Performance of quality monitor models in spot welding determines the monitor precision directly, so it’s crucial to evaluate it. Previously, mean square error (MSE) is often used to evaluate performances of models, b...Performance of quality monitor models in spot welding determines the monitor precision directly, so it’s crucial to evaluate it. Previously, mean square error (MSE) is often used to evaluate performances of models, but it can only show the total errors of finite specimens of models, and cannot show whether the quality information inferred from models are accurate and reliable enough or not. For this reason, by means of measure error theory, a new way to evaluate the performances of models according to the error distributions is developed as follows: Only if correct and precise enough the error distribution of model is, the quality information inferred from model is accurate and reliable.展开更多
A method was developed to realize quality evaluation on every weld-spot in resistance spot welding based on information processing of artificial intelligent. Firstly, the signals of welding current and welding voltage...A method was developed to realize quality evaluation on every weld-spot in resistance spot welding based on information processing of artificial intelligent. Firstly, the signals of welding current and welding voltage, as information source, were synchronously collected. Input power and dynamic resistance were selected as monitoring waveforms. Eight characteristic parameters relating to weld quality were extracted from the monitoring waveforms. Secondly, tensile-shear strength of the spot-welded joint was employed as evaluating target of weld quality. Through correlation analysis between every two parameters of characteristic vector, five characteristic parameters were reasonably selected to found a mapping model of weld quality estimation. At last, the model was realized by means of the algorithms of Radial Basic Function neural network and sample matrixes. The results showed validations by a satisfaction in evaluating weld quality of mild steel joint on-line in spot welding process.展开更多
文摘Performance of quality monitor models in spot welding determines the monitor precision directly, so it’s crucial to evaluate it. Previously, mean square error (MSE) is often used to evaluate performances of models, but it can only show the total errors of finite specimens of models, and cannot show whether the quality information inferred from models are accurate and reliable enough or not. For this reason, by means of measure error theory, a new way to evaluate the performances of models according to the error distributions is developed as follows: Only if correct and precise enough the error distribution of model is, the quality information inferred from model is accurate and reliable.
基金supported by National Natural Science Foundation of China (No.50275028)
文摘A method was developed to realize quality evaluation on every weld-spot in resistance spot welding based on information processing of artificial intelligent. Firstly, the signals of welding current and welding voltage, as information source, were synchronously collected. Input power and dynamic resistance were selected as monitoring waveforms. Eight characteristic parameters relating to weld quality were extracted from the monitoring waveforms. Secondly, tensile-shear strength of the spot-welded joint was employed as evaluating target of weld quality. Through correlation analysis between every two parameters of characteristic vector, five characteristic parameters were reasonably selected to found a mapping model of weld quality estimation. At last, the model was realized by means of the algorithms of Radial Basic Function neural network and sample matrixes. The results showed validations by a satisfaction in evaluating weld quality of mild steel joint on-line in spot welding process.
