Predicting thrust force during drilling of composite laminates with step drills through the Gaussian process regression
Yun Zhang, Xiaojie Xu
Abstract
Purpose Here, the authors use step angles, stage ratios, feed rates and spindle speeds as predictors to develop a Gaussian process regression for predicting thrust force during composite laminates drilling with step drills. Design/methodology/approach Use of machine learning methods could benefit machining process optimizations. Accurate, stable and robust performance is one of major criteria in choosing among different models. For industrial applications, it is also important to consider model applicability, ease of implementations and cost effectiveness. Findings This model turns out to be simple, accurate and stable, which helps fast estimates of thrust force. Through combining the Taguchi method's optimization results and the Gaussian process regression, more data could be expected to be extracted through fewer experiments. Originality/value Through combining the Taguchi method's optimization results and the Gaussian process regression, more data could be expected to be extracted through fewer experiments.