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journal article·2024-07

Prediction of Surface Roughness in Hybrid Magnetorheological Finishing of Silicon Using Machine Learning

Mayank Srivastava, Gurminder Singh, Kshitij Verma, Pulak M. Pandey, Prashant Singh Rana, Munish Kumar Gupta, Navneet Khanna

Silicon· Volume 16· Issue 10
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OpenAlex topics: Advanced Surface Polishing Techniques · Advanced machining processes and optimization · Advanced Machining and Optimization Techniques

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References (46)

Show source-provided references
1. Daud ND, Hasan MN, Saleh T et al (2022) Non-traditional machining techniques for silicon wafers. Int J Adv Manuf Technol 121:29–5710.1007/s00170-022-09365-z ↗
2. Pandey K, Sharma A, Singh AK (1940) Silicon Wafers; Its Manufacturing Processes and Finishing Techniques: an Overview. https://doi.org/10.1007/s12633-022-01940-z/Published10.1007/s12633-022-01940-z/Published ↗
3. Jarin S, Saleh T, Muthalif AGA et al (2018) Towards achieving nanofinish on silicon (Si) wafer by μ-wire electro-discharge machining. Int J Adv Manuf Technol 99:3005–3015. https://doi.org/10.1007/s00170-018-2692-410.1007/s00170-018-2692-4 ↗
4. Ghosh G, Sidpara A, Bandyopadhyay PP (2021) Experimental and theoretical investigation into surface roughness and residual stress in magnetorheological finishing of OFHC copper. J Mater Process Technol 288. https://doi.org/10.1016/j.jmatprotec.2020.11689910.1016/j.jmatprotec.2020.116899 ↗
5. Singh J, Singh G, Pandey PM (2020) Electric discharge machining using rapid manufactured complex shape copper electrode: Parametric analysis and process optimization for material removal rate, electrode wear rate and cavity dimensions. Proc Inst Mech Eng C J Mech Eng Sci 234:2459–2473. https://doi.org/10.1177/095440622090644510.1177/0954406220906445 ↗
6. Singh G, Singh S, Prakash C, Ramakrishna S (2021) On investigating the soda-lime shot blasting of AZ31 alloy: Effects on surface roughness, material removal rate, corrosion resistance, and bioactivity. J Magnes Alloys 9:1272–1284. https://doi.org/10.1016/j.jma.2020.11.01710.1016/j.jma.2020.11.017 ↗
7. Gorana VK, Jain VK (2006) Lal GK (2006) Prediction of surface roughness during abrasive flow machining. Int J Adv Manuf Technol 31(3):258–267. https://doi.org/10.1007/S00170-005-0197-410.1007/S00170-005-0197-4 ↗
8. Du KJ, Choi MS (1995) Simulation for the prediction of surface-accuracy in magnetic abrasive machining. J Mater Process Tech 53:630–642. https://doi.org/10.1016/0924-0136(94)01753-N10.1016/0924-0136(94)01753-N ↗
9. Wani AM, Yadava V, Khatri A (2007) Simulation for the prediction of surface roughness in magnetic abrasive flow finishing (MAFF). J Mater Process Technol 190:282–290. https://doi.org/10.1016/j.jmatprotec.2007.02.03610.1016/j.jmatprotec.2007.02.036 ↗
10. Jain RK, Jain VK, Dixit PM (1999) Modeling of material removal and surface roughness in abrasive flow machining process. Int J Mach Tools Manuf 39:1903–1923. https://doi.org/10.1016/S0890-6955(99)00038-310.1016/S0890-6955(99)00038-3 ↗
11. Uhlmann E, Dethlefs A, Eulitz A (2014) Investigation into a geometry-based model for surface roughness prediction in vibratory finishing processes. Int J Adv Manuf Technol 75:815–823. https://doi.org/10.1007/s00170-014-6194-810.1007/s00170-014-6194-8 ↗
12. Ranjan P, Balasubramaniam R, Jain VK (2017) Analysis of magnetorheological fluid behavior in chemo-mechanical magnetorheological finishing (CMMRF) process. Precis Eng 49:122–135. https://doi.org/10.1016/j.precisioneng.2017.02.00110.1016/j.precisioneng.2017.02.001 ↗
13. Misra A, Pandey PM, Dixit US (2017) Modeling and simulation of surface roughness in ultrasonic assisted magnetic abrasive finishing process. Int J Mech Sci 133:344–356. https://doi.org/10.1016/j.ijmecsci.2017.08.05610.1016/j.ijmecsci.2017.08.056 ↗
14. Oh JH, Lee SH (2011) Prediction of surface roughness in magnetic abrasive finishing using acoustic emission and force sensor data fusiong. Proc Inst Mech Eng B J Eng Manuf 225:853–865. https://doi.org/10.1177/09544054JEM205510.1177/09544054JEM2055 ↗
15. Erzurumlu T, Oktem H (2007) Comparison of response surface model with neural network in determining the surface quality of moulded parts. Mater Des 28:459–465. https://doi.org/10.1016/J.MATDES.2005.09.00410.1016/J.MATDES.2005.09.004 ↗
16. Çaydaş U, Hasçalik A (2008) A study on surface roughness in abrasive waterjet machining process using artificial neural networks and regression analysis method. J Mater Process Technol 202:574–582. https://doi.org/10.1016/J.JMATPROTEC.2007.10.02410.1016/J.JMATPROTEC.2007.10.024 ↗
17. Preston W (1927) The theory and design of plate glass polishing machines. J Soc Glass 11:214–256
18. Buisara VH, Ahn Y, Chandrasekar S, Farris TN (1997) Polishing and lapping temperatures. J Tribol 119:163–170. https://doi.org/10.1115/1.283245310.1115/1.2832453 ↗
19. Luo J, Dornfeld DA (2001) Material removal mechanism in chemical mechanical polishing: Theory and modeling. IEEE Trans Semicond Manuf 14:112–133. https://doi.org/10.1109/66.92072310.1109/66.920723 ↗
20. Wang P, Gao RX, Yan R (2017) A deep learning-based approach to material removal rate prediction in polishing. CIRP Ann Manuf Technol 66:429–432. https://doi.org/10.1016/J.CIRP.2017.04.01310.1016/J.CIRP.2017.04.013 ↗
21. Deng J, Zhang Q, Lu J et al (2021) Prediction of the surface roughness and material removal rate in chemical mechanical polishing of single-crystal SiC via a back-propagation neural network. Precis Eng 72:102–110. https://doi.org/10.1016/j.precisioneng.2021.04.01210.1016/j.precisioneng.2021.04.012 ↗
22. Segreto T, Teti R (2019) Machine learning for in-process end-point detection in robot-assisted polishing using multiple sensor monitoring. Int J Adv Manuf Technol 103:4173–4187. https://doi.org/10.1007/s00170-019-03851-710.1007/s00170-019-03851-7 ↗
23. Chaudhari R, Ayesta I, Doshi M et al (2022) Effect of multi-walled carbon nanotubes on the performance evaluation of nickel-based super-alloy–Udimet 720 machined using WEDM process. Int J Adv Manuf Technol 123:2087–2105. https://doi.org/10.1007/s00170-022-10209-z10.1007/s00170-022-10209-z ↗
24. Chaudhari R, Ayesta I, Doshi M et al (2022) Implementation of Passing Vehicle Search Algorithm for Optimization of WEDM Process of Nickel-Based Superalloy Waspaloy. Nanomaterials 12. https://doi.org/10.3390/nano1224439410.3390/nano12244394 ↗
25. Chen T, Guestrin C (2016) XGBoost: A scalable tree boosting system. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, New York, NY, USA, pp 785–79410.1145/2939672.2939785 ↗
26. Solomatine DP, Shrestha DL (2004) AdaBoost.RT: A boosting algorithm for regression problems. In: IEEE International Conference on Neural Networks - Conference Proceedings. pp 1163–1168
27. Ma H, Yang X, Mao J, Zheng H (2018) The Energy Efficiency Prediction Method Based on Gradient Boosting Regression Tree. In: 2nd IEEE Conference on Energy Internet and Energy System Integration, EI2 2018 - Proceedings. pp 1–910.1109/EI2.2018.8581904 ↗
28. Kim S, Jeong M, Ko BC (2021) Self-Supervised Keypoint Detection Based on Multi-Layer Random Forest Regressor. IEEE Access 9:40850–40859. https://doi.org/10.1109/ACCESS.2021.306502210.1109/ACCESS.2021.3065022 ↗
29. Wang Y, Li Y, Pu W et al (2016) Random bits forest: A strong classifier/regressor for big data. Sci Rep 6:30086. https://doi.org/10.1038/srep3008610.1038/srep30086 ↗
30. Grömping U (2009) Variable importance assessment in regression: Linear regression versus random forest. Am Stat 63:308–319. https://doi.org/10.1198/tast.2009.0819910.1198/tast.2009.08199 ↗
31. Naseem I, Togneri R, Bennamoun M (2010) Linear regression for face recognition. IEEE Trans Pattern Anal Mach Intell 32:2106–2112. https://doi.org/10.1109/TPAMI.2010.12810.1109/TPAMI.2010.128 ↗
32. Hancock JT, Khoshgoftaar TM (2020) CatBoost for big data: an interdisciplinary review. J Big Data 7:94. https://doi.org/10.1186/s40537-020-00369-810.1186/s40537-020-00369-8 ↗
33. Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A (2017) CatBoost: Unbiased boosting with categorical features. https://doi.org/10.48550/arXiv.1706.0951610.48550/arXiv.1706.09516 ↗
34. Jurkovic Z, Cukor G, Brezocnik M, Brajkovic T (2018) A comparison of machine learning methods for cutting parameters prediction in high speed turning process. J Intell Manuf 29:1683–1693. https://doi.org/10.1007/s10845-016-1206-110.1007/s10845-016-1206-1 ↗
35. Kim DH, Kim TJY, Wang X et al (2018) Smart Machining Process Using Machine Learning: A Review and Perspective on Machining Industry. Int J Precis Eng Manuf Green Technol 5:555–56810.1007/s40684-018-0057-y ↗
36. Wuest T, Weimer D, Irgens C, Thoben K-D (2016) Machine learning in manufacturing: advantages, challenges, and applications. Prod Manuf Res 4:23–45. https://doi.org/10.1080/21693277.2016.119251710.1080/21693277.2016.1192517 ↗
37. Kubo A, Teti R, Ullah AS et al (2021) Determining surface topography of a dressed grinding wheel using bio-inspired DNA-based computing. Materials 14:1899. https://doi.org/10.3390/ma1408189910.3390/ma14081899 ↗
38. Srivastava M, Singh G, Pandey PM (2023) Ultrasonic vibration assisted double disc chemo-magnetorheological finishing of silicon wafer: Experimental investigations and optimization for surface roughness. Proc Inst Mech Eng B J Eng Manuf. https://doi.org/10.1177/0954405423117895210.1177/09544054231178952 ↗
39. Srivastava M, Pandey PM, Kuldeep et al (2021) Synthesis and characterization of the rheological behavior of MR fluid for polishing silicon wafer using double-disc chemical-assisted magneto-rheological finishing process. J Magn Magn Mater 534. https://doi.org/10.1016/j.jmmm.2021.16804410.1016/j.jmmm.2021.168044 ↗
40. Alagumurthi N, Palaniradja K, Soundararajan V (2006) Optimization of grinding process through Design of Experiment (DOE) - A comparative study. Mater Manuf Processes 21:19–21. https://doi.org/10.1080/AMP-20006060510.1080/AMP-200060605 ↗
41. Singh N, Singh G (2021) Advances in polymers for bio-additive manufacturing: A state of art review. J Manuf Process 72:439–457. https://doi.org/10.1016/j.jmapro.2021.10.04510.1016/j.jmapro.2021.10.045 ↗
42. Srivastava M, Pandey PM (2021) The influence of ultrasonic vibrations on material removal in the silicon wafer polishing using DDCAMRF: Experimental investigations and process optimization. Proc Inst Mech Eng C J Mech Eng Sci. https://doi.org/10.1177/0954406221103897910.1177/09544062211038979 ↗
43. Kwak JS (2005) Application of Taguchi and response surface methodologies for geometric error in surface grinding process. Int J Mach Tools Manuf 45:327–334. https://doi.org/10.1016/j.ijmachtools.2004.08.00710.1016/j.ijmachtools.2004.08.007 ↗
44. Saravanan R, Sachithanandam M (2001) Genetic algorithm (GA) for multivariable surface grinding process optimisation using a multi-objective function model. Int J Adv Manuf Technol 17:330–338. https://doi.org/10.1007/s00170017016710.1007/s001700170167 ↗
45. Singh G, Pandey PM (2020) Rapid manufacturing of copper-graphene composites using a novel rapid tooling technique. Rapid Prototyp J 26:765–776. https://doi.org/10.1108/RPJ-10-2019-025810.1108/RPJ-10-2019-0258 ↗
46. Singh N, Singh G, Farina I, Colangelo F (2023) Experimental-theoretical comparative analysis of PLA-based 3D lattice. J Thermoplast Compos Mater 36:4826–4840. https://doi.org/10.1177/0892705723116573210.1177/08927057231165732 ↗