Predictive maintenance feasibility assessment based on nonreturn valve wear of injection molding machines

Abstract

Abstract Predictive maintenance techniques are increasingly important in Industry 4.0; they can be applied to nonreturn valves to mitigate waste from improper replacement. To improve such predictive maintenance, this study used four nonreturn valves with different outer diameters to investigate the effects of wear on process variables and product quality under different process parameters. The results indicated that melt temperature (process parameter) had the most substantial influence on the amount of melt backflow. The process variables are the screw position at the end of the packing stage, the slope of the screw position during the packing stage, and peak pressure. The study investigated the influence of nonreturn valve wear on the entire molding process over different periods. The findings can be extended to prediction models for developing process windows and realizing the predictive maintenance of nonreturn valves in injection molding machines.