Sergey Shevchik

TL;DR: In this paper, the authors investigated the feasibility of using acoustic emission for quality monitoring and combined a sensitive acoustic emission sensor with machine learning, where the acoustic signals were recorded using a fiber Bragg grating sensor during the powder bed additive manufacturing process in a commercially available selective laser melting machine.

TL;DR: This paper is a supplement to existing studies in this field and proposes a unique combination of highly sensitive acoustic sensor and machine learning for process monitoring of AM processes since it requires minimum modifications of commercially available industrial machines.

TL;DR: In this paper, the authors proposed to combine acoustic emission and reinforcement learning for in situ and real-time quality monitoring of additive manufacturing (AM) processes in commercial equipment and demonstrated that each level of quality produced unique acoustic signatures during the build that were recognized by the classifier.

TL;DR: A method for real-time detection of process instabilities that can lead to defects that can be exploited to provide feedbacks in a closed-loop quality control system is proposed.

TL;DR: It is reported that most of the Artificial Intelligence algorithms available are not fully exploited for monitoring and modelling in abrasive finishing and emphasizes on bridging this gap.