Tool wear estimation and life prognostics in milling: Model extension and generalization

Zhu, Kunpeng; Duan, Xianyin; Li, Si

doi:10.1016/j.ymssp.2021.107617

Cited by 50 publications

(11 citation statements)

References 39 publications

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“…Palmai 6 established a new flank wear rate model that can predict tool life under arbitrary failure criteria, considering the influence of process parameters and temperature. Zhang et al 7 divided the tool life into three stages according to different wear mechanisms, and then established a general tool wear model and a tool life model. However, these mathematical models fail to update with online measurement data, and complex wear mechanisms limit the application of physics-based methods.…”

Section: Introductionmentioning

confidence: 99%

Cutting tool remaining useful life prediction based on robust empirical mode decomposition and Capsule-BiLSTM network

Sun

Zhao

Huang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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In industrial production, effectively predicting the remaining useful life (RUL) of cutting tools can avoid overuse or underuse, which is of great significance for ensuring the processing quality of products and reducing enterprises’ production costs. This paper proposes a new method for RUL prediction of cutting tools based on robust empirical mode decomposition (REMD) and capsule bidirectional long short-term memory (Capsule-BiLSTM) network to improve accuracy. On one hand, new state features are extracted using REMD as the input of the deep learning network. On the other hand, a Capsule-BiLSTM network structure is designed to achieve RUL prediction of cutting tools by connecting the four layers. Finally, the effectiveness of the proposed method is verified by a series of cutting tool life tests. Comparison with some mainstream methods indicates that the proposed method has more advantages in RUL prediction of cutting tools with the average accuracy reaching up to 93.97%.

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Section: Introductionmentioning

confidence: 99%

Cutting tool remaining useful life prediction based on robust empirical mode decomposition and Capsule-BiLSTM network

Sun

Zhao

Huang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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show abstract

“…Zhang [5] proposed a generalized wear model with adjustable coefficients, which considered the mechanism of the tool in different wear stages, and divided the entire tool life into three main wear areas according to the critical time, corresponding to the three main wear types: running-in wear, adhesive wear and three-body abrasive wear. The model is based on experimental data and refers to other well-known wear models to enhance adaptability and generalization.…”

Section: Introductionmentioning

confidence: 99%

Tool wear prediction model based on wear influence factor

Cheng¹,

Shi²,

Xin

et al. 2023

Preprint

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This study proposed a new method for predicting tool wear curve over machining time through abscissa stretching or compressing based on wear influence factor. In this method, firstly, the relationship model between the tool wear rate and the cutting parameters needs to be built, and the wear influence factor can be derived from this relationship model. Then, it needs to record the curve of the tool wear value over machining time under a certain cutting parameters through experiments. This curve is called the benchmark tool wear curve, and the wear influence factor under this cutting parameters is called the benchmark wear influence factor. When the cutting parameters change, it is only required to solve the ratio between the wear influence factor under current cutting parameters and the benchmark wear influence factor, then use the ratio to stretch or compress the benchmark tool wear curve in the direction of the abscissa, that is the tool wear prediction curve under current cutting parameters.

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“…The current research work primarily focuses on evaluating tool wear of machine tools. While cutting the workpiece, tool wear will gradually occur under the influence of friction, thermal deformation, and other factors, 30,31 Tool wear is an important factor affecting the machining quality. 32,33 Monitoring tool wear during the machining process is of great significance, as it can affect the machining quality of the workpiece and pose a risk to workers when the cutting tool reaches a certain level of wear, the machining quality of the workpiece cannot be guaranteed and even bring danger to workers.…”

Section: Introductionmentioning

confidence: 99%

Time-frequency analysis-based impulse feature extraction method for quantitative evaluation of milling tool wear

Guo,

Tu,

Abbas

et al. 2023

Structural Health Monitoring

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Mechanical system condition monitoring is an important procedure in modern industry, which not only reduces maintenance costs but also ensures safe equipment operation. At present, the monitoring method based on signal processing is one of the most common and effective fault diagnosis methods. In this work, the time-frequency distribution (TFD) obtained by generalized horizontal synchrosqueezing transform is used to extract the impulse feature of the non-stationary vibration signal of the tool. By using the TFD result, the two-dimensional (2D) Fourier transform can further detect the periodic pulses. Next, the energy proportion factor of periodic frequency point is proposed to evaluate the different tool wear degrees. Numerical simulations and experimental data analysis demonstrate the effectiveness of the proposed method as well as the potential for condition monitoring.

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Tool wear estimation and life prognostics in milling: Model extension and generalization

Cited by 50 publications

References 39 publications

Cutting tool remaining useful life prediction based on robust empirical mode decomposition and Capsule-BiLSTM network

Cutting tool remaining useful life prediction based on robust empirical mode decomposition and Capsule-BiLSTM network

Tool wear prediction model based on wear influence factor

Time-frequency analysis-based impulse feature extraction method for quantitative evaluation of milling tool wear

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