Novel Analysis Methodology of Cavity Pressure Profiles in Injection-Molding Processes Using Interpretation of Machine Learning Model

Abstract

The cavity pressure profile representing the effective molding condition in a cavity is closely related to part quality. Analysis of the effect of the cavity pressure profile on quality requires prior knowledge and understanding of the injection-molding process and polymer materials. In this work, an analysis methodology to examine the effect of the cavity pressure profile on part quality is proposed. The methodology uses the interpretation of a neural network as a metamodel representing the relationship between the cavity pressure profile and the part weight as a quality index. The process state points (PSPs) extracted from the cavity pressure profile were used as the input features of the model. The overall impact of the features on the part weight and the contribution of them on a specific sample clarify the influence of the cavity pressure profile on the part weight. The effect of the process parameters on the part weight and the PSPs supported the validity of the methodology. The influential features and impacts analyzed using this methodology can be employed to set the target points and bounds of the monitoring window, and the contribution of each feature can be used to optimize the injection-molding process.

Machine Learning in Injection Molding: An Industry 4.0 Method of Quality Prediction

“…In Equation (12), means the estimate of effect size, corresponds to the -statistic, and is the total number of classifications. If epsilon squared is 1, it means perfect correlation, and if it is 0, it indicates no relationship [ 28 ]. The analysis showed that there is a significant difference between the classifications.…”

“…Several authors confirmed that cavity pressure is a valuable data source that can represent the quality of injection molded products [ 27 , 28 , 29 ]. In a typical cavity pressure profile, several feature points can be extracted that define the characteristics of the injection molding conditions [ 28 , 29 ].…”

“…Several authors confirmed that cavity pressure is a valuable data source that can represent the quality of injection molded products [ 27 , 28 , 29 ]. In a typical cavity pressure profile, several feature points can be extracted that define the characteristics of the injection molding conditions [ 28 , 29 ]. However, the question still remains: which and how many features should be selected from the cavity pressure profile to adequately characterize and predict the quality of injection-molded products?…”

“…For example, Huang et al [ 30 ] used four features from the cavity pressure curve: the peak pressure, the pressure gradient, the viscosity index, and the energy index. Gim and Rhee [ 28 ] proposed five features to be extracted from cavity pressure profiles: the starting point of the filling stage, the switchover point from filling to packing, maximum cavity pressure, the endpoint of the packing stage, and end of the cooling stage. Determining the optimal number of features is a difficult question.…”

Evaluation of the Strength of Slab-Column Connections with FRPs Using Machine Learning Algorithms

“…In addition, most of the existing design models for slab-column connections lack a physical sense, which is due to their being empirical or semi-empirical [4]. On the other hand, machine learning (ML) models can model the behavior with a high level of precision and consistency [8][9][10].…”