The fraction defective of semi-finished products is predicted to optimize the process of relay production lines, by which production quality and productivity are increased, and the costs are decreased. The process par...The fraction defective of semi-finished products is predicted to optimize the process of relay production lines, by which production quality and productivity are increased, and the costs are decreased. The process parameters of relay production lines are studied based on the long-and-short-term memory network. Then, the Keras deep learning framework is utilized to build up a short-term relay quality prediction algorithm for the semi-finished product. A simulation model is used to study prediction algorithm. The simulation results show that the average prediction absolute error of the fraction is less than 5%. This work displays great application potential in the relay production lines.展开更多
Hole-drilling method is a commonly used method for measuring residual stress. The calibration coefficients in ASTM E837-13 a would cause large errors due to the plasticity deformation of materials. In the study, calib...Hole-drilling method is a commonly used method for measuring residual stress. The calibration coefficients in ASTM E837-13 a would cause large errors due to the plasticity deformation of materials. In the study, calibration coefficients were modified in the plasticity deformation stage based on the distortion energy theory. The calibration experiment of calibration coefficients was simulated by the finite element model, and the plasticity modification formulas of 7075 aluminum alloy were obtained. From the results of uniaxial tensile loading test, the measuring errors of high residual stress are significantly reduced from-4.071%～53.440% to-5.140% ～ 0.609% after the plasticity modification. This work provides an effective way to expand the application of hole-drilling method.展开更多
基金funded by Fujian Science and Technology Key Project(No.2016H6022,2018J01099,2017H0037)
文摘The fraction defective of semi-finished products is predicted to optimize the process of relay production lines, by which production quality and productivity are increased, and the costs are decreased. The process parameters of relay production lines are studied based on the long-and-short-term memory network. Then, the Keras deep learning framework is utilized to build up a short-term relay quality prediction algorithm for the semi-finished product. A simulation model is used to study prediction algorithm. The simulation results show that the average prediction absolute error of the fraction is less than 5%. This work displays great application potential in the relay production lines.
基金supported by the Natural Science Foundation of Fujian Provinceof China(No.2018J01082)the China Scholarship Council(No.201806315006)the National Natural Science Foundation of China(No.51305371)
文摘Hole-drilling method is a commonly used method for measuring residual stress. The calibration coefficients in ASTM E837-13 a would cause large errors due to the plasticity deformation of materials. In the study, calibration coefficients were modified in the plasticity deformation stage based on the distortion energy theory. The calibration experiment of calibration coefficients was simulated by the finite element model, and the plasticity modification formulas of 7075 aluminum alloy were obtained. From the results of uniaxial tensile loading test, the measuring errors of high residual stress are significantly reduced from-4.071%～53.440% to-5.140% ～ 0.609% after the plasticity modification. This work provides an effective way to expand the application of hole-drilling method.
