Kunpeng Zhu

TL;DR: This review provides a comprehensive survey of the current work on wavelet approaches to TCM and also proposes two new prospects for future studies in this area.

TL;DR: The intelligent classification methods, support vector machines (SVM) and convolutional neural network (CNN) were proposed for quality level identification in this work and indicated the information from different objects is sensitive to different types of quality anomalies.

TL;DR: A novel method for the defect detection within the SLM parts is proposed by a simplified classification structure without signal preprocessing and feature extraction and showed that the utilization of acoustic signals was workable for quality monitoring, and the DBN approach could reach high defect detection rate among five melted states without signalPreprocessing.

TL;DR: In this paper, a multi-category classification approach is proposed for tool wear state identification in micro-milling, and continuous hidden Markov models (HMMs) are adapted for modeling of the tool wear process in micromilling.

TL;DR: This study provides a flexible and integrated method for in situ process monitoring and melted state recognition during the SLM process, and it is useful for process optimization to decrease part quality issues.