Abstract
Thermoplastic injection molding is a widely used process for producing complex three-dimensional plastic parts with tight dimensional tolerances. A key determinant of part quality is the switchover point-the transition from velocity-controlled filling to pressure-controlled packing. This transition affects critical product attributes, such as d imensional accuracy, weight consistency, and surface finish. Precise control of the switchover point enhances process stability, robustness, and adaptability. This review consolidates recent advancements in switchover methods and adaptive control techniques. Improvements in traditional methods include the use of pressure gradient detection to mitigate viscosity variations and adaptive control to refine stroke- and time-dependent switchovers. In addition, deformation-based strategies detect the mold-opening force associated with cavity pressure through clamping force, mold separation, or tie-bar elongation. The integration of machine learning and feature extraction techniques enables the real-time adjustment of the switchover point by mapping relationships between process parameters and quality criteria. In addition, ultrasonic sensors provide non-invasive melt front detection, reducing the risk of mold damage. Real-time simulations, updated through nozzle pressure feedback, complement these methods to achieve precise switchover timing. This review also identifies persistent challenges, such as sensitivity to material properties, machine wear, and environmental conditions, and it explores future directions for improving the accuracy and adaptability of switchover control in modern injection molding processes.