Optimization of Copper Electroforming Process Parameters Based on Double Hidden Layer BP Neural Network
In order to optimize the pulse electroforming copper process, a double hidden layer BP (back propagation) neural network was constructed. Through sample training, the mapping relationship between electroforming copper process conditions and target properties was accurately established, and the predi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Micromachines |
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| Online Access: | https://www.mdpi.com/2072-666X/12/10/1157 |
| _version_ | 1797513855806472192 |
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| author | Feng Ji Chao Chen Yongfei Zhao Byungwon Min |
| author_facet | Feng Ji Chao Chen Yongfei Zhao Byungwon Min |
| author_sort | Feng Ji |
| collection | DOAJ |
| description | In order to optimize the pulse electroforming copper process, a double hidden layer BP (back propagation) neural network was constructed. Through sample training, the mapping relationship between electroforming copper process conditions and target properties was accurately established, and the prediction of microhardness and tensile strength of the electroforming layer in the pulse electroforming copper process was realized. The predicted results were verified by electrodeposition copper test in copper pyrophosphate solution system with pulse power supply. The results show that the microhardness and tensile strength of copper layer predicted by “3-4-3-2” structure double hidden layer neural network are very close to the experimental values, and the relative error is less than 2.82%. In the parameter range, the microhardness of copper layer is between 100.3~205.6 MPa and the tensile strength is between 165~485 MPa. When the microhardness and tensile strength are optimal, the corresponding range of optimal parameters are as follows: current density is 2–3 A·dm<sup>−2</sup>, pulse frequency is 1.5–2 kHz and pulse duty cycle is 10–20%. |
| first_indexed | 2024-03-10T06:23:26Z |
| format | Article |
| id | doaj.art-d42226428e084d1189aa6a116d4e81c9 |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-10T06:23:26Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-d42226428e084d1189aa6a116d4e81c92023-11-22T19:10:41ZengMDPI AGMicromachines2072-666X2021-09-011210115710.3390/mi12101157Optimization of Copper Electroforming Process Parameters Based on Double Hidden Layer BP Neural NetworkFeng Ji0Chao Chen1Yongfei Zhao2Byungwon Min3College of Engineering, Mokwon University, Daejeon 35349, KoreaCollege of Engineering, Mokwon University, Daejeon 35349, KoreaCollege of Mechanical Engineering, Nantong University, Nantong 226000, ChinaCollege of Engineering, Mokwon University, Daejeon 35349, KoreaIn order to optimize the pulse electroforming copper process, a double hidden layer BP (back propagation) neural network was constructed. Through sample training, the mapping relationship between electroforming copper process conditions and target properties was accurately established, and the prediction of microhardness and tensile strength of the electroforming layer in the pulse electroforming copper process was realized. The predicted results were verified by electrodeposition copper test in copper pyrophosphate solution system with pulse power supply. The results show that the microhardness and tensile strength of copper layer predicted by “3-4-3-2” structure double hidden layer neural network are very close to the experimental values, and the relative error is less than 2.82%. In the parameter range, the microhardness of copper layer is between 100.3~205.6 MPa and the tensile strength is between 165~485 MPa. When the microhardness and tensile strength are optimal, the corresponding range of optimal parameters are as follows: current density is 2–3 A·dm<sup>−2</sup>, pulse frequency is 1.5–2 kHz and pulse duty cycle is 10–20%.https://www.mdpi.com/2072-666X/12/10/1157double hidden layerBP neural networkelectroformingoptimization |
| spellingShingle | Feng Ji Chao Chen Yongfei Zhao Byungwon Min Optimization of Copper Electroforming Process Parameters Based on Double Hidden Layer BP Neural Network Micromachines double hidden layer BP neural network electroforming optimization |
| title | Optimization of Copper Electroforming Process Parameters Based on Double Hidden Layer BP Neural Network |
| title_full | Optimization of Copper Electroforming Process Parameters Based on Double Hidden Layer BP Neural Network |
| title_fullStr | Optimization of Copper Electroforming Process Parameters Based on Double Hidden Layer BP Neural Network |
| title_full_unstemmed | Optimization of Copper Electroforming Process Parameters Based on Double Hidden Layer BP Neural Network |
| title_short | Optimization of Copper Electroforming Process Parameters Based on Double Hidden Layer BP Neural Network |
| title_sort | optimization of copper electroforming process parameters based on double hidden layer bp neural network |
| topic | double hidden layer BP neural network electroforming optimization |
| url | https://www.mdpi.com/2072-666X/12/10/1157 |
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