A two-step machine learning approach for dynamic model selection: A case study on a micro milling process

Abstract

Generally, dynamic model selection is implemented using algorithms that need a feedback from the system’s output; but, in many real-world applications this feedback is not available. For dealing with this challenge, a novel two-step machine learning approach is introduced by designing a dynamic model selection strategy where the selector only requires information about the system parametrization and not about its output. The first step of this approach is centred on a selection procedure that determines the most adequate model from a digital library with eight machine learning techniques using k-Nearest Neighbours. In the second step, the selected model is then used to make the prediction. The proposed approach is validated in a case study for predicting surface roughness of a micro-machining process which presents complex cutting phenomena, such as built-up edge and micro-burr formation. The experimental results corroborate the advantages of the proposed method increasing R2 from 0.892 to 0.915 and decreasing mean absolute percentage error from 19.79 % to 14.63 % when compared to the best individual models’ metrics.