A comprehensive investigation on the influences of optimal CNC wood machining variables on surface quality and process time using GMDH neural network and bees optimization algorithm

Abstract

The present research work is concerned with the effects of machining parameters on the quality and economical aspects of wooden product. Process variables include depth of cut, feed rate, spindle speed, and step over in three levels and the purpose of the current study is to minimize the surface roughness and the process time, simultaneously. Each variable has three levels and as a result, the number of experimental tests in full factorial mode was considered to 81. Group method of data handling (GMDH) has been employed to train the neural network and extract the relationships governing the machining process. Optimization of the machining parameters has been done using the bees algorithm (BA). The outcomes obtained from GMDH-BA hybrid technique demonstrated that the combined objective function of the surface roughness and process time has been improved about 23% in comparison with the best experiment in all practical tests. The value of surface roughness and the process time in the optimized experimental test were reduced to 4.36 µm and 93 s, respectively.