Analyzing and modeling the relation between monitoring information during welding and quality information of the joints is the foundation of monitoring resistance spot welding quality. According to the means of modeli...Analyzing and modeling the relation between monitoring information during welding and quality information of the joints is the foundation of monitoring resistance spot welding quality. According to the means of modeling, the known models can be divided into three large categories: single linear regression models, multiple linear regression models and multiple non linear models. By modeling the relations between dynamic resistance information and welding quality parameters with different means, this paper analyzes effects of modeling means on performances of monitoring models of resistance spot welding quality. From the test results, the following conclusions can be drawn: By comparison with two other kinds of models, artificial neural network (ANN) model can describe non linear and high coupling relationship between monitoring information and quality information more reasonably, improve performance of monitoring model remarkably, and make the estimated values of welding quality parameters more accurate and reliable.展开更多
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
文摘Analyzing and modeling the relation between monitoring information during welding and quality information of the joints is the foundation of monitoring resistance spot welding quality. According to the means of modeling, the known models can be divided into three large categories: single linear regression models, multiple linear regression models and multiple non linear models. By modeling the relations between dynamic resistance information and welding quality parameters with different means, this paper analyzes effects of modeling means on performances of monitoring models of resistance spot welding quality. From the test results, the following conclusions can be drawn: By comparison with two other kinds of models, artificial neural network (ANN) model can describe non linear and high coupling relationship between monitoring information and quality information more reasonably, improve performance of monitoring model remarkably, and make the estimated values of welding quality parameters more accurate and reliable.
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.
