CNNs are the most widely used type of ANNs in TCM. They are a type of deep learning model used to process data with a grid pattern, such as images. Some studies convert sound signals into images, training CNNs for classification, showing high efficacy in detecting worn tools [37,47,63]. Khan et al [54] solve the classification problem with a Long Short-Term Memory Network (LSTM), achieving a prediction accuracy of over 98%. Zhou et al. [25] introduced the Two-Layer Angle Kernel Extreme Learning Machine (TAKELM) for tool wear regression. This
algorithm, implemented in a single hidden-layer feedforward neural network, sets random values for weights and biases, keeping them fixed during training. By utilizing two-angle kernel functions without hyperparameters, TAKELM simplifies the model and outperforms existing methods, showcasing remarkable predictive accuracy with minimal error compared to state-of-the-art approaches in tool wear prediction.

## Multi-sensor approach

To overcome the limitations of the use of AE or sound signals, the multi-sensor fusion method has become a hot research direction of TCM [65]. Combining various types of signals can improve the robustness of the method and reduce the sensitivity to noise and transient faults associated with using audio signals alone. A combination of vibration and sound signals (or AE signals) was adopted in more research work [34,35,43,57,61]. Vibrations can be acquired with the use of piezoelectric sensors and occur in the milling process due to the interaction between the material surface and the tool at particular spindle rotation frequencies. They are detrimental to tool life and workpiece surface finish. Zhu et al. [58] combined the use of all three types of signals (vibration, sound, and AE) to predict the tool conditions, fusing all three in a 2-D image with the angular matrix imaging method. The results demonstrated that vibration signals incorporated with sound signals outperform other combinations of signals to solve the tool wear classification problem. Yan et al. [29] combined the three types of signals, constructing a condition-adaptive hidden semi-Markov model for evaluating the wear condition and estimating RUL. Some studies demonstrated, instead, that the use of the only vibration sensor against the sound sensor allows to reached better performance for both regression and classification problems [24,48]. Cutting force signals, highly correlated with tool wear, offer another avenue for signal combination [36]. An increase in tool wear determines an increase in cutting force during milling operations, without changing the machine parameters. Dynamometer can be used to acquire cutting force signals, requiring a relatively simple installation in the machining process. A big disadvantage of using this type of signal is the need to reduce the noise content. Some research work used the current signal in combination with vibration and sound signals [26,41]. Letford et al. [27] combined the use of the four sensors (vibration, AE, cutting force, and spindle motor current) to solve the regression problem of tool wear prediction. Good performance was achieved by applying an artificial neural network model.

Other combinations are carried out in various research work. Sun et al. [49] conducted various experiments to determine optimal sensors and feature combinations for monitoring tool wear. Accelerometers, microphones, current transformers, and AE sensors are considered in the experiments and 29 features are selected from the time and frequency domain of the signals and the machine parameters. Results demonstrated that the use of all sensors, if not appropriate for the examined problem, can achieve poor performance. In addition, the use of only one type of sensor is preferred because it allows a considerable reduction in associated costs. Other studies used the axial load signals, combined with sound and spindle power signals, to solve the three-classes classification problem [50-51]. Chen et al. [53] used an online tool image acquisition method combined with the collection of AE signals to monitor the milling process. The images collected were processed to obtain the tool wear while AE features were used to train four ML models, achieving a recognition accuracy of 96.11%. McLeay et al. [40] developed a fault detection system for predicting tool replacement, utilizing an unsupervised learning model based on multiple sensors (AE, two microphones, two accelerometers, and a spindle power sensor).

Benchmark dataset. Two are the public benchmark datasets for the tool condition problem of a milling process: NASA and PHM 2010. These datasets are intended to check the accuracy of the RUL prediction models. More research work solved the problems using different approaches, where tool wear prediction or tool wear condition classification has been proposed. NASA dataset [66] was provided by the UC Berkeley Emergent Space Tensegrities (BEST) Lab. Experiments