Polylactic acid (PLA) is a biobased, biodegradable, non-toxic polymer widely considered for replacing traditional petroleum-based polymer materials. Being a semi-crystalline material, PLA has great potential in many fields, such as medical implants, drug delivery systems, etc. However, the slow crystallization rate of PLA limited the application and efficient fabrication of highly crystallized PLA products. This review paper investigated and summarized the influence of formulation, compounding, and processing on PLA’s crystallization behaviors and mechanical performances. The paper reviewed the literature from different studies regarding the impact of these factors on critical crystallization parameters, such as the degree of crystallinity, crystallization rate, crystalline morphology, and mechanical properties, such as tensile strength, modulus, elongation, and impact resistance. Understanding the impact of the factors on crystallization and mechanical properties is critical for PLA processing technology innovations to meet the requirements of various applications of PLA.

Polylactic acid (PLA) is a versatile and sustainable polymer used in various applications. This research explores the use of orotic acid (OA) and ethylene bis-stearamide (EBS) as nucleating agents to enhance the quiescent crystallization of PLA within the temperature range of 80 °C to 140 °C. Different blends were produced via melt processing before analyzing via DSC, XRD, and SEM. Our results show that both nucleating agents significantly accelerated the crystallization process and reduced the incubation time and the crystallization half-time. The most promising results were obtained with 1% EBS at 110 °C, achieving the fastest crystallization. The XRD analysis showed that at 80 °C, the disordered α’phase predominated, while more stable α phases formed at 110 °C and 140 °C. Combining the 1% nucleating agent and 110 °C promotes densely packed crystalline lamellae. The nucleated PLA exhibited a well-organized spherulitic morphology in agreement with the Avrami modeling of DSC data. Higher nucleating agent concentrations yielded smaller, more evenly distributed crystalline domains. Utilizing OA or EBS in PLA processing could offer enhanced properties, improved processability, and cost-efficiency, making PLA more competitive in various applications.

The increased interest in artificial intelligence in manufacturing has driven the adoption of machine learning to optimize processes and improve efficiency. A key challenge in injection molding is the variability of recycled materials, which affects part quality and processing stability. This study presents a novel closed-loop process control approach for injection molding, leveraging machine learning to adaptively predict processing inputs and quality outcomes. The methodology was tested on five blends of recycled polypropylene (rPP), using artificial neural networks (ANNs), linear regression, and polynomial regression to model the relationships between material properties and process parameters. The dataset was split 80/20 into training and testing sets. The ANN model was implemented using TensorFlow and Keras, with six hidden layers of 32 neurons per layer, ReLU activation, and an Adam optimizer. Empirical tuning and early stopping were used to optimize performance and prevent overfitting. Predictions were evaluated based on mean absolute error (MAE), mean squared error (MSE), and percentage error. The results showed that yield stress, ultimate elongation, and part weight were accurately predicted within a 5% error for linear and polynomial regression models and within a 10% error for the ANN. However, modulus predictions were less reliable, with errors of ~11% for ANN and linear regression and ~40% for polynomial regression, reflecting the inherent variability of this property in rPP blends. Predictions of processing inputs had errors ranging from 3% to 25%, depending on the model and response variable. No single modeling approach was consistently superior across all responses, highlighting the complexity of the relationship between material properties, process parameters, and quality metrics. Overall, the work demonstrates that closed-loop process control, powered by machine learning, can effectively predict key quality parameters in injection molding of recycled materials. The proposed approach can improve process stability and material utilization, facilitating increased adoption of sustainable materials.

This research investigates the correlation between polymer melt viscosity, tensile properties, and injection molding energy consumption for three grades of polypropylene: a virgin grade, a recycled grade, and a modified recycled grade. Cold runner and hot runner molds are considered. The experiments focus on characterizing the thermal and mechanical energy drawn by the injection molding machine during the cycle. The data collected from the experiments are used to calculate the embodied energy as a function of the polymer viscosity and processing conditions. The analysis of the relationship between polymer rheology and processing provided guidelines for the molded parts’ embodied energy and mechanical characteristics. These guidelines and estimation techniques will support sustainable design for manufacturing practices.

This study examines the effects of 2 wt% orotic acid (OA) nucleation and injection molding conditions on the crystallization behavior and thermo-mechanical performance of polylactic acid (PLA). Differential scanning calorimetry and X-ray diffraction revealed that 2 wt.% OA accelerates crystallization, enabling molded PLA to achieve crystallinity levels as high as 52–53% under low packing pressure and long hold time. Mechanical testing showed that tensile modulus increased with longer hold time, while tensile strength decreased due to constrained relaxation in the skin layer. Flexural strength increased with packing pressure, whereas flexural modulus decreased as the degree of crystallinity decreased under higher pressure conditions. Heat deflection temperature (HDT) showed the greatest sensitivity to processing, rising from 58 °C to 100–131 °C in optimized PLA–OA samples. The highest HDT values occurred under conditions that promoted both high crystallinity and extended lamellar development with strong α-phase formation. These results demonstrate that combining OA nucleation with controlled injection molding enables high-crystallinity, high-HDT PLA without post-annealing, offering a viable route for producing thermally stable PLA components suitable for hot-fill and reheatable food packaging applications.