Tool life prediction based on particle swarm optimization–back-propagation neural network

Xue, Haitao; Wang, Shilong; Yi, Lili; Zhu, Rui; Cai, Bin; Sun, Shouli

doi:10.1177/0954405414538961

Cited by 18 publications

(10 citation statements)

References 21 publications

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“…[12] estimated tool conditions by applying neuro-fuzzy techniques, which yielded the best results for tool-wear estimation with cutting force and machining time variables. [20] developed a model based on particle-swarm optimization that fitted better than the back-propagation neural network for tool-life prediction.…”

Section: Related Workmentioning

confidence: 99%

A statistical data-based approach to instability detection and wear prediction in radial turning processes

Cortadi¹,

Irigoien²,

Boto³

et al. 2018

EiN

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Article citation info: among other events that can damage both the workpiece and the machine tool [13].Tool condition monitoring can track tool wear and thereby predict the Remaining Useful Life (RUL) of the tool, a very important issue for rapid machining processes involving superalloys. Together with the detection of instability, tool monitoring provides a valuable data source for improving the efficiency of superalloy turning processes.In this paper, cutting-force signals are studied for the detection of instabilities during the hard-turning process and a tool-wear prediction model is proposed. The three superalloys tested in this study were as follows: Inconel 718, Haynes 282 and Waspalloy.The structure of this paper will be as follows. In Section 2, related work on radial turning process optimization, tool-wear prediction, and the improvement of tool life will be discussed. In Section 3, the industrial application will be explained and, in Section 4, the methodology used to detect instabilities and to predict tool wear. The results will

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Section: Related Workmentioning

confidence: 99%

A statistical data-based approach to instability detection and wear prediction in radial turning processes

Cortadi¹,

Irigoien²,

Boto³

et al. 2018

EiN

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show abstract

“…The particle swarm optimization (PSO) algorithm has the advantages of fast convergence, few parameters and easy implementation. It can optimize the weight and structure of the neural network, avoid falling into a local optimal solution, and expand the search range and improve the efficiency of calculation [ 21 , 22 ]. In addition, for the selection of time domain and frequency domain, Adpa et al [ 23 ] considered that it was reasonable to select only the time domain response of sound signal over frequency and time–frequency, after consulting a large number of studies.…”

Section: Introductionmentioning

confidence: 99%

Prediction Model of Sound Signal in High-Speed Milling of Wood–Plastic Composites

et al. 2022

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The accuracy of the acoustic signal prediction model for wood–plastic composites milling has an important influence on the condition monitoring of the cutting process and the improvement of the machining environment. To establish a high-precision prediction model of sound signal in the high-speed milling of wood–plastic composites, high-speed milling experiments on self-developed wood–plastic composites were carried out with cemented carbide tools. A mathematical model of the relationship of the four milling parameters, including axial cutting depth, radial cutting depth, feed rate and cutting speed, and the sound signal of wood–plastic composites milling, was established by using the full-factor test method. The experimental data obtained by the orthogonal test method were used as the test samples in the mathematical model. Test results show that the prediction accuracy of the mathematical model of the sound signal in the milling of wood–plastic composites exceeds 95.4%. To further improve the prediction accuracy of the sound signal in the milling of wood–plastic composites, a prediction model was established using back propagation (BP) neural network. Then, the particle swarm optimization (PSO) algorithm was used to optimize the BP neural network, obtaining the PSO–BP neural network prediction model. The test results show that the prediction accuracy of the PSO–BP prediction model for the sound signal in the high-speed milling of wood–plastic composites exceeds 97.5%. The PSO–BP model has a better global approximation ability and higher prediction accuracy than the BP model. The research results can provide a reference basis for sound signal prediction in the high-speed milling of wood–plastic composites.

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“…Thus, the diagnosis of potential faults concealed inside operating mechanisms is meaningful in enhancing the stability of the electrical power supply to consumers. With the advantages of strong self-adaptability, robustness and great fault tolerance, artificial neural networks (ANNs) can store large amounts of precise nonlinear input-output mapping relationships through sufficient training without revealing the mathematical equation [9]. Consequently, ANNs have recently become applied in fault diagnoses [10,11].…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization-Support Vector Machine Model for Machinery Fault Diagnoses in High-Voltage Circuit Breakers

et al. 2020

Chin. J. Mech. Eng.

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According to statistic data, machinery faults contribute to largest proportion of High-voltage circuit breaker failures, and traditional maintenance methods exist some disadvantages for that issue. Therefore, based on the wavelet packet decomposition approach and support vector machines, a new diagnosis model is proposed for such fault diagnoses in this study. The vibration eigenvalue extraction is analyzed through wavelet packet decomposition, and a four-layer support vector machine is constituted as a fault classifier. The Gaussian radial basis function is employed as the kernel function for the classifier. The penalty parameter c and kernel parameter δ of the support vector machine are vital for the diagnostic accuracy, and these parameters must be carefully predetermined. Thus, a particle swarm optimizationsupport vector machine model is developed in which the optimal parameters c and δ for the support vector machine in each layer are determined by the particle swarm algorithm. The validity of this fault diagnosis model is determined with a real dataset from the operation experiment. Moreover, comparative investigations of fault diagnosis experiments with a normal support vector machine and a particle swarm optimization back-propagation neural network are also implemented. The results indicate that the proposed fault diagnosis model yields better accuracy and efficiency than these other models.

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Tool life prediction based on particle swarm optimization–back-propagation neural network

Cited by 18 publications

References 21 publications

A statistical data-based approach to instability detection and wear prediction in radial turning processes

A statistical data-based approach to instability detection and wear prediction in radial turning processes

Prediction Model of Sound Signal in High-Speed Milling of Wood–Plastic Composites

Particle Swarm Optimization-Support Vector Machine Model for Machinery Fault Diagnoses in High-Voltage Circuit Breakers

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