Abstract
Metal cutting researchers attempt to develop technology for metal cutting and use of cutting fluids have been continuous due to increasing demands for high productivity. This work reports on the effect of coconut oil under minimum quantity lubrication (MQL) during end milling of AL7049. Dry, wet and MQL approaches are all used to conduct the experimental studies. In contrast to dry and wet machining, it is apparent that MQL generates surfaces with less roughness. A multilayer perceptron (MLP) prediction model was developed considering the real-time dataset with three inputs, and an output that led to prediction of surface roughness. It was analyzed to determine the appropriate activation function and observed that ReLU activation function outpaces sigmoid and tanh and adapted to the proposed solution. K fold cross-validation was done for the developed MLP model with backpropagation to substantiate high accuracy in predictions than KNN and linear regression. The MQL will be good alternative to wet machining and also environmentally friendly machining solution.
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Abbreviations
- Δwij :
-
Weight of the synaptic link between ith and jth neurons
- ξ :
-
Error or training loss
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Lavanya G holds U.G. degree in Electrical and Electronics Engineering, P.G. degree in Information Technology, and a Ph.D., in Information and Communication Technology. With an impressive career spanning 23 years in teaching and 5 years dedicated to research, her contributions to the field of academia are substantial. Her commitment to education extends beyond teaching, as she has actively contributed to research projects and published 12 papers in prestigious journals. Her passion for machine learning and IoT enables to contribute in develo** technological advancements.
Sundaramurthy K is currently working as Professor in Department of Mechanical Engineering, Paavai Engineering College, India. He obtained his Ph.D. in machining studies from Anna University, Chennai, India in 2013. He obtained his Master’s degree in CAD/CAM from VIT University, Vellore, India in 2003 and Bachelor’s degree in Mechanical Engineering from Madras University, India in 2000. His areas of interests are study of machining parameters, optimization using soft computing techniques and environment friendly machining techniques. He is having 20 years of experience in teaching. Presently, he is guiding 7 research scholars.
Subburam V is working as a Professor in the Department of Mechanical Engineering at Paavai Engineering College, Tamilnadu, India. He received his Bachelor’s degree in Mechanical Engineering from Madras University and Master’s degree in Product Design and Development from Anna University, Chennai, India. He earned his Ph.D. from Anna University, Chennai, India. His research interests includes micromachining, composite materials and machining studies. He is having vast experience in teaching and research with many publications.
Makesh M is a Professor at Paavai Engineering College, Tamil Nadu, India. He completed his U.G. in 1998 in Madras University, India and earned his Master’s degree in 2007 and Ph.D. in 2019 from Anna University, Chennai, India. He is having research and teaching experience of 23 years. His areas of interest are machining studies, welding and hot corrosion. He has published 15 national and international journals.
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Lavanya, G., Sundaramurthy, K., Subburam, V. et al. Intelligent prediction using AI-based modeling and optimization of surface roughness in Al7049 end milling with coconut oil under minimum quantity lubrication. J Mech Sci Technol 38, 2005–2014 (2024). https://doi.org/10.1007/s12206-024-0332-5
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DOI: https://doi.org/10.1007/s12206-024-0332-5