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A machine learning model for flank wear prediction in face milling of Inconel 718

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Abstract

Optimization of flank wear width (VB) progression during face milling of Inconel 718 is challenging due to the synergistic effect of cutting parameters on the complex wear mechanisms and failure modes. The lack of quantitative understanding between VB and the cutting conditions limits the development of the tool life extension. In this study, a Gaussian kernel ridge regression was employed to develop the VB progression model for face milling of Inconel 718 using multi-layer physical vapor deposition-TiAlN/NbN-coated carbide inserts with the input feature of cutting speed, feed rate, axial depth of cut, and cutting length. The model showed a root mean square error of 30.9 (49.7) μm and R2 of 0.93 (0.81) in full fit (5-fold cross-validation test). The statistics along with the cross-plot analyses suggested that the model had a high predictive ability. A new promising condition at the cutting speed of 40 m/min, feed rate of 0.08 mm/tooth, and axial depth of cut of 0.9 mm was designed and experimentally validated. The measured and predicted VB agreed well with each other. This model is thus applicable for VB prediction and optimization in the real face milling operation of Inconel 718.

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The data used in this manuscript is available from the corresponding author and can be accessed on reasonable request.

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References

  1. Arunachalam RM, Mannan MA, Spowage AC (2004) Surface integrity when machining age hardened Inconel 718 with coated carbide cutting tools. Int J Mach Tools Manuf 44(14):1481–1491. https://doi.org/10.1016/j.ijmachtools.2004.05.005

    Article  Google Scholar 

  2. Dudzinski D, Devillez A, Moufki A, Larrouquère D, Zerrouki V, Vigneau J (2004) A review of developments towards dry and high speed machining of Inconel 718 alloy. Int J Mach Tools Manuf 44(4):439–456. https://doi.org/10.1016/S0890-6955(03)00159-7

    Article  Google Scholar 

  3. Chan CH et al (2017) Analysis of face milling performance on Inconel 718 using FEM and historical data of RSM. IOP Conf Ser Mater Sci Eng 270(1). https://doi.org/10.1088/1757-899X/270/1/012038

  4. Houghton Q (n.d.) Quaker Houghton - industrial chemicals, process fluids & lubricants. https://home.quakerhoughton.com/?utm_source=quakerchem&utm_medium=Legacy&utm_campaign=Decommissioning&utm_term= Accessed 02 Apr 2022

  5. Polvorosa R, Suárez A, López de Lacalle LN, Cerrillo I, Wretland A, Veiga F (2017) Tool wear on nickel alloys with different coolant pressures: comparison of Alloy 718 and Waspaloy. J Manuf Process 26:44–56. https://doi.org/10.1016/j.jmapro.2017.01.012

    Article  Google Scholar 

  6. Akhtar W, Sun J, Sun P, Chen W, Saleem Z (2014) Tool wear mechanisms in the machining of Nickel based super-alloys: a review. Front Mech Eng 9(2):106–119. https://doi.org/10.1007/s11465-014-0301-2

    Article  Google Scholar 

  7. Kamdani et al (2019) Study on tool wear and wear mechanism of end milling Nickel-based alloy. Jurnal Tribologi 2019(21):82–92

    Google Scholar 

  8. Banda T, Ho KY, Akhavan Farid A, Lim CS (2021) Characterization of tool wear mechanisms and failure modes of TiAlN-NbN coated carbide inserts in face milling of Inconel 718. J Mater Eng Perform. https://doi.org/10.1007/s11665-021-06301-2

    Article  Google Scholar 

  9. Anderson M, Patwa R, Shin YC (2006) Laser-assisted machining of Inconel 718 with an economic analysis. Int J Mach Tools Manuf 46(14):1879–1891. https://doi.org/10.1016/j.ijmachtools.2005.11.005

    Article  Google Scholar 

  10. Huang W et al (2021) Tool wear in ultrasonic vibration–assisted drilling of CFRP: a comparison with conventional drilling. Int J Adv Manuf Technol 1809–1820. https://doi.org/10.1007/s00170-021-07198-w

  11. Ezugwu EO, Wang ZM, Machado AR (2000) Wear of coated carbide tools when machining nickel (Inconel 718) and titanium base (Ti-6A1-4V) alloys. Tribol Trans 43(2):263–268. https://doi.org/10.1080/10402000008982338

    Article  Google Scholar 

  12. Liu Y, Yu S, Shi Q, Ge X, Wang W (2022) Multilayer coatings for tribology: a mini review. Nanomaterials 12:1388. https://doi.org/10.3390/nano12091388

    Article  Google Scholar 

  13. Jawaid A, Koksal S, Sharif S (2001) Cutting performance and wear characteristics of PVD coated and uncoated carbide tools in face milling Inconel 718 aerospace alloy. J Mater Process Technol 116(1):2–9. https://doi.org/10.1016/S0924-0136(01)00850-0

    Article  Google Scholar 

  14. Suresh R, Basavarajappa S, Gaitonde VN (2015) Experimental studies on the performance of multilayer coated carbide tool in hard turning of high strength low alloy steel. J Mater Res 30(20):3056–3064. https://doi.org/10.1557/jmr.2015.236

    Article  Google Scholar 

  15. Anthony Xavior M, Manohar M, Madhukar PM, Jeyapandiarajan P (2017) Experimental investigation of work hardening, residual stress and microstructure during machining Inconel 718. J Mech Sci Technol 31(10):4789–4794. https://doi.org/10.1007/s12206-017-0926-2

    Article  Google Scholar 

  16. Klocke F, Lung D, Cordes SE, Gerschwiler K (2008) Performance of PVD-coatings on cutting tools for machining Inconel 718, austenitic steel and quenched and tempered steel. Proceedings of the 7th International Conference THE Coatings in Manufacturing, no. October

  17. Kosaraju S, Vijay Kumar M, Sateesh N (2018) Optimization of machining parameter in turning Inconel 625. Mater Today: Proceedings 5(2): Part 1, 5343–5348. https://doi.org/10.1016/j.matpr.2017.12.119

  18. Guo J, Li A, Zhang R (2020) Tool condition monitoring in milling process using multifractal detrended fluctuation analysis and support vector machine. Int J Adv Manuf Technol 110(5):1445–1456. https://doi.org/10.1007/s00170-020-05931-5

    Article  Google Scholar 

  19. Banda T, Farid AA, Li C, Jauw VL, Lim CS (2022) Application of machine vision for tool condition monitoring and tool performance optimization–a review. Int J Adv Manuf Technol 121(11):7057–7086. https://doi.org/10.1007/s00170-022-09696-x

    Article  Google Scholar 

  20. Gao D, Liao Z, Lv Z, Lu Y (2015) Multi-scale statistical signal processing of cutting force in cutting tool condition monitoring. Int J Adv Manuf Technol 80(9–12):1843–1853. https://doi.org/10.1007/s00170-015-7116-0

    Article  Google Scholar 

  21. Thakre AA, Lad AV, Mala K (2019) Measurements of tool wear parameters using machine vision system. Model Simul Eng 2019:1–10. https://doi.org/10.1155/2019/1876489

    Article  Google Scholar 

  22. Salimiasl A, Özdemir A (2016) Analyzing the performance of artificial neural network (ANN)-, fuzzy logic (FL)-, and least square (LS)-based models for online tool condition monitoring. Int J Adv Manuf Technol 87(1–4):1145–1158. https://doi.org/10.1007/s00170-016-8548-x

    Article  Google Scholar 

  23. Morgan D, Jacobs R (2020) Opportunities and challenges for machine learning in materials science. Annu Rev Mater Res 50:71–103. https://doi.org/10.1146/annurev-matsci-070218-010015

    Article  Google Scholar 

  24. Link P et al (2022) Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing. J Intell Manuf 2022:1–14. https://doi.org/10.1007/S10845-022-01975-4

    Article  Google Scholar 

  25. Liu YC, Afflerbach B, Jacobs R, Lin SK, Morgan D (2019) Exploring effective charge in electromigration using machine learning. MRS Commun 9(2):567–575. https://doi.org/10.1557/mrc.2019.63

    Article  Google Scholar 

  26. Liu YC, Liu TY, Huang TH, Chiu KC, Lin SK (2021) Exploring dielectric constant and dissipation factor of ltcc using machine learning. Materials 14(19):1–14. https://doi.org/10.3390/ma14195784

    Article  Google Scholar 

  27. Liu Y-c, Wu H, Mayeshiba T et al (2022) Machine learning predictions of irradiation embrittlement in reactor pressure vessel steels. NPJ Comput Mater 8:85. https://doi.org/10.1038/s41524-022-00760-4

  28. Wu X, Liu Y, Zhou X, Mou A (2019) Automatic identification of tool wear based on convolutional neural network in face milling process. Sensors (Switzerland) 19(18). https://doi.org/10.3390/s19183817

  29. Kaya B, Oysu C, Ertunc HM (2011) Advances in Engineering Software Force-torque based on-line tool wear estimation system for CNC milling of Inconel 718 using neural networks. Adv Eng Softw 42(3):76–84. https://doi.org/10.1016/j.advengsoft.2010.12.002

    Article  Google Scholar 

  30. Nath C, Brooks Z, Kurfess TR (2015) Machinability study and process optimization in face milling of some super alloys with indexable copy face mill inserts. J Manuf Process 20:88–97. https://doi.org/10.1016/j.jmapro.2015.09.006

    Article  Google Scholar 

  31. Banda T, Lestari V, Chuan J, Ali L, Farid A, Seong C (2022) Flank wear prediction using spatial binary properties and artificial neural network in face milling of Inconel 718. Int J Adv Manuf Technol 0123456789. https://doi.org/10.1007/s00170-022-09039-w

  32. Jacobs R et al (2020) The Materials Simulation Toolkit for Machine learning (MAST-ML): an automated open source toolkit to accelerate data-driven materials research. Comput Mater Sci 176:2019. https://doi.org/10.1016/j.commatsci.2020.109544

    Article  Google Scholar 

  33. Pedregosa F et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12(2014):2825–2830

    MathSciNet  MATH  Google Scholar 

  34. De Melo ACA, Milan JCG, Da Silva MB, Machado ÁR (2006) Some observations on wear and damages in cemented carbide tools. J Braz Soc Mech Sci Eng 28(3):269–277. https://doi.org/10.1590/s1678-58782006000300004

    Article  Google Scholar 

  35. Bilgin MB (2015) Investigating the effects of cutting parameters on the built-up-layer and built-up-edge formation during the machining of AISI 310 austenitic stainless Steels. Materiali Tehnologije 49(5):779–784. https://doi.org/10.17222/mit.2014.253

    Article  Google Scholar 

  36. Kakaš D et al (2009) Influence of load and sliding speed on friction coefficient of IBAD deposited TiN. Tribol Ind 31(3–4):3–10

    Google Scholar 

Download references

Funding

This work was supported by the National Science and Technology Council (NSTC) (110–2222-E-006–008, 111–2222-E-006–011-MY3, and 111–2622-8–006-029) and from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) and NSTC (111–2634-F-006–008) in Taiwan.

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Authors and Affiliations

  1. Department of Mechanical, Materials and Manufacturing Engineering, University of Nottingham Malaysia, Semenyih, Malaysia

    Tiyamike Banda, Ali Akhavan Farid & Chin Seong Lim

  2. Department of Mechanical Engineering, National Cheng Kung University, Tainan, 70101, Taiwan

    Yu-chen Liu

  3. Hierarchical Green-Energy Materials (Hi-GEM) Research Center, National Cheng Kung University, Tainan, 70101, Taiwan

    Yu-chen Liu

  4. Program On Smart and Sustainable Manufacturing, Academy of Innovative Semiconductor and Sustainable Manufacturing, National Cheng Kung University, Tainan, 70101, Taiwan

    Yu-chen Liu

  5. Program On Semiconductor Packaging and Testing, Academy of Innovative Semiconductor and Sustainable Manufacturing, National Cheng Kung University, Tainan, 70101, Taiwan

    Yu-chen Liu

  6. Core Facility Center, National Cheng Kung University, Tainan, 70101, Taiwan

    Yu-chen Liu

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  1. Tiyamike Banda
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  2. Yu-chen Liu
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  3. Ali Akhavan Farid
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  4. Chin Seong Lim
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Contributions

All authors contributed to the conceptual idea of the manuscript. The first draft of the manuscript was written by Mr. Tiyamike Banda and Dr Yu-chen Liu. All authors commented on the previous versions. Dr. Yu-chen Liu, Dr. Ali Akhavan Farid, and Dr. Chin Seong Lim supervised, reviewed, and edited the manuscript. All authors read and finally approved the final version of the manuscript.

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Correspondence to Yu-chen Liu.

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Banda, T., Liu, Yc., Farid, A.A. et al. A machine learning model for flank wear prediction in face milling of Inconel 718. Int J Adv Manuf Technol 126, 935–945 (2023). https://doi.org/10.1007/s00170-023-11152-3

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