Optimization of Milling Parameters for Energy Savings and Surface Quality

Enhancing energy efficiency and product quality by means of optimal inputs is a cost-effective solution, as compared to the drastic investment. This paper aims to optimize the machining inputs to enhance energy efficiency (EF) as well as the power factor (PO) and decrease the surface roughness (Ra) for the milling process. The factors considered are the feed (f), depth of cut (a), milling speed (V), and tool radius (r). The machining operations were executed on the vertical milling under the dry condition for the stainless steel 304. A type of neutral network entitled the radius basic function (RBF) was used to render the relationships between milling inputs and performances measured. The adaptive simulated annealing (ASA) algorithm was applied to obtain the optimal values. The outcomes indicated that the milling responses are primarily influenced by a, f, V, and r, respectively. The reduction in Ra is approximately 39.18%, while the improvements in EF and PO are around 22.61% and 26.47%, respectively, as compared to the initial parameter settings. The explored findings are expected as a prominent solution for the industrial application of the dry machining. The combination of the RBF models and ASA could be considered as an efficient approach for modeling dry machining processes and generating reliable as well as feasible optimal results.