A Novel, Nelder-Mead Optimization Approach, based on Neuro-regression modeling for the Energy Efficiency Parameters of End Milling Process

Global crises are increasing day by day due to the rapid depletion of energy supplies around the planet. One of the goals of engineering is to prevent this situation by developing innovative solutions to this rapid energy consumption that has disappeared in the world. A solution could be to reduce the energy consumption of the machines that are used during production. In this study, a new design technique based on the neuro-regression approach and non-linear regression modeling was offered as an alternative to Taguchi design to reduce energy consumption. Thus, a cutting parameter optimization model was created to examine the effects of the constraint conditions on energy consumption. The cutting power, the surface roughness of the part, and tool life were handled as objective functions(constraint conditions). First of all, the multiple non-linear regression modeling was created using design variables in end milling . These design variables were determined as spindle rotational speed, feed rate power, radial cut depth, axial cut depth, and cutting speed. Then, objective functions were brought to the proper minimum optimal levels due to this optimization modeling. As a result of the optimization model built with design variables, accurate modeling was achieved in this work by studying several optimization models utilized to optimize the minimum objective functions, which play a significant role in reducing energy consumption in end milling. After the optimization, the maximum value was found as 110.791. At the end of the study, some options of direct search method to maximize and minimize results were applied.