关键词: 注塑成形, 非对称风险, 机器学习, 尺寸预测, 轻量级梯度提升机

Abstract:  Due to the influences of environmental instability factors such as temperature and air pressure during the injection molding processes， the processing parameters were changed during the molding processes， resulting in a decrease in product accuracy， product degradation or scrap. Aiming at the problems of similar environmental instability factors， using the data in the molding processes to predict the sizes of injection molding was helpful for the timely detection of unqualified products and reducing the occurrence of unqualified products. Based on the LightGBM framework， an injection molding product size prediction model was designed based on processing data and parameters. Through feature extraction， abnormal data processing， data set division， model training， model verification， and other steps， a product size prediction model with asymmetric risk characteristics was established. Because of the asymmetric risk of product size exceeding the specification， a weighted correction method was introduced to improve the prediction accuracy of the prediction model for the abnormal size based on the size range in the model training processes. Finally， the Foxconn injection molding size prediction data set was used to verify the prediction model， the results show that the model has higher prediction accuracy for out-of-specification dimensions. The average error of the verification set size prediction results is as 0.015 mm， and the weighted average error considering the asymmetry risk is as 5×10-6 mm. 

Key words:  , injection molding, asymmetric risk, machine learning, product size prediction, light gradient boosting machine （LightGBM）