사물인터넷융복합논문지 (Journal of Internet of Things and Convergence)

한국사물인터넷학회 (The Korea Internet of Things Society)
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이중 비밀 다층구조 네트워크에 기반한 전기주조 공정 시스템의 개선

Improvement of Electroforming Process System Based on Double Hidden Layer Network

  • 민병원 (목원대학교 게임소프트웨어공학과)
  • Byung-Won Min (Department of Game Software Engineering, Mokwon University)
  • 투고 : 2023.04.02
  • 심사 : 2023.05.25
  • 발행 : 2023.06.30
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초록

구리의 전기주조 공정을 최적화하기 위하여 이중 비밀 다층구조의 역전파 뉴럴 네트워크가 구성된다. 샘플 학습을 통하여, 구리 전기주조 공정 조건과 목표 특성 간의 함수관계가 정확히 성취되고, 구리 전기주조 공정 내에서 다층구조의 미세강도와 장력에 대한 예측이 이루어진다. 예측된 결과는 펄스 전원공급기를 장착한 구리 피로인산염 솔루션 시스템 내에서 구리의 전해석출 시험에 의하여 증명된다. 그 결과는 다음과 같이 나타난다. "3-4-3-2" 구조의 이중비밀 다층구조 뉴럴 네트워크에 의하여 예측된 구리 다층구조의 미세강도와 장력은 실험값에 매우 근접하며 그 상대적 오차는 2.32%보다 작다. 주어진 파라미터의 범위 내에서, 구리의 미세강도는 100.3~205.6MPa이며, 장력은 112~485MPa 정도로 측정된다. 미세강도와 장력이 최적인 조건에서 그에 대응하는 공정 조건은 다음과 같다: 전류밀도는 2A·dm-2, 펄스 주파수는 2KHz, 펄스의 듀티싸이클은 10%이다.

In order to optimize the pulse electroforming copper process, a double hidden layer BP (Back Propagation) neural network is constructed. Through sample training, the mapping relationship between electroforming copper process conditions and target properties is accurately established, and the prediction of microhardness and tensile strength of the electroforming layer in the pulse electroforming copper process is realized. The predicted results are 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 2.32%. In the parameter range, the microhardness of copper layer is between 100.3~205.6MPa and the tensile strength is between 112~485MPa.When the microhardness and tensile strength are optimal,the corresponding process conditions are as follows: current density is 2A-dm-2, pulse frequency is 2KHz and pulse duty cycle is 10%.

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참고문헌

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