On Estimation of Optimal Process Parameters of the Abrasive Waterjet Machining

This paper is a commentary on the work of Zain et al. (2011a) in which they proposed the use of soft computing techniques, i.e., genetic algorithm (GA) and simulated annealing (SA), to estimate optimal process parameters of the abrasive waterjet machining. We found a fundamental problem with their work; the authors have misinterpreted the original work of Caydas and Hascalik (2008) in which the real problem is how to build predictive models for the machining process, not to optimize the model variables using the estimate model as presented by Zain et al. (2011a). In addition to this problem, the objective function to be optimized is an infinitely differentiable quadratic formula with box constraints, thus instead of using soft computing techniques, gradient methods that have better convergence property and are usually faster should be employed first. In particular, we show that the active set method outperforms GA and SA both in the machining performance and computational times (about 13 times faster than GA and 462 times faster than SA).