Implementation of Adaptive Neuro Fuzzy Inference System in Determining the Influence of Bearing Clearance in Mild Steel Turning

References

[1] G. Struzikiewicz and A. Sioma, “Evaluation of Surface Roughness and Defect Formation after The Machining of Sintered Aluminum Alloy AlSi10Mg,” Materials (Basel)., vol. 13, no. 7, p. 1662, Apr. 2020, doi: 10.3390/ma13071662.

[2] V. Songmene, R. Khettabi, I. Zaghbani, J. Kouam, and A. Djebara, “Machining and machinability of aluminum alloys,” Alum. Alloy. Theory Appl, pp. 377–400, 2011.

[3] P. G. Benardos and G.-C. Vosniakos, “Predicting surface roughness in machining: a review,” Int. J. Mach. Tools Manuf., vol. 43, no. 8, pp. 833–844, Jun. 2003, doi: 10.1016/S0890-6955(03)00059-2.

[4] F. Koenigsberger, Design principles of metal-cutting machine tools. Elsevier, 2013.

[5] S. N. Melkote et al., “Advances in material and friction data for modelling of metal machining,” CIRP Ann., vol. 66, no. 2, pp. 731–754, 2017, doi: 10.1016/j.cirp.2017.05.002.

[6] M. P. Grooer, “Fundamentals of Modern Manufacturing, John Willey \& Sons Inc.” US, 2002.

[7] R. W. Ivester et al., “ASSESSMENT OF MACHINING MODELS: PROGRESS REPORT,” Mach. Sci. Technol., vol. 4, no. 3, pp. 511–538, Nov. 2000, doi: 10.1080/10940340008945720.

[8] H. S. Rao and A. Mukherjee, “Artificial neural networks for predicting the macromechanical behaviour of ceramic-matrix composites,” Comput. Mater. Sci., vol. 5, no. 4, pp. 307–322, Apr. 1996, doi: 10.1016/0927-0256(95)00002-X.

[9] U. Çaydaş, A. Hasçalık, Ö. Buytoz, and A. Meyveci, “Performance Evaluation of Different Twist Drills in Dry Drilling of AISI 304 Austenitic Stainless Steel,” Mater. Manuf. Process., vol. 26, no. 8, pp. 951–960, Aug. 2011, doi: 10.1080/10426914.2010.520790.

[10] L. A. Thiede and U. Parlitz, “Gradient based hyperparameter optimization in Echo State Networks,” Neural Networks, vol. 115, pp. 23–29, Jul. 2019, doi: 10.1016/j.neunet.2019.02.001.

[11] D. Bailey and D. Thompson, “How to develop neural-network applications,” AI Expert, vol. 5, no. 6, pp. 38–47, 1990.

[12] Y.-J. Zhai and D.-L. Yu, “Neural network model-based automotive engine air/fuel ratio control and robustness evaluation,” Eng. Appl. Artif. Intell., vol. 22, no. 2, pp. 171–180, Mar. 2009, doi: 10.1016/j.engappai.2008.08.001.

[13] A. Garg, K. Tai, C. H. Lee, and M. M. Savalani, “A hybrid-genetic programming approach for ensuring greater trustworthiness of prediction ability in modelling of FDM process,” J. Intell. Manuf., vol. 25, no. 6, pp. 1349–1365, 2014.

[14] N. R. J. Hynes and R. Kumar, “Process optimization for maximizing bushing length in thermal drilling using integrated ANN-SA approach,” J. Brazilian Soc. Mech. Sci. Eng., vol. 39, no. 12, pp. 5097–5108, Dec. 2017, doi: 10.1007/s40430-017-0820-y.

[15] P. Melin, J. Soto, O. Castillo, and J. Soria, “A new approach for time series prediction using ensembles of ANFIS models,” Expert Syst. Appl., vol. 39, no. 3, pp. 3494–3506, Feb. 2012, doi: 10.1016/j.eswa.2011.09.040.

[16] K. Cheng, Z.-C. Niu, R. C. Wang, R. Rakowski, and R. Bateman, “Smart cutting tools and smart machining: development approaches, and their implementation and application perspectives,” Chinese J. Mech. Eng., vol. 30, pp. 1162–1176, 2017.

[17] H. Park, B. Qi, D.-V. Dang, and D. Y. Park, “Development of smart machining system for optimizing feedrates to minimize machining time,” J. Comput. Des. Eng., vol. 5, no. 3, pp. 299–304, Jul. 2018, doi: 10.1016/j.jcde.2017.12.004.

[18] A. du Preez and G. A. Oosthuizen, “Machine learning in cutting processes as enabler for smart sustainable manufacturing,” Procedia Manuf., vol. 33, pp. 810–817, 2019, doi: 10.1016/j.promfg.2019.04.102.

[19] K. Manjunath, S. Tewary, N. Khatri, and K. Cheng, “Monitoring and Predicting the Surface Generation and Surface Roughness in Ultraprecision Machining: A Critical Review,” Machines, vol. 9, no. 12, p. 369, Dec. 2021, doi: 10.3390/machines9120369.

[20] A. Abdulshahed and I. Badi, “Prediction and control of surface roughness for end milling process using ANFIS,” Oper. Res. Eng. Sci. Theory Appl., vol. 1, no. 1, pp. 1–12, Dec. 2019, doi: 10.31181/oresta1901201011a.

[21] S.-P. Lo, “An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling,” J. Mater. Process. Technol., vol. 142, no. 3, pp. 665–675, Dec. 2003, doi: 10.1016/S0924-0136(03)00687-3.

[22] W.-H. Ho, J.-T. Tsai, B.-T. Lin, and J.-H. Chou, “Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid Taguchi-genetic learning algorithm,” Expert Syst. Appl., vol. 36, no. 2, pp. 3216–3222, Mar. 2009, doi: 10.1016/j.eswa.2008.01.051.

[23] A. B. Sharkawy, “Prediction of Surface Roughness in End Milling Process Using Intelligent Systems: A Comparative Study,” Appl. Comput. Intell. Soft Comput., vol. 2011, pp. 1–18, 2011, doi: 10.1155/2011/183764.

[24] M. Dong and N. Wang, “Adaptive network-based fuzzy inference system with leave-one-out cross-validation approach for prediction of surface roughness,” Appl. Math. Model., vol. 35, no. 3, pp. 1024–1035, Mar. 2011, doi: 10.1016/j.apm.2010.07.048.

[25] U. M. R. Paturi, H. Devarasetti, D. A. Fadare, and S. K. R. Narala, “Application of artificial neural network and response surface methodology in modeling of surface roughness in WS2 solid lubricant assisted MQL turning of Inconel 718,” in IOP conference series: materials science and engineering, 2018, p. 12085.

[26] S. Guillaume, “Designing fuzzy inference systems from data: An interpretability-oriented review,” IEEE Trans. Fuzzy Syst., vol. 9, no. 3, pp. 426–443, Jun. 2001, doi: 10.1109/91.928739.

[27] L. D. Schroeder, D. L. Sjoquist, and P. E. Stephan, Understanding Regression Analysis: An Introductory Guide. 2455 Teller Road, Thousand Oaks California 91320: SAGE Publications, Inc, 2017. doi: 10.4135/9781506361628.

[28] N. K. Kasabov, Foundations of neural networks, fuzzy systems, and knowledge engineering. Marcel Alencar, 1996.

[29] A. M. Abd El-Raaouf, M. S. Osman, M. H. El-Axir, and A. A. Elshanawani, “Applicability of fuzzy approach for the optimization and analysis of surface roughness in orthogonal cutting,” in Seventh international conference on production engineering, design, and control, Alexandria-Egypt, 2001, pp. 1261–1272.

[30] E. Czogała and J. Leski, Fuzzy and neuro-fuzzy intelligent systems, vol. 47. Springer Science \& Business Media, 2000.

@article{1705041,
      author = {Amiebenomo Sebastian Oaihimire, Ighodalo Osagie},
      title = {Implementation of Adaptive Neuro Fuzzy Inference System in Determining the Influence of Bearing Clearance in Mild Steel Turning},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {3},
      pages = {236-251},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705041.pdf},
      abstract = {The metal cutting industry constantly seeks to optimize machining techniques to guarantee highly accurate parts while minimizing costs. However, the complexity of the relationship between process parameters and performance measures such as metal removal rate (MRR), surface finish, chip flow pattern, specific energy consumption, and tool life presents a significant challenge for manufacturers. The paper introduces an innovative solution for determining the influence of bearing clearance on mild steel turning operations using the Adaptive Neuro Fuzzy Inference System (ANFIS) optimization model. By assimilating insights from training data onto a fuzzy inference system, ANFIS effectively maps solutions to complex problems. The proposed approach accurately predicts surface roughness, MRR, and tool wear for different sets of cutting parameters, offering a viable approach for improving product quality and profitability while reducing associated manufacturing costs. By applying substrative clustering with values of radius of parameter equal to 0.1, 0.2, and 0.3 respectively, the initial membership function of the independent variables and fuzzy rules were developed. Training was done by using an initial step size of 0.1, the value of MAPE obtained was 3.123% and correlation coefficient (R) of 0.9072. The results obtained indicate that the ANFIS model predicts surface roughness with high accuracy, with an average error of 0.17?m. The study also found that increasing bearing clearance results in a decrease in surface roughness, as seen with a reduction from 3.96?m to 2.22 ?m. Furthermore, the model predicted MRR with an average error of 0.6%, and it revealed that increasing bearing clearance results in a significant increase in MRR, ranging from 2.22 m3/min to 5.98 m3/min at a clearance level of 0.010mm. The findings contribute to the body of knowledge in manufacturing engineering, offering valuable insights into the relationship between bearing clearance, machining parameters, and performance measures.},
      keywords = {Accurate machining techniques, ANFIS model, Bearing clearance, Surface roughness in mild steel, Manufacturing engineering, Optimization modeling },
      month = {September},
  }