In this research, we are developing a predictive maintenance model of the injection molding machines based on the prediction of trend of injection molding parameters. At first, we developed an interface method to directly monitor the real-time injection molding parameter data from injection molding machine controller. Second, we identified the principal injection parameters which mainly affect the quality of injection molding products and need to be monitored for maintenance. Third, based on the time series analysis, we developed the prediction models of the principal injection molding parameters, which are identified by previous statistical model to forecast its future patterns/trends and schedule its maintenance point in time. We adopted Nelson’s rules to identify abnormal patterns in predicted data. Finally, we used FTA (fault tree analysis) to relate the injection molding parameters to the parts of the injection molding machine, find out the equipment or parts to be corrected.

Cost reduction, time to market, and quality improvement of software product are critical issues to the software companies which try to survive in recent competitive market environments. Software Product Line Engineering (SPLE) is one of the approaches to