Plastic injection molding is a widely used manufacturing process for producing plastic components. However, achieving optimal process stability and part quality remains a persistent challenge due to limited real-time feedback during production. The main objective of this study is to present a method to overcome this limitation by integrating in-mold cavity pressure sensors with an Iterative Learning Control (ILC) strategy to optimize key processing parameters autonomously. The ILC methodology established a closed-loop system; over successive production cycles, cavity pressure profiles were analyzed to automatically adjust the holding pressure, holding time, and switchover point. Each iteration refined the parameters based on sensor data, creating a learning-based optimization loop that accelerated the convergence to optimal settings. The methodology was validated by producing an automotive plastic component. The results demonstrate a 100% success rate in correcting ten critical dimensional errors, fulfilling all part tolerances. Additionally, the overall cycle time decreased by 8%, from 55.0 to 50.6 s. Other findings included updates to key process molding parameters, such as reducing holding pressure from 250 to 230 bar and holding time from 18 to 12 s, as well as increasing the switchover point from 41 to 72 mm. This research confirms that combining real-time cavity pressure monitoring with ILC offers a strong, data-driven framework for significantly improving quality, efficiency, and process stability in injection molding.
García-Sánchez et al. (Sun,) studied this question.