Integrated capacitance–pressure–temperature sensor for injection molding applications: Shrinkage estimation, process monitoring, and leakage detection

With increasing material variations and demands for process transparency in injection molding, it is essential that sensors evolve beyond machine/cavity condition sensing to material behavior monitoring. A promising solution is to supplement conventional pressure (P) and temperature (T) sensors with a capacitance (C) sensor capable of characterizing in-mold material dielectric properties in real time, thereby monitoring its shrinkage behavior. Unlike traditional data-driven approaches that predict only final shrinkage and require large datasets, this research aims to achieve in-process shrinkage estimation by integration with the capacitive sensor physical model. The method is validated through acrylonitrile butadiene styrene (ABS) and polypropylene (PP) experiments at different pressure settings, showing a low root mean square error of less than 11 μ m for our 2 mm thick part. Through online shrinkage estimation, the P-C correlation reveal the pressure effects on part dimension, thus facilitating pressure setting optimization. With real-time insight into packing effectiveness (under-, well-, over-packed), this approach is more efficient than conventional quality checks method. In addition, abnormal P–C correlations are investigated and found to be associated with injection molding leakage malfunctions. By thoroughly exploring the C-P-T sensor’s potential for shrinkage estimation, process monitoring, setting optimization, and leakage detection, this work sheds new light on the plastic industry automation. • Proposed capacitive method for in-process shrinkage estimation in injection molding. • Estimation method validated via mold-open shrinkage of two distinct materials. • Online thickness estimation enables efficient pressure setting optimization. • Abnormal capacitance-pressure correlation linked to leakage anomalies.