Title: Artificial intelligent denoising spectrograms approach for enhanced chatter detection in robotic machining

Authors: Dialoke Ejiofor Matthew; Hongrui Cao; Jianghai Shi

Addresses: National and Local Joint Engineering Research Center of Equipment Operation Safety and Intelligent Monitoring, Xi'an Jiaotong University, Xi'an, 710049, China; School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, China ' National and Local Joint Engineering Research Center of Equipment Operation Safety and Intelligent Monitoring, Xi'an Jiaotong University, Xi'an, 710049, China; School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, China ' National and Local Joint Engineering Research Center of Equipment Operation Safety and Intelligent Monitoring, Xi'an Jiaotong University, Xi'an, 710049, China; School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, China

Abstract: Accurate chatter detection becomes vital to preventing chatter issues. Because of the complicated dynamics involved, chatter can be particularly difficult to identify and reduce in robotic machining, where robotic arms are used for material removal operations. In order to detect chatter in robotic machining, this paper provides an artificial intelligence (AI) approach called attention-based denoising and adaptive thresholding methods. A potent method for spectrogram denoising, which provides flexibility to adaptively focus on pertinent information while maintaining significant characteristics. By utilising an efficient attention mechanism that can extract pertinent information from the input data, it provides a high-resolution spectrogram in comparison to the conventional method. The efficiency of the proposed method was demonstrated by a series of experimental tests. The average entropy of the spectrograms generated by the conventional and proposed methods is found to be 12.66 and 10.01, respectively.

Keywords: attention-based denoising; chatter detection; robotic milling; VMD; variational mode decomposition; machining dynamics.

DOI: 10.1504/IJMMS.2024.144290

International Journal of Mechatronics and Manufacturing Systems, 2024 Vol.17 No.4, pp.387 - 410

Received: 28 Apr 2024
Accepted: 20 Sep 2024
Published online: 04 Feb 2025 *

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