Online damage detection of cutting tools using Dirichlet process mixture models. (15th November 2022)

• Record Type: Journal Article
• Authors: Wickramarachchi, Chandula T.; Rogers, Timothy J.; McLeay, Thomas E.; Leahy, Wayne; Cross, Elizabeth J.
• Is Part Of: Mechanical systems and signal processing. Volume 180(2022)
• Publication Date: 2022-11-15
• Subjects: Dirichlet process -- Tool wear -- PcBN -- Unsupervised learning -- Clustering -- Damage detection; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2022.109434
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: The ability to monitor and predict tool deterioration during machining is an important goal because the state of wear has a significant influence on the surface quality of machined components. To build up a comprehensive condition monitoring system for diagnosis and prognosis, however, extensive measurements and knowledge of tool wear is required. Collecting labelled datasets that include damage information for this purpose can be expensive and time consuming. This paper suggests an unsupervised clustering approach using Dirichlet process mixture models to detect the change in characteristics of a cutting process online for diagnosis. As well as providing a useful monitoring tool, this approach has the potential to reduce the need for exhaustive wear measurements associated required for prognosis. The model is well suited to the erratic and unpredictable nature of tool wear progression, as the number of clusters required to determine the possible damage states are not set a-priori . Consequently, this method is equipped to handle variations across homogeneous and heterogeneous groups of tool material compositions. The proposed approach is demonstrated here as a method to reduce the time required for trials for wear characterisation of new tools. In the example shown, the results indicate that the approach would result in around a 30% reduction of test times (on average) during outer diameter turning of case hardened steel, across 10 Polycrystalline cubic Boron Nitride tools from two different material compositions. Highlights: New online method for detection of tool deterioration. Unsupervised clustering able to detect changes in turning tools in real-time. Harmonics of chip formation frequency from acoustic emissions used as inputs. Method provides potential to significantly reduce time and cost of trials for new tools. Parallel Dirichlet processes provide robustness against detection of changing work piece.