Multi-response Optimization Research Of Product Molding Process In SX Company

In the design of the actual product and process optimization, often need to consider multiple quality characteristics, which is multi-response problem, this problem usually does not exist a specific set of input variables to make all response variables at the same time to achieve optimal, multi-response optimization design can effectively improve the quality of the products and generate huge economic benefits, so the multi-response optimization design show an increasingly important position and role in the continuous quality improvement activities.This paper takes multi-response problems of SX company's product molding process as the research object, through the current situation analysis of multi-response problem to find, analysis personnel taken the traditional multiple response optimization method, namely use least squares method (OLS) to fit the model of response variables and the control variables, and then based on the satisfactory degree function to determine the optimal factor level combination. At the same time, via referring to domestic and foreign literatures found, least square method cannot accurately fit the model in the case of more factors, and in the optimal process needs to consider the correlation between responses. In view of the above analysis, in this paper, according to the factor number in the injection molding process, the injection molding process is divided into complex injection molding process and simple injection molding process, at the same time, put forward two kinds of multiple response optimization thought.(1) Multiple response optimizations for Complex injection molding process based on ANN. Calculate satisfaction value of each response variables, use the ANN to fit the model of response variables satisfaction value and the control variable, and conduct to predict; then use the PCA method transfer a series of related variables into irrelevant variables, and get the principal component sequence; after determining principal components optimization direction, use TOPSIS method to determine the optimal factor level combination. (2) Multiple response optimizations for simple injection molding process based on SUR. As the SUR method can either accurately fit model, or solve the correlation between the responses, therefore, use SUR method to fit model, then determine the satisfaction function of each variables and overall satisfaction function, on the basis of these, obtain optimal factor level combination.Finally, conduct test validation for the best combination of factor levels, and compare the response'results on the theory and practice, and then find the response values have been optimized. The fifth chapter proposes management measures and recommendations to promote multi-response optimization.