A Novel Method for Tool Wear Prediction in Titanium Milling by Simulink Feedback Method

Ding, Baoyang

doi:10.3901/jme.2019.11.224

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…Dai et al 9 improve the prediction accuracy and generalization performance of tool wear monitoring based on deep learning. Ding et al 10 state a novel method for tool wear prediction in titanium milling using the Simulink feedback method. Cao et al 11 introduce a tool condition monitoring approach based on a convolutional neural network.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of milling process parameters and prediction of abrasive wear rate increment based on cutting force experiment

Liu

2021

Advances in Mechanical Engineering

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Tuning the parameters of Computerized Numerical Control (CNC) is essential for practical manufacturers. Well configured parameters ensure the efficiency of production and the accuracy of the products. However, with the abrasive wear on the flank of the milling cutter, the milling processing parameters should re-configure to adapt to the increment of the abrasive wear. This paper aims to propose a method to predict the abrasive wear rate increment on the flank of the milling cutter and optimize the processing parameters of CNC milling. Firstly, we set a cutting data acquisition system to sample the processing time and cutting force among X, Y coordinates based on the five-factor and four-level orthogonal experiments. Then, the sampled cutting force data increment is transformed into the abrasive wear rate increment by applying the incremental model. Next, five processing parameters for CNC milling are optimized by the gray relational method, which takes the limited abrasive wear rate increment of the flank face and the non-increasing processing time as the constrained conditions. We obtain the relationship between five processing parameters and abrasive wear rate increment. We also find the basic principle of selecting process parameters is to reduce the abrasive wear rate without increasing the processing time. The experimental results verify that the optimized process parameters make the gray relational degree increase by 0.02, and the abrasive wear rate increment decreases by 0.42432 × 10−10 mm3/s without affecting the production efficiency. In the prediction section, by applying the Back Propagation (BP) neural network, we obtain an accurate prediction model from measurable five factors to the abrasive wear increment on the flank of the milling cutter. The maximum error between the predicted value and the actual value is 0.0003, and the predicted value curve fits well with the actual value curve. From the perspective of abrasive wear rate increment prediction, it provides a new idea for online tool wear monitoring.

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Section: Introductionmentioning

confidence: 99%

“…coefficient of abrasive wear rate. The K abr is usually determined by experiments, in this paper, the value of K abr is based on the experimental results of Ding et al10 K abr = 2.37 3 10 211 . The formula is simplified to:…”

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confidence: 99%

Optimization of milling process parameters and prediction of abrasive wear rate increment based on cutting force experiment

Liu

2021

Advances in Mechanical Engineering

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Tool wear mechanism, monitoring and remaining useful life (RUL) technology based on big data: a review

Yang

Liu

et al. 2022

SN Appl. Sci.

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Tool wear is a key factor affecting many aspects of metal cutting machining, including surface quality, machining efficiency and tool life. As machining continues to evolve towards intelligence, hot spots and trends in tool wear-related research are also changing. However, in the current research on tool wear, there are still no recognized most effective tool wear suppression methods, signals are easily disturbed, low efficiency of signal processing methods and poor model generalization ability, etc. Therefore, a comprehensive summary and outlook of tool wear-related research is urgently needed, on the basis of which it is important to predict the hot spots and trends in tool wear research. In this paper, the current state of research on tool wear is systematically described from three aspects: tool wear mechanism, online monitoring and RUL (remaining useful life) prediction, and the shortcomings of tool wear-related research are pointed out. After an in-depth discussion, this paper also foresees the development trends of tool wear related research: (1) tool wear suppression research based on new technologies; (2) online monitoring and RUL prediction technology based on the fusion of data, features and pattern recognition; (3) intelligent, self-learning and self-regulating intelligent machining equipment that integrates multiple objectives (e.g. tool wear, chatter and remaining bearing life, etc.); (4) based on big data, the application of data-driven algorithms in tool wear mechanism, online monitoring and RUL prediction.

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Parameter optimization of titanium alloy considering energy efficiency and tool wear based on RBFNN-MOPSO algorithm in milling

Xi,

Li,

et al. 2024

Journal of Manufacturing Processes

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A Novel Method for Tool Wear Prediction in Titanium Milling by Simulink Feedback Method

Cited by 4 publications

References 2 publications

Optimization of milling process parameters and prediction of abrasive wear rate increment based on cutting force experiment

Optimization of milling process parameters and prediction of abrasive wear rate increment based on cutting force experiment

Tool wear mechanism, monitoring and remaining useful life (RUL) technology based on big data: a review

Parameter optimization of titanium alloy considering energy efficiency and tool wear based on RBFNN-MOPSO algorithm in milling

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