Issue 70

P. Kulkarni et alii, Frattura ed Integrità Strutturale, 70 (2024) 71-90; DOI: 10.3221/IGF-ESIS.70.04

capabilities, fuzzy principles, and evolutionary computational philosophy, are being increasingly utilized in modeling such complex machining processes. Utilizing well-designed NFMQL machining trials, this study builds models for adaptive neuro-fuzzy inference systems (ANFIS) and artificial neural networks (ANN) to forecast the machining performance of Inconel 718 turning. The study used the neural network toolbox and ANFIS toolbox in MATLAB software to train and forecast the cutting force, surface roughness, and tool life. From the current study, the following conclusions could be drawn:  The selection of feed and depth of cut turned out to be more critical in achieving a lower cutting force. Feed significantly impacts surface roughness. The selection of lower cutting parameters led to better tool life estimates.  The highest tool life of 8-10 min was obtained at a lower cutting speed of 30 m/min, a feed in the range of 0.2-0.25 mm/rev, and a lower depth of cut. The surface roughness was found to reach beyond 2 µm at higher values of feed and depth of cut of 0.3 mm/rev and 0.8 mm. The cutting force found to be higher, reaching up to 800 N at higher values of feed and depth of cut of 0.3 mm/rev and 0.8 mm.  The ANN model developed to predict cutting force, surface roughness, and tool life using one hidden layer with ten neurons showed optimal validation performance at epoch 5, with a score of 92.89 and a prediction accuracy of 0.9942.  The ANFIS models were developed using triangular, trapezoidal, Gaussian, and generalized bell membership functions. ANFIS models developed for cutting force and surface roughness showed better prediction accuracy with the triangular membership function, followed by the Gaussian (Gaussmf) membership function. However, better prediction accuracy of the ANFIS tool life model was observed with almost all the different membership functions considered in the present study.  The ANFIS models developed using the triangular membership function showed minimum testing error with better R squared values of 0.9815, 0.8543, and 0.9986 for cutting force, surface roughness, and tool life, respectively.  This study found a good agreement between the experimental findings and the ANN and ANFIS predicted results, with an average error of less than 15%. However, ANFIS outperforms ANN in terms of accuracy, with prediction errors of 4.47% and 10.97% for surface roughness, and 6.05% and 9.86% for tool life, respectively. However, the accuracy of cutting force prediction was slightly higher with the ANN, with prediction errors of 11.02% against 13.15% for ANFIS. This shows that ANFIS could be a better option for forecasting the machining performance while turning Inconel 718.  This study suggests further investigation into ANFIS modeling, with a focus on membership function parameter optimization through hybrid optimization techniques. [1] Kulkarni, P. and Chinchanikar, S. (2023). A Review on Machining of Nickel-Based Superalloys Using Nanofluids Under Minimum Quantity Lubrication (NFMQL). J. Inst. Eng. India Ser. C, 104(1), pp. 183-199. DOI: 10.1007/s40032-022-00905-w [2] Saleem, M.Q. and Mehmood, A. (2022). Eco-friendly precision turning of superalloy Inconel 718 using MQL based vegetable oils: tool wear and surface integrity evaluation. J. Manuf. Processes, 73, pp. 112-127. DOI: 10.1016/j.jmapro.2021.10.059 [3] Airao, J., Khanna, N. and Nirala, C.K. (2022). Tool wear reduction in machining Inconel 718 by using novel sustainable cryo-lubrication techniques. Tribol. Int., 175, p. 107813. DOI: 10.1016/j.triboint.2022.107813 [4] Pandey, K. and Datta, S. (2021). Machinability study of Inconel 825 superalloy under nanofluid MQL: Application of sunflower oil as a base cutting fluid with MWCNTs and nano-Al 2 O 3 as additives. In Sustainable Manufacturing and Design (pp. 151-197). Woodhead Publishing. DOI: 10.1016/B978-0-12-822124-2.00008-1 [5] Prabhu, S. and Vinayagam, B.K. (2015). Adaptive neuro fuzzy inference system modelling of multi-objective optimisation of electrical discharge machining process using single-wall carbon nanotubes. Aust. J. Mech. Eng., 13(2), pp. 97-117. DOI: 10.7158/M13-074.2015.13.2 [6] Hegab, H., Salem, A., Rahnamayan, S. and Kishawy, H.A. (2021). Analysis, modeling, and multi-objective optimization of machining Inconel 718 with nano-additives based minimum quantity coolant. Appl. Soft Comput., 108, p. 107416. DOI: 10.1016/j.asoc.2021.107416 [7] Chinchanikar, S. and Gadge, M. (2024). Investigations on tool wear behavior in turning AISI 304 stainless steel: An empirical and neural network modeling approach. Frattura ed Integrità Strutturale, 18(67), 176-191. DOI:10.3221/IGF-ESIS.67.13 R EFERENCES

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