Feature representation of audible sound signal in monitoring surface roughness of the grinding process

Abstract

This study proposes the feature representation method of audible sound (AS) signal in the grinding process. The extracted sound feature is provided as the input for the machine learning model to predict the machining surface roughness. Firstly, a sensitive EEMD-IMPE feature set is extracted from the AS signal basing on the combination of the ensemble empirical mode decomposition (EEMD) and improved multiscale permutation entropy (IMPE) methods. Then, an optimized PSO-LS-SVR predictor model is- established basing on the particle swarm optimization algorithm (PSO) and least square support vector regression (LS-SVR) to predict the surface roughness. The experiments demonstrated the consistent AS feature, which is specific to the grinding surface quality in a cutting parameter set. The results of the PSO-LS-SVR model show that the extracted EEMD-IMPE feature is used to predict the grinding surface roughness with the high prediction accuracy and can be controlled within 8% of testing data.

Keywords

• Audible sound (AS)
• EEMD-IMPE feature representation
• surface roughness
• grinding
• optimized predictor model