Prediction of Process Parameters of New Injection Molding Products Based on Historical Qualified Product Data

Abstract In the field of thermoplastic injection molding, there are a large number of qualified product data, mining and analyzing these data is of great significance to enterprise production. At present, most scholars do optimization research around given process parameters, and cannot fundamentally solve the problem of process parameter setting values. To this end, this article proposes a new method, through the analysis of the material, structure and process of historical qualified products, from the perspective of the product, find the factors that affect the process parameters, and use the BP neural network model to train the non-linear mapping relationship between the product and several main process parameters, and predict the main process parameter values of the new product. For inexperienced manufacturers of injection products, this method can greatly improve their productivity.