Computational methods for the detection of wear and damage to milling tools

Ninevski, Dimitar; Thaler, Julia; O’Leary, Paul; Klünsner, Thomas; Mücke, Manfred; Hanna, Lukas; Teppernegg, Tamara; Treichler, Martin; Peissl, Patrick; Czettl, Christoph

doi:10.1016/j.jmapro.2022.07.030

Cited by 3 publications

(1 citation statement)

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“…Li et al [5] proposed an early flutter detection method based on variational mode decomposition and power spectrum entropy difference, which can be applied to different milling situations. Ninevski et al [6] proposed a new computational method for detecting wear and damage of milling cutters and fully tested it on real industrial datasets, successfully demonstrating the technique's effectiveness for wear detection of milling cutters in actual industrial production and showing the potential of the technical process. Shi et al [7] proposed a new method for chattering detection based on ordered neuron long-and short-time memory (ON-LSTM) and population-based training (PBT).…”

Section: Introductionmentioning

confidence: 99%

Milling Fault Detection Method Based on Fault Tree Analysis and Hierarchical Belief Rule Base

Cheng¹,

Long²,

Wang³

et al. 2023

Computer Systems Science and Engineering

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Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base. The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the milling fault detection model. However, due to the complexity of the milling system structure and the uncertainty of the milling failure index, it is often impossible to construct model expert knowledge effectively. Therefore, a milling system fault detection method based on fault tree analysis and hierarchical BRB (FTBRB) is proposed. Firstly, the proposed method uses a fault tree and hierarchical BRB modeling. Through fault tree analysis (FTA), the logical correspondence between FTA and BRB is sorted out. This can effectively embed the FTA mechanism into the BRB expert knowledge base. The hierarchical BRB model is used to solve the problem of excessive indexes and avoid combinatorial explosion. Secondly, evidence reasoning (ER) is used to ensure the transparency of the model reasoning process. Thirdly, the projection covariance matrix adaptation evolutionary strategies (P-CMA-ES) is used to optimize the model. Finally, this paper verifies the validity model and the method's feasibility techniques for milling data sets.

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Section: Introductionmentioning

confidence: 99%