An automated feature extraction method with application to empirical model development from machining power data Dimitrios Pantazis Paul Goodall Paul Conway Andrew West 2134/36951 https://repository.lboro.ac.uk/articles/journal_contribution/An_automated_feature_extraction_method_with_application_to_empirical_model_development_from_machining_power_data/9563618 Machining shop floor jobs are rarely optimised for minimisation of the energy consumption, as no clear guidelines exist in operating procedures and high production rates and finishing quality are requirements with higher priorities. However, there has been an increased interest recently in more energy-efficient process designs, due to new regulations and increases in energy charges. Response Surface Methodology (RSM) is a popular procedure using empirical models for optimising the energy consumption in cutting operations, but successful deployment requires good understanding of the methods employed and certain steps are time-consuming. In this work, a novel method that automates the feature extraction when applying RSM is presented. Central to the approach is a continuous Hidden Markov model, where the probability distribution of the observations at each state is represented by a mixture of Gaussian distributions. When applied to a case study, the automated extracted material cutting energies lay within 1.12% of measured values and the spindle acceleration energies within 3.33% of their actual values. 2019-02-22 15:17:24 Feature extraction Machining Hidden Markov model (HMM) Response Surface Methodology (RSM) Power data Time-series segmentation Energy decomposition Hierarchical clustering End-milling Empirical model Mechanical Engineering not elsewhere classified Mechanical Engineering