Kosarac, A.; Tabakovic, S.; Mladjenovic, C.; Zeljkovic, M.; Orasanin, G. Next-Gen Manufacturing: Machine Learning for Surface Roughness Prediction in Ti-6Al-4V Biocompatible Alloy Machining. J. Manuf. Mater. Process.2023, 7, 202.
Kosarac, A.; Tabakovic, S.; Mladjenovic, C.; Zeljkovic, M.; Orasanin, G. Next-Gen Manufacturing: Machine Learning for Surface Roughness Prediction in Ti-6Al-4V Biocompatible Alloy Machining. J. Manuf. Mater. Process. 2023, 7, 202.
Kosarac, A.; Tabakovic, S.; Mladjenovic, C.; Zeljkovic, M.; Orasanin, G. Next-Gen Manufacturing: Machine Learning for Surface Roughness Prediction in Ti-6Al-4V Biocompatible Alloy Machining. J. Manuf. Mater. Process.2023, 7, 202.
Kosarac, A.; Tabakovic, S.; Mladjenovic, C.; Zeljkovic, M.; Orasanin, G. Next-Gen Manufacturing: Machine Learning for Surface Roughness Prediction in Ti-6Al-4V Biocompatible Alloy Machining. J. Manuf. Mater. Process. 2023, 7, 202.
Abstract
Mechanical engineering plays an important role in the design and manufacture of medical devices, implants, prostheses, and other medical equipment, where the machining of bio-compatible materials have a special place. There are a lot of different conventional and non-conventional types of machining of biocompatible materials. One of the most frequently used methods is milling. The first part of this paper considers the optimization of machining parameters for minimizing surface roughness in milling biocompatible alloy Ti-6Al-4V. With four factors (cutting speed, feed rate, depth of cut, and cooling/lubricating method), each having three levels, the full factorial design implies 81 experiments have to be carried out. Using the Taguchi method, the experiment number was reduced from 81 to 27 runs through an orthogonal design. According to the analysis of variance (ANOVA), the most significant parameter for surface roughness is feed rate. In the second part possibilities of different ML techniques to create a predictive model for average surface roughness using previously created small dataset are explored. Several ML technologies and techniques that can deal successfully with small dataset were explored. The best results show commonly used machine learning algorithm Random Forest that is widely used in regression problems.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
