Optimized tabu search estimation of wear characteristics and cutting forces in compact core drilling of basalt rock using PCD tool inserts

Abstract

Monitoring of cutting tool wear is crucial in machining operations where planning for longer tool life would result in economical savings. While deterministic and stochastic methods were sought for identifying optimal cutting conditions, efficiency and/or accuracy of the generated model fits were found to suffer when dealing with large number of variables. In this work, we present a novel method for tool wear optimization that combines tabu search algorithm with regression analysis (dubbed TS-REG). First, this method is validated against literature-reported model equation fitting of several modeling studies of turning, end milling, and drilling processes. Corroboration of TS-REG is established having found that it outperforms other techniques such as REG, artificial neural networks (ANN), and genetic algorithm (GA). Then, TS-REG is utilized to estimate tool wear and cutting forces for efficient core drilling of basalt rock, a hard and abrasive component of the Martian surface. For cutting data, this work utilizes previously reported tool wear and force data by Hamade and coworkers of polycrystalline diamond (PCD) compact core drilling experiments. The cutting data is comprised of dependent variables of tool wear (flank wear and cutting edge radius wear) and forces (thrust force and torque)as function of several independent variables (process parameters) namely rake angle, spindle speed, tool feed, rock specimen's ultimate compressive strength, UCS, specimen rock type, and drilled depth. These independent variables and their combinations resulted in fit equations with total number of 117 different variables. As compared with conventional REG and ANN fit estimates of the same data, TS-REG was found to yield the best statistical estimates of tool wear and cutting forces model equations where all dependent parameters were included in the model. Statistical metrics used for assessment of the fitted models were p-value, R-squared, and mean absolute percentage error. © 2019 Elsevier Ltd