Multi-objective optimization of cutting parameters in sculptured parts machining based on neural network

Li, Li; Liu, Fei; Chen, Bing; Li, Cong Bo

doi:10.1007/s10845-013-0809-z

Cited by 64 publications

(30 citation statements)

References 28 publications

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“…(2) Cutting power should not exceed the effective power transmitted to the cutting point by the machine tool (Li et al 2015):…”

Section: ) Constraintsmentioning

confidence: 99%

A comprehensive approach to parameters optimization of energy-aware CNC milling

Tang

et al. 2016

J Intell Manuf

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Cutting parameters are important components in the process of computer numerical control (CNC) machining, and reasonable choice of cutting parameters can significantly affect the energy efficiency. This paper presents a multi-objective parameter optimization method for energy efficiency in CNC milling process. Firstly, the energy consumption composition characteristics and temporal characteristics in CNC milling are analyzed, respectively. The energy model of CNC milling is then established, of which the correlation coefficient is obtained through nonlinear regression fitting. Then a multi-objective optimization model is proposed to take the highest energy efficiency and the minimum production time as the optimization objectives, which is solved based on Tabu search algorithm. Finally, a case study is conducted to validate the proposed multi-objective optimization model and the optimal parameter solutions of maximum energy efficiency and minimum production time is obtained. Moreover, the parametric influence on specific energy consumption and production time are explicitly analyzed. The experiment results show that cutting depth and width are the most influential parameters for specific energy consumption, and spindle speed ranks the first for the production time.

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“…(2) Cutting power should not exceed the effective power transmitted to the cutting point by the machine tool (Li et al 2015):…”

Section: ) Constraintsmentioning

confidence: 99%

A comprehensive approach to parameters optimization of energy-aware CNC milling

Tang

et al. 2016

J Intell Manuf

Self Cite

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“…Oktem et al [9] developed method for determination of the optimum cutting conditions leading to minimum surface roughness in milling of mold surfaces by coupling response surface methodology with a genetic algorithm. Li et al [10] presents a multi-objective optimization approach, based on neural network, to optimize the cutting parameters in sculptured parts machining. Zain et al [11] used genetic algorithm technique for estimation of the optimal cutting conditions in end milling machining process that yield the minimum surface roughness value.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Machining Parameters With Minimum Surface Roughness for Three-Axis Milling of Sculptured Parts

Cica¹,

Zeljković²,

Sredanović³

et al. 2017

J. Prod. Eng.

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“…The response surface methodology (RSM) and artificial intelligence approach (NN and GA) [16] are used in the process of predicting the forces of milling in the function of material and analysed parameters. For the multicriteria optimization of cutting parameters, the authors [17] use the optimization mathematical model that processes the machining parameters (spindle speed, feed rate, depth of cut and path spacing) as the input parameters, and machining time, use of energy and surface roughness as the output parameters. A prediction model of cutting parameters was made using the NN (Back-Propagation algorithm).…”

Section: Introductionmentioning

confidence: 99%

Use of soft computing technique for modelling and prediction of CNC grinding process

et al. 2016

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Original scientific paper Due to the complexity of grinding process of multilayer ceramics, and the need for a specific product quality, the choice of optimal technological parameters is a challenging task for the manufacturers. The main aim of investigation is to secure the demanded final product quality (plane parallelism) in the function of input parameters (machine, machine operator, foil and production line). "Soft computing techniques" are becoming more interesting to the researchers for the modelling of processing parameters of complex technological processes. In this paper, a soft computing technique, known as the Artificial Neural Networks (ANN), is used for the modelling and prediction of parameters of technological process of CNC grinding of multilayer ceramics. The results show that the ANN with the back-propagation algorithm justifies the application also to this problem. By designing different architectures of ANN (learning rules, transfer functions, number and structure of hidden layers and other) on the set of data from the productiontechnological process, the best result of RMS error (10,76 %) in the process of learning and 12,07 % in the process of validation was achieved. The achieved results confirm the acceptability and the application of this investigation in the technological and operational preparation of production. Keywords: grinding, neural networks, prediction, soft computing Uporaba tehnike mekog računalstva za modeliranje i predviđanje postupka CNC brušenjaIzvorni znanstveni rad Zbog složenosti procesa brušenja višeslojne keramike te osiguranja zahtijevane kvalitete proizvoda, odabir optimalnih tehnoloških parametara je izazovan zadatak za proizvođače. Osigurati traženu izlaznu kvalitetu proizvoda (paralelnost površina) u funkciji ulaznih parametara (stroj, operater stroja, folija i proizvodna linija) predstavlja glavni cilj istraživanja. "Tehnike mekog računalstva" dobivaju pozornost istraživača za modeliranje procesnih parametara složenih tehnoloških procesa. U ovom radu koristi se tehnika mekog računalstva poznata kao umjetne neuronske mreže (ANN) za modeliranje i predviđanje parametara tehnološkog procesa CNC brušenja višeslojne keramike. Rezultati su pokazali da ANN s algoritmom širenja unazad potvrđuje primjenu i na ovaj problem. Oblikovanjem različitih arhitektura ANN (pravila učenja, prijenosne funkcije, broj i strukture skrivenih slojeva i drugi) na setu podataka iz proizvodno -tehnološkog procesa ostvaren je najbolji rezultat RMS greške od 10,76 % u procesu učenja i 12,07 % u procesu validacije. Ostvareni rezultati potvrđuju prihvatljivost i primjenu ovog istraživanja u tehnološkoj i operativnoj pripremi proizvodnje.

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Multi-objective optimization of cutting parameters in sculptured parts machining based on neural network

Cited by 64 publications

References 28 publications

A comprehensive approach to parameters optimization of energy-aware CNC milling

A comprehensive approach to parameters optimization of energy-aware CNC milling

Optimization of Machining Parameters With Minimum Surface Roughness for Three-Axis Milling of Sculptured Parts

Use of soft computing technique for modelling and prediction of CNC grinding process

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