Support vector regression and genetic-algorithm-based multiobjective optimization of mesoscopic geometric characteristic parameters of ball-end milling tool

Tong, Xin; Liu, Xianli; Shen, Yu

doi:10.1177/0954405420911528

Cited by 6 publications

(5 citation statements)

References 10 publications

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“…Loss functions to establish support vector regression models are the same as literature. 29 Support vector regression machine and MATLAB are used as tools to optimize the evaluation index of micro-texture ball-end milling cutter. The support vector regression machine type is SVM, and the kernel function type is Gaussian kernel function.…”

Section: Optimization Of Target Data Based On Support Vector Machinementioning

confidence: 99%

Chip formation and the interaction of mesoscopic geometric features of cutter

Tong

Ren

Shen

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part B:

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Most of the researches on the properties of micro-textured tools are based on an orthogonal test, while the interaction between micro-textured parameters is ignored. Therefore, this thesis is based on an interaction test to study the cutting performance of cutting tools. According to the chip morphology obtained from the interactive test, the micro texture diameter of 60 μm is obtained when the cutting is stable. It was also found that the synergistic effect of multiple mesoscopic geometric features had a significant influence on cutting performance. By analysis, we found the optimized parameters for the milling tool were D = 60 μm, l = 100 μm, l1 = 150 μm, r = 60 μm. Furthermore, prediction models of the cutting performance were established by univariate linear regression and the validity of these models was verified. Thus, this thesis provides a reference for improving the performance of cutting tools and for achieving efficient and high-quality machining of titanium alloys.

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Section: Optimization Of Target Data Based On Support Vector Machinementioning

confidence: 99%

Chip formation and the interaction of mesoscopic geometric features of cutter

Tong

Ren

Shen

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part B:

Self Cite

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“…Du et al 3 investigate the optimization of high-speed milling process parameters for the new ultra high strength titanium alloy TB17, based on multiple performance characteristics. Tong et al 4 state the optimal mesoscopic geometric characteristic parameters under the four evaluation indices, such as mechanical thermal characteristics, tool wear, and surface quality of the workpiece. Shang et al 5 propose a reliability optimization model of milling process parameters considering the variations of milling speeds and feeds per tooth.…”

Section: Introductionmentioning

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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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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“…33 Accordingly, it has been widely used in the field of machining, such as predicting surface roughness and tool wear. 34,35…”

Section: Introductionmentioning

confidence: 99%

Prediction models for specific energy consumption of machine tools and surface roughness based on cutting parameters and tool wear

Zhao

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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The specific energy consumption of machine tools and surface roughness are important indicators for evaluating energy consumption and surface quality in processing. Accurate prediction of them is the basis for realizing processing optimization. Although tool wear is inevitable, the effect of tool wear was seldom considered in the previous prediction models for specific energy consumption of machine tools and surface roughness. In this paper, the prediction models for specific energy consumption of machine tools and surface roughness considering tool wear evolution were developed. The cutting depth, feed rate, spindle speed, and tool flank wear were featured as input variables, and the orthogonal experimental results were used as training points to establish the prediction models based on support vector regression (SVR) algorithm. The proposed models were verified with wet turning AISI 1045 steel experiments. The experimental results indicated that the improved models based on cutting parameters and tool wear have higher prediction accuracy than the prediction models only considering cutting parameters. As such, the proposed models can be significant supplements to the existing specific energy consumption of machine tools and surface roughness modeling, and may provide useful guides on the formulation of cutting parameters.

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Support vector regression and genetic-algorithm-based multiobjective optimization of mesoscopic geometric characteristic parameters of ball-end milling tool

Cited by 6 publications

References 10 publications

Chip formation and the interaction of mesoscopic geometric features of cutter

Chip formation and the interaction of mesoscopic geometric features of cutter

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

Prediction models for specific energy consumption of machine tools and surface roughness based on cutting parameters and tool wear

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