Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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A Study on the Prediction of Welding Flaw Using Neural Network

인공 신경망을 이용한 실시간 용접품질 예측에 관한 연구

  • Cho, Jae Hyung (Department of Industrial Engineering, Dankook University) ;
  • Ko, Sang Hyun (Department of Industrial Engineering, Dankook University)
  • Received : 2019.03.25
  • Accepted : 2019.05.20
  • Published : 2019.05.28
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Abstract

A study in predicting defects of spot welding in real time in automotive field is essential for cost reduction and high quality production. Welding quality is determined by shear strength and the size of the nugget, and results depend on different independent variables. In order to develop the real-time prediction system, multiple regression analyses were conducted and the two dependent variables were obtained with sufficient statistical results with three independent variables, however, the quality prediction by the regression formula could not ensure accuracy. In this study, a multi-layer neural network circuit was constructed. The neural network by 10 dynamic resistance variables was constructed with three hidden layers to obtain execution functions and weighting matrix. In this case, the neural network was established with three independent variables based on regression analysis, as there could be difficulties in real-time control due to too many input variables. As a result, all test data were divided into poor, partial, and modalities. Therefore, a real-time welding quality determination system by three independent variables obtained by multiple regression analysis was completed.

자동차 분야에서 저항 점용접의 결함 및 품질을 실시간으로 예측할 수 있는 연구는 원가절감과 고품질 생산을 위한 필수 불가결한 연구 분야라 할 수 있다. 용접 품질은 전단강도와 너깃의 크기에 의해서 결정되며 여러 가지 독립변수에 따라 결과가 달라진다. 실시간 예측시스템을 개발하기 위하여 다중 회귀분석을 실시하여 3개의 독립변수로 두 가지 종속변수를 충분한 통계적 결과로 구하였으나 회귀식에 의한 품질 예측은 정확도를 보장할 수 없었다. 본 연구에서는 다층 신경망 회로를 구축하였다. 10가지의 동저항 변수에 의한 신경망은 3개의 은닉층을 구축하여 실행 함수와 가중치 행렬을 구하였다. 그러나 이 경우, 입력 변수가 너무 많아 실시간 제어에 어려움이 있을 수 있으므로 회귀분석에 의한 3개의 독립변수로 신경망을 구축하였다. 그 결과 모든 시험데이터를 불량, 부분 불량, 양품으로 구분하는데 성공하였다. 따라서 다중 회귀분석에 의해서 구한 3개의 독립변수에 의한 실시간 용접 품질 판정 시스템을 완성할 수 있었다.

Keywords

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Fig. 1. A Method of Construction of Neural Network for Real Time Monitoring of Spot Welding.

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Fig. 2. Comparisons of Regression Line and Test Variables.

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Fig. 3. 10 Inputs Neural Network Structure

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Fig. 4. Learning Performance of 10 input Neural Network

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Fig. 5. 3 Inputs Neural Network Structure

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Fig. 6. Moving Variables of Network Train.

Table 1. The Result of Multi-Regression Anlalysis for Stress of Spot Welding.

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Table 2. The Result of Multi-Regression Anlalysis for Nugget Diameter of Spot Welding.

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Table 3. The Comparison Results of Forecasting Welding Qualities.

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