Abstract—Titanium alloy is characterized with excellent mechanical properties such as lightweight, and good corrosion resistance ability, hence, it finds application in many industrial and engineering applications. This study considers the process design of the milling operation of titanium alloy using artificial intelligence. The numerical experimentation involves the use of the Artificial Neural Network (ANN) back propagation and Levenberg-Marquardt algorithm for the correlation of the process parameters while the physical experiments were investigated using a DMU80monoBLOCK Deckel Maho 5-axis CNC milling machine and carbide-cutting inserts of 12 and 14 mm (RCKT1204MO-PM S40T) under the cooling and dry machining conditions. The developed network was used to obtain a regression analysis which is suitable for the prediction of the feasible range of the process parameters. The results obtained from the physical experiments indicate significant reduction in the rate of tool wear under the cooling conditions as opposed to the dry machining. The findings of this work will find suitable application as a decision making tool in the manufacturing industries most especially the manufacturing industries, which employs titanium alloy for component part development.

Index Terms—ANN, milling operation, process design, process parameters, titanium alloy

Cite: Ilesanmi Afolabi Daniyan, Khumbulani Mpofu, Isaac Tlhabadira, and Boitumelo Innocent Ramatsetse, "Process Design for Milling Operation of Titanium Alloy (Ti6Al4V) Using Artificial Neural Network," International Journal of Mechanical Engineering and Robotics Research, Vol. 10, No.11, pp. 601-611, November 2021. DOI: 10.18178/ijmerr.10.11.601-611

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