Orthogonal array and artificial neural network approach for sustainable cutting optimization machining of 17-4 PH steel under CNC wet turning operations
Abstract
Sustainable manufacturing strives to increase output while minimizing resource usage, costs, and environmental impact. Tool longevity is crucial, considering material, power, and resource consumption. Challenges like chip removal, heat, and friction necessitate effective solutions. Cutting fluids, like rice bran oil, reduce temperatures, friction, and enhance tool life and surface quality. Research focuses on using rice bran oil with a TiAlN tipped tool to minimize friction and cutting forces. Taguchi's robust design and L-9 orthogonal array reduce experimental trials. Controllable factors include speed, feed, depth of cut, and rice bran oil flow rate, with surface roughness as the performance metric. Taguchi analysis improves turning performance, particularly with 17-4 PH stainless steel. Rice bran oil reduces frictional forces, enabling improved surface quality across all samples, promoting sustainability. Surface roughness (Ra) is notably influenced by feed rate and rice bran oil, contributing 58.06% and 49.5%, respectively. This research underscores the potential of optimizing cutting fluid and process parameters to enhance sustainability in manufacturing.
References
Abbas, A. T., Pimenov, D. Y., Erdakov, I. N., Mikolajczyk, T., Soliman, M. S., & El Rayes, M. M. (2019). Optimization of cutting conditions using artificial neural networks and the Edgeworth-Pareto method for CNC face-milling operations on high-strength grade-H steel. The International Journal of Advanced Manufacturing Technology, 105, 2151-2165. https://doi.org/10.1007/s00170-019-04327-4
Aoyama, T., Kakinuma, Y., Yamashita, M., & Aoki, M. (2008). Development of a new lean lubrication system for near dry machining process. CIRP Annals, 57(1), 125-128. https://doi.org/10.1016/j.cirp.2008.03.094
Braham-Bouchnak, T., Germain, G., Robert, P., & Lebrun, J. L. (2010). High pressure water jet assisted machining of duplex steel: machinability and tool life. Int. J. Mater. Form., 3(Suppl 1), 507–510. https://doi.org/10.1007/s12289-010-0818-9
Choi, Y. (2019). Effects of cutting speed on surface integrity and fatigue performance of hard machined surfaces. International Journal of Precision Engineering and Manufacturing, 20, 139-146. http://dx.doi.org/10.1007/s12541-019-00045-9
Elangovan, M., Ramachandran, K. I., &Sugumaran, V. (2010). Studies on Bayes classifier for condition monitoring of single point carbide tipped tool based on statistical and histogram features. Expert Systems with Applications, 37(3), 2059-2065. https://doi.org/10.1016/j.eswa.2009.06.103
Ezugwu, E. O., Fadare, D. A., Bonney, J., Da Silva, R. B., & Sales, W. F. (2005). Modelling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using an artificial neural network. International Journal of Machine Tools and Manufacture, 45(12-13), 1375-1385. https://doi.org/10.1016/j.ijmachtools.2005.02.004
G, V., & M, D. (2023). TLBO-trained ANN-based Shunt Active Power Filter for Mitigation of Current Harmonics. Int. J. Exp. Res. Rev., 34(Special Vol), 11-21. https://doi.org/10.52756/ijerr.2023.v34spl.002
Haloi, R., Chanda, D., Hazarika, J., & Barman, A. (2023). Statistical feature-based EEG signals classification using ANN and SVM classifiers for Parkinson’s disease detection. Int. J. Exp. Res. Rev., 31(Spl Volume), 141-149.
https://doi.org/10.52756/10.52756/ijerr.2023.v31spl.014
Hong, S. Y., Ding, Y., &Jeong, W. C. (2001). Friction and cutting forces in cryogenic machining of Ti–6Al–4V. International Journal of Machine Tools and Manufacture, 41(15), 2271-2285.
http://dx.doi.org/10.1016/S0890-6955(01)00029-3
Khani, S., Farahnakian, M., &Razfar, M. R. (2015). Experimental study on hybrid cryogenic and plasma-enhanced turning of 17-4PH stainless steel. Materials and Manufacturing Processes, 30(7), 868-874.
Koyee, R. D., Heisel, U., Eisseler, R., & Schmauder, S. (2014). Modeling and optimization of turning duplex stainless steels. Journal of Manufacturing Processes, 16(4), 451-467.
http://dx.doi.org/10.1016/j.jmapro.2014.05.004
Krolczyk, G. M., Nieslony, P., & Legutko, S. (2015). Determination of tool life and research wear during duplex stainless steel turning. Archives of Civil and Mechanical Engineering, 15(2), 347-354. https://doi.org/10.1016/j.acme.2014.05.001
Li, N., Chen, Y. J., & Kong, D. D. (2018). Wear mechanism analysis and its effects on the cutting performance of PCBN inserts during turning of hardened 42CrMo. International Journal of Precision Engineering and Manufacturing, 19, 1355-1368. https://doi.org/10.1007/s12541-018-0160-6
Sharma, V. S., Dogra, M., & Suri, N. M. (2009). Cooling techniques for improved productivity in turning. International Journal of Machine Tools and Manufacture, 49(6), 435-453.
https://doi.org/10.1016/j.ijmachtools.2008.12.010
Sivaiah, P., & Chakradhar, D. (2019). Modeling and optimization of sustainable manufacturing process in machining of 17-4 PH stainless steel. Measurement, 134, 142-152.
https://doi.org/10.1016/j.measurement.2018.10.067
Venkatalaxmi, A., Padmavathi, B. S., &Amaranath, T. J. F. D. R. (2004). A general solution of unsteady Stokes equations. Fluid Dynamics Research, 35(3), 229-236.
http://dx.doi.org/10.1016/j.fluiddyn.2004.06.001
Warcholinski, B., &Gilewicz, A. (2011). Multilayer coatings on tools for woodworking. Wear, 271(11-12), 2812-2820. https://doi.org/10.1016/j.wear.2011.05.048
Copyright (c) 2024 International Academic Publishing House (IAPH)
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.