• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2024, Volume: 17, Issue: 2, Pages: 171-183

Original Article

Comparative Machining Performance Analysis between Taguchi’s Method and Random Forest Model

Received Date:27 October 2023, Accepted Date:12 December 2023, Published Date:12 January 2024


Objectives: To optimize the process parameters in dry turning of hardened 20MnCr5 using a PVD coated insert to minimize surface roughness and power consumption and maximize material removal rate using the Taguchi technique and machine learning model. Methods: For carrying out the experiments, an L27 orthogonal array was employed and Taguchi’s means of mean method was used to find the optimal condition. ANOVA was utilized for determining the significance of factors. The Random Forest model uses an ensemble learning method where multiple decision trees are developed and then predictions are made. To boost the prediction efficiency, averages are made from these multiple decision trees and the voting method to finalize the model that needs to be used for the prediction. Finally, experimental and predicted values are compared using statistical metrics to determine the model's effectiveness. Findings: From experimental data at 80 m/min, 0.2 mm/rev, 0.5 mm and 120 m/min, 0.05 mm/rev, 0.25 mm and 100 m/min, 0.2 mm/rev, 0.1 mm are the optimal conditions found for material removal rate, surface roughness and power consumption respectively. Feed was the most influential factor, with a percentage contribution of 90.98%, 46.86% and 87.053% for surface roughness, material removal rate and power consumption respectively. The developed random forest models had an accuracy of 91.057%, 96.546% and 96.0122% for surface roughness, power consumption and material removal rate respectively, and they had less mean absolute errors. Novelty: Alloy steels are used to manufacture components that need to resist wear and plastic deformation, so it’s important to know how hardened material behaves under dry turning operations as the world is focusing on green manufacturing. Moreover, the experimental approach is time-consuming and costly, so a machine learning model was developed based on the available experimental data to predict the output responses for additional inputs.

Keywords: Dry turning, MQL machining, Taguchi technique, ANOVA, Random Forest model


  1. Jadhav BR, Patil AP, Dalavi SB. Review Paper of Dry Machining. International Journal of Science Technology & Engineering. 2020;6(7):11–15. Available from: https://ijste.org/Article.php?manuscript=IJSTEV6I7004
  2. Yugeshwar C, Prasad MVRD, Ramana MV. Dry machining of alloy steels – A review. Materials Today: Proceedings. 2023. Available from: https://doi.org/10.1016/j.matpr.2023.09.143
  3. Sinha MK, Pal A, Kishore K, Singh A, Archana, Sansanwal H, et al. Applications of sustainable techniques in machinability improvement of superalloys: a comprehensive review. International Journal on Interactive Design and Manufacturing (IJIDeM). 2023;17(2):473–498. Available from: https://doi.org/10.1007/s12008-022-01053-2
  4. Vukelic D, Simunovic K, Kanovic Z, Saric T, Tadic B, Simunovic G. Multi-objective optimization of steel AISI 1040 dry turning using genetic algorithm. Neural Computing and Applications. 2021;33(19):12445–12475. Available from: https://doi.org/10.1007/s00521-021-05877-z
  5. Gürbüz H, Gönülaçar YE. Optimization and evaluation of dry and minimum quantity lubricating methods on machinability of AISI 4140 using Taguchi design and ANOVA. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. 2021;235(7):1211–1227. Available from: https://doi.org/10.1177/0954406220939609
  6. Şahinoğlu A, Rafighi M. Investigation of Vibration, Sound Intensity, Machine Current and Surface Roughness Values of AISI 4140 During Machining on the Lathe. Arabian Journal for Science and Engineering. 2020;45(2):765–778. Available from: https://doi.org/10.1007/s13369-019-04124-x
  7. Paturi UMR, Yash A, Palakurthy ST, Reddy NS. Modeling and optimization of machining parameters for minimizing surface roughness and tool wear during AISI 52100 steel dry turning. Materials Today: Proceedings. 2022;50(Part 5):1164–1172. Available from: https://doi.org/10.1016/j.matpr.2021.08.047
  8. Adizue UL, Tura AD, Isaya EO, Farkas BZ, Takács M. Surface quality prediction by machine learning methods and process parameter optimization in ultra-precision machining of AISI D2 using CBN tool. The International Journal of Advanced Manufacturing Technology. 2023;129(3-4):1375–1394. Available from: https://doi.org/10.1007/s00170-023-12366-1
  9. Shanmugasundar G, Vanitha M, Čep R, Kumar V, Kalita K, Ramachandran M. A Comparative Study of Linear, Random Forest and AdaBoost Regressions for Modeling Non-Traditional Machining. Processes. 2021;9(11):1–14. Available from: https://doi.org/10.3390/pr9112015
  10. Kishore K, Chauhan SR, Sinha MK. Application of machine learning techniques in environmentally benign surface grinding of Inconel 625. Tribology International. 2023;188:108812. Available from: https://doi.org/10.1016/j.triboint.2023.108812
  11. HMT. Production technology. (p. 98) Tata McGraw-Hill Publishing Company Limited. 2009.
  12. Şahin E, Esen İ. Statistical and Experimental Investigation of Hardened AISI H11 Steel in CNC Turning with Alternative Measurement Methods. Advances in Materials Science and Engineering. 2021;2021:1–17. Available from: https://doi.org/10.1155/2021/9480303
  13. Kumar R, Bilga PS, Singh S. An Investigation of Energy Efficiency in Finish Turning of EN 353 Alloy Steel. Procedia CIRP. 2021;98:654–659. Available from: https://doi.org/10.1016/j.procir.2021.01.170
  14. Sivaiah P, Prasad MG, M MS, Uma B. Machinability evaluation during machining of AISI 52100 steel with textured tools under Minimum Quantity Lubrication – A comparative study. Materials and Manufacturing Processes. 2020;35(15):1761–1768. Available from: https://doi.org/10.1080/10426914.2020.1802034
  15. Rajarajan S, Kannan CR, Dennison MS. A comparative study on the machining characteristics on turning AISI 52100 alloy steel in dry and microlubrication condition. Australian Journal of Mechanical Engineering. 2022;20(2):360–371. Available from: https://doi.org/10.1080/14484846.2019.1710019
  16. Patel S, Patel D, Patel R, Patel M, Patel K. Experimental research on power consumption, material removal rate and surface roughness for machining of EN19 steel. In: 2ND INTERNATIONAL CONFERENCE ON MANUFACTURING, MATERIAL SCIENCE AND ENGINEERING 2020: ICMMSE 2020, AIP Conference Proceedings. Hyderabad, India, 18–19 December 2020. AIP Publishing. 2358, Issue 1.
  17. Airao J, Nirala CK, Bertolini R, Krolczyk GM, Khanna N. Sustainable cooling strategies to reduce tool wear, power consumption and surface roughness during ultrasonic assisted turning of Ti-6Al-4V. Tribology International. 2022;169:1–12. Available from: https://doi.org/10.1016/j.triboint.2022.107494
  18. Santhosh AJ, Tura AD, Jiregna IT, Gemechu WF, Ashok N, Ponnusamy M. Optimization of CNC turning parameters using face centred CCD approach in RSM and ANN-genetic algorithm for AISI 4340 alloy steel. Results in Engineering. 2021;11:1–9. Available from: https://doi.org/10.1016/j.rineng.2021.100251


© 2024 Yugeshwar et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


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