## 1 Introduction  
Injection molding is a widely used process for mass production of plastic parts owing to its advantages, such as net shaping of complex geometry parts, allowing the use of various materials, and rapid production times. However, part quality can be influenced by numerous factors, such as material properties, mold design, part geometry, and processing conditions [1; 2; 3; 4]. During the injection molding process, different parts of the molded material are subjected to various thermal-mechanical stresses due to disparities in pressure, temperature, shear stress, and other factors. These variations form different pressure-temperature paths on the pressure-volume-temperature (PVT) diagram. If these paths cannot converge or meet at a common, specific volume value during ejection, they can lead to uneven shrinkage and warping [2; 3; 4]. Achieving high dimensional accuracy and defect-free parts is a challenge for molders in their daily operations since they rely heavily on their molding expertise to conduct mold trials. Although computer-aided engineering simulations conducted in various commercial software programs can assist in identifying more suitable processing conditions, issues such as path differences in pressure and temperature and molding-induced molecular orientation remain. These issues cause anisotropic properties and uneven shrinkage, which are difficult to solve. Previous research has focused on shrinkage issues based on PVT relationships [3; 4; 5; 6; 7; 8; 9; 10]. The influence of factors such as cavity pressure, mold temperature, filling-to-packing switchover, and other factors on shrinkage has also been reported [11; 12; 13; 14]. Generally, the melt temperature, mold temperature, packing pressure, and packing time, including the profiled packing pressure and multistage packing, are critical factors that significantly influence the final part shrinkage. For crystalline polymers, the PVT relationship diagram is even more complicated because of phase changes (Figure 1a) [15]. Figure 1: Schematic of (**a**) a PVT diagram for a crystalline polymer and assumed PT path; (**b**) the change in the PVT curve due to the degree of crystallization in a semi-crystalline polymer. Controlling the extent of molded part shrinkage in crystalline polymers is particularly difficult because the processing conditions also affect the degree of crystallinity (DQC) [16; 17; 18; 19], which plays a significant role in determining part shrinkage. To avoid the ambiguity of DQC-induced uncertainties for shrinkage or PVT behavior (Figure 1b), the solidification process during crystallization is often monitored [17; 18; 19; 20]. Previous studies have reported methods for detecting crystallization. Because of the limitations of contact-type temperature sensors, the detection of crystallization or the associated enthalpy transformation [21; 22; 23] must be combined with a temperature profile simulation within the melt. Usually, this involves assuming the crystallization kinetics [23] and verifying the temperature profile at the cavity surface where the temperature is measured. To solve this limitation, we proposed an infrared-type temperature sensor manufactured by Futaba. The infrared sensor, whose validity was verified in earlier studies [24; 25], can detect the temperature inside the melt (Figure 2), particularly the melt temperature in the center of the cavity gap, where the maximum temperature occurs [26]. By analyzing the variation in the measured melt temperature profile, the enthalpy transformation process and the start and end of crystallization can be determined. The cooling rate decreases during the crystallization process because of the release of latent heat. Upon the completion of crystallization, the cooling rate increases and reverts to the original variation trend. Thus, the crystallization completion time can be identified from the slope of the temperature variation curve. The minimum value, indicating the end of crystallization, can be observed on the measured temperature profile.