Optimization method of machining parameters based on intelligent algorithm
Abstract The processing parameters have a particularly significant impact on the quality and efficiency of processing. Selecting the correct processing parameters can greatly improve the processing performance of the machine tool. To this end, by improving the chromosome structure and genetic operat...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Cai, Jie [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Distributed and parallel databases - Springer US, 1993, 40(2021), 4 vom: 03. Aug., Seite 737-752 |
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volume:40 ; year:2021 ; number:4 ; day:03 ; month:08 ; pages:737-752 |
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| DOI / URN: |
10.1007/s10619-021-07357-8 |
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| 520 | |a Abstract The processing parameters have a particularly significant impact on the quality and efficiency of processing. Selecting the correct processing parameters can greatly improve the processing performance of the machine tool. To this end, by improving the chromosome structure and genetic operators of the GA algorithm, a new GA-BP neural network algorithm is proposed and combined BP neural network method for adaptive crossover and mutation probability optimization. Then, through comparison experiments. After selecting a certain type of CNC EDM machine, find its standard process parameter table and select 50 groups of data as preparation. 30 groups of data are randomly sampled from the inside to serve as training sample data, and the remaining 20 groups serve as performance test samples. Experimental results show that the prediction accuracy of the new algorithm is higher than that of the conventional algorithm, pulse width or peak current. The new prediction results are often closer to the true value, and the prediction accuracy is higher, which can better meet the processing requirements. | ||
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| 700 | 1 | |a Zhao, Weisheng |4 aut | |
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10.1007/s10619-021-07357-8 doi (DE-627)OLC2080009591 (DE-He213)s10619-021-07357-8-p DE-627 ger DE-627 rakwb eng 070 020 004 VZ 24,1 ssgn Cai, Jie verfasserin aut Optimization method of machining parameters based on intelligent algorithm 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract The processing parameters have a particularly significant impact on the quality and efficiency of processing. Selecting the correct processing parameters can greatly improve the processing performance of the machine tool. To this end, by improving the chromosome structure and genetic operators of the GA algorithm, a new GA-BP neural network algorithm is proposed and combined BP neural network method for adaptive crossover and mutation probability optimization. Then, through comparison experiments. After selecting a certain type of CNC EDM machine, find its standard process parameter table and select 50 groups of data as preparation. 30 groups of data are randomly sampled from the inside to serve as training sample data, and the remaining 20 groups serve as performance test samples. Experimental results show that the prediction accuracy of the new algorithm is higher than that of the conventional algorithm, pulse width or peak current. The new prediction results are often closer to the true value, and the prediction accuracy is higher, which can better meet the processing requirements. Intelligent algorithms Processing parameters Heavy-duty CNC machine tools Neural network algorithms Zhang, Wei aut Deng, Jinlian aut Zhao, Weisheng aut Enthalten in Distributed and parallel databases Springer US, 1993 40(2021), 4 vom: 03. Aug., Seite 737-752 (DE-627)165664401 (DE-600)913166-8 (DE-576)038480352 0926-8782 nnns volume:40 year:2021 number:4 day:03 month:08 pages:737-752 https://doi.org/10.1007/s10619-021-07357-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OPC-BBI GBV_ILN_2244 GBV_ILN_4126 GBV_ILN_4318 AR 40 2021 4 03 08 737-752 |
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Abstract The processing parameters have a particularly significant impact on the quality and efficiency of processing. Selecting the correct processing parameters can greatly improve the processing performance of the machine tool. To this end, by improving the chromosome structure and genetic operators of the GA algorithm, a new GA-BP neural network algorithm is proposed and combined BP neural network method for adaptive crossover and mutation probability optimization. Then, through comparison experiments. After selecting a certain type of CNC EDM machine, find its standard process parameter table and select 50 groups of data as preparation. 30 groups of data are randomly sampled from the inside to serve as training sample data, and the remaining 20 groups serve as performance test samples. Experimental results show that the prediction accuracy of the new algorithm is higher than that of the conventional algorithm, pulse width or peak current. The new prediction results are often closer to the true value, and the prediction accuracy is higher, which can better meet the processing requirements. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstractGer |
Abstract The processing parameters have a particularly significant impact on the quality and efficiency of processing. Selecting the correct processing parameters can greatly improve the processing performance of the machine tool. To this end, by improving the chromosome structure and genetic operators of the GA algorithm, a new GA-BP neural network algorithm is proposed and combined BP neural network method for adaptive crossover and mutation probability optimization. Then, through comparison experiments. After selecting a certain type of CNC EDM machine, find its standard process parameter table and select 50 groups of data as preparation. 30 groups of data are randomly sampled from the inside to serve as training sample data, and the remaining 20 groups serve as performance test samples. Experimental results show that the prediction accuracy of the new algorithm is higher than that of the conventional algorithm, pulse width or peak current. The new prediction results are often closer to the true value, and the prediction accuracy is higher, which can better meet the processing requirements. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Abstract The processing parameters have a particularly significant impact on the quality and efficiency of processing. Selecting the correct processing parameters can greatly improve the processing performance of the machine tool. To this end, by improving the chromosome structure and genetic operators of the GA algorithm, a new GA-BP neural network algorithm is proposed and combined BP neural network method for adaptive crossover and mutation probability optimization. Then, through comparison experiments. After selecting a certain type of CNC EDM machine, find its standard process parameter table and select 50 groups of data as preparation. 30 groups of data are randomly sampled from the inside to serve as training sample data, and the remaining 20 groups serve as performance test samples. Experimental results show that the prediction accuracy of the new algorithm is higher than that of the conventional algorithm, pulse width or peak current. The new prediction results are often closer to the true value, and the prediction accuracy is higher, which can better meet the processing requirements. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Optimization method of machining parameters based on intelligent algorithm |
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Zhang, Wei Deng, Jinlian Zhao, Weisheng |
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