A frequency-based analysis of cutting force for depths of cut identification in peripheral end-milling

Grossi, Niccolò; Morelli, Lorenzo; Scippa, Antonio; Campatelli, Gianni

doi:10.1016/j.ymssp.2022.108943

Cited by 21 publications

(2 citation statements)

References 30 publications

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“…With the similar method, Jiang et al [4] built a model for the ball-end milling cutter. Differently, Grossi et al [5] built the model based on frequency characteristics of the milling force. In order to obtain accurate milling force mechanism model, the above methods need to calibrate milling force coefficient through experiments, which reduced its generalization and application.…”

Section: Introductionmentioning

confidence: 99%

Physics-informed interpretable machine learning method for DOC monitoring in peripheral milling

Li,

Zheng,

Jiang

et al. 2024

Int J Adv Manuf Technol

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On-line monitoring of depths of cut (DOC) is an essential way to avoid machining defects, such as over-cutting and machining chatter. Data-driven machine learning algorithm play a principal role to realize the monitor. However, deficient training data and un-interpretable algorithm made it difficult to application. Therefore, a physics-informed interpretable machine learning algorithm was proposed. Firstly, a physics simulation model was established with DOC, rotation speed and feed speed as its inputs and time-domain signals of milling force as its outputs. The output force signals were quantitatively presented by the characteristic values of time domain, frequency domain and waveform parameters. Secondly, six dimensionless characteristics, namely the kurtosis, skewness, waveform factor, peak factor, pulse factor and margin factor of the resultant milling force, were explored through sensitivity analysis method. They were sensitive to DOC but insensitive to milling force coefficient, speed and feed speed. Then, a quantitative relationship model between features and DOC was established by using the least squares linear regression algorithm, which has an intrinsic interpretability. The model was trained by a labeled 100 groups test data. The experiments show that the accuracy of the proposed model for DOC monitoring is greater than 90%.

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Section: Introductionmentioning

confidence: 99%

Physics-informed interpretable machine learning method for DOC monitoring in peripheral milling

Li,

Zheng,

Jiang

et al. 2024

Int J Adv Manuf Technol

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show abstract

“…Quality controls of machining system have attracted widely attentions with the development of manufactures (Grossi et al, 2022;Zhou et al, 2016aZhou et al, , 2016b. Cutting force prediction for CNC machine tools is one of the most important target with the quality controls (Guo et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A method of milling force predictions for machining tools based on an improved ARMA model

Gao

Zhou

et al. 2023

AEAT

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Purpose Cutting force prediction is pretty important for manufacture management. Thus, the purpose of this paper is to obtain the cutting force of the machining process with high efficiency and low cost. A method based on the improved auto regressive moving average (ARMA) model is proposed for cutting force predictions in milling process. Design/methodology/approach First, classification and normalization are made for initial cutting force. Second, the cutting force sequences are compressed followed singular and valid value removed. At last, the improved ARMA model is used for cutting force fit and extrapolation considered the time domain characteristics. Findings A series of cutting force with the spindle speed 595r/min is carried out in the research. It is showed that the mean absolute percentage error value of cutting force extrapolation results which is based on the improved model is smaller. The percentage value is approximately 5.80%. Then the root mean square error test value is only 72.49, which is smaller than that with other traditional method, such as hidden Markov model. The extrapolation results with the proposed model performed good consistency and accuracy in terms of peaks, valleys and volatility compared with the experiment results. Originality/value The proposed method that is based on the improved ARMA model can be used for cutting force predictions conveniently. And the predictions can be used for improving the qualities in milling process.

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Milling Cutting Force Model Including Tool Runout

Morelli,

Grossi,

Scippa

2023

Lecture Notes in Mechanical Engineering

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A frequency-based analysis of cutting force for depths of cut identification in peripheral end-milling

Cited by 21 publications

References 30 publications

Physics-informed interpretable machine learning method for DOC monitoring in peripheral milling

Physics-informed interpretable machine learning method for DOC monitoring in peripheral milling

A method of milling force predictions for machining tools based on an improved ARMA model

Milling Cutting Force Model Including Tool Runout

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