Selection of drilling operations parameters for optimal tool loading using integrated response surface methodology: a Tribes approach

Abstract

Response surface methodology (RSM) is a technique for determining and representing the cause-and-effect relationship between true mean responses and input control variables influencing the responses as a two- or three-dimensional hyper surface. First, this paper presents a mathematical model for correlating the interactions of some drilling control parameters such as speed, feed rate and drill diameter, and their effects on some responses such as axial force and torque acting on the cutting tool during drilling by means of response surface methodology. For this exercise, a three-level full factorial design was chosen for experimentation using a computer-based computer numerically controlled drilling machine built in-house. Second, since finding the optimum set of experimental factors that produces maximum or minimum value of response(s), is a major step in RSM, the paper describes a new approach to this step, which is the optimization of the mathematical model realized from the RSM using one of the recent optimization techniques, Tribes. Comparing the optimization results for another published response problem between Gauss–Jordan algorithm and Tribes approaches, it was found that Tribes clocked a better result. Consequently, this paper reports not only on the use of RSM for analysing the cause and effect of process parameters on responses, but also on optimization of the process parameters themselves in order to realize optimal responses.