A Novel Sensing Feature Extraction Based on Mold Temperature and Melt Pressure for Plastic Injection Molding Quality Assessment

Injection molding is one of the polymer molding methods. Product quality mainly can be affected by temperature and pressure. To observe the process of the melt forming in the mold, it is required to monitor the signal with the sensor. To achieve the purpose of robust molding, this research provides a new design-process window architecture based on machine learning of the injection product quality assessment. The process window is also known as the forming area map. The monitored value for defective products will be above or below the variable limit. The process is set to the center of this window so that any variation within the window will still result in an acceptable product. There are some potential problems in the current process. First, the conventional process window uses a small number of process variables as monitoring indicators, and it cannot provide stronger monitoring indicators. Second, most of the conventional process windows are 2-D and there are many monitoring windows, so the information that can be monitored is limited. Therefore, product quality cannot be effectively monitored without proper selection of important factors. Finally, the current process window can only provide information in the process and cannot make product quality prediction. In order to solve the above problems, a machine learning method is proposed to design the process window. The random forest classifier and regressor are used to predict the product quality. In order to improve the usability of the factor, the design of the proposed new variable is derived from the classical formula of thermodynamics (heat conduction). Using existing factors and proposing time-based feature changes can better represent the molding process as a whole. Based on the experimental results, the proposed variables can effectively increase the predictive performance of random forest model for quality assessment. The research results make a satisfactory result to the quality assessment of industrial injection molding. The accuracy of the prediction results is 100%. Moreover, it can be found from the 3-D process window that the data are very concentrated and can be clearly classified by the hyperplane, which is defined as the shortest distance between two clusters, and this space is the boundary of the process window. Therefore, compared with the past, the judgment boundary based on the random forest process window is no longer a simple boundary. Random forests can provide more accurate visualization boundaries.