Multi-Point Face Milling Tool Condition Monitoring Through Vibration Spectrogram and LSTM-Autoencoder

doi:10.23940/ijpe.22.08.p5.570579

Abstract

Abstract: The intelligent factory defined by Industry 4.0 is established on intelligent machines, services, and production. To cope up with these requirements, topics such as Artificial Intelligence (AI), Evolutionary Computation (EC), Internet of Things (IoT), and Big Data are gaining a lot of attention in the manufacturing sector. In spite of the use of optimized input parameters, owing to some unknown moments, a machining activity tends to produce tool wear, break, and chatter that affect tool life, resulting in poorer surface roughness. Thus, there is a need to adopt self-monitoring of tools so that the diagnosis can be done without any human intervention. As the 4th industrial revolution pervades production sector, the volume of manufacturing data created has approached big data proportions, and is very dynamic. In an attempt to compute such vast data, Deep Learning (DL) approach is being considered to be a potential tool. In this paper, investigation of unknown vibration moments leading to cutting tool faults is ventured upon through spectrogram and deep learning ensemble i.e. LSTM (Long-Short-Term Memory)-Auto encoder.

Key words: multi point face milling tool, condition monitoring, spindle vibration, spectrogram, LSTM autoencoder

Keshav H. Jatakar, Gopal Mulgund, Abhishek D. Patange, B. B. Deshmukh, and Kishor S. Rambhad. Multi-Point Face Milling Tool Condition Monitoring Through Vibration Spectrogram and LSTM-Autoencoder [J]. Int J Performability Eng, 2022, 18(8): 570-579.

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