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Advanced materials and new lightweighting technologies are essential for boosting the fuel economy of modern automobiles while maintaining performance and safety. A novel approach called subcritical gas-laden pellet injection molding foaming technology (SIFT) was performed to produce foamed polyamide/glass fiber (PA/GF) composite. Gas-laden pellets loaded with nitrogen (N2) were produced by introducing sub-critical N2 into PA/GF composite during compounding using a twin-screw extruder equipped with a simple gas injection unit. Compared to the commercial microcellular injection molding (MIM) technologies, gas-laden pellets enable the production of foamed parts with a standard injection molding machine, which is more cost-effective and easier to implement. To the best of our knowledge, this is the first attempt that the SIFT technology is being used for the PA/GF composites for making foamed parts. The tensile strength, fiber orientation, cell morphology, and densities of foamed PA/GF parts were investigated, and the shelf life of N2-laden PA/GF pellets was examined. Results showed that the N2-laden pellets still possessed good foaming ability after one week of storage under ambient atmospheric conditions. One week is a noticeable improvement compared to those N2-laden neat polymer pellets without glass fibers. With this approach, the weight reduction of foamed PA/GF parts was able to reach 12.0 wt. %. Additionally, a nondestructive analysis of the fiber orientation using micro-computed tomography suggested that the MIM and SIFT samples exhibited a less degree of fiber orientation along the flow direction when compared to the solid samples and that the tensile strength of both technologies was very close at a similar weight reduction. Cell size increased and cell density decreased as the shelf life increased. These findings showed that this processing method could act as an alternative to current commercial foam injection molding technology for producing lightweight parts with greater design freedom.

The industry is heading towards digitalization with production and quality assurance of processes and products. Novel practices such as machine learning or artificial intelligence make it possible for highly complex and nonlinear occurrences to be modeled and predicted with immense accuracy when real experiences are used to train the algorithm. For example, supervised learning in a closed‐loop system allows the user to analyze and predict outcomes and gives it the ability to adapt and add intelligence to the current system. This study focuses on the development of a neural network (NN) for surface defect prediction in injection molding of model polypropylene. Feature optimization allows us to conclude that rheological parameters such as the melt flow index and relaxation time (λ) can improve predictive accuracy. Furthermore, Bayesian optimization is implemented to optimize the NN structure. The optimization approach allowed for a cross‐validation (CV) accuracy of 90.2% ± 4.4% with only five input parameters, while the seven‐input parameter optimized structure arrived at a CV accuracy of 92.4% ± 11.4%. Although the full‐feature structure optimized with Bayesian optimization concluded with slightly higher accuracy, the error range dramatically increased, meaning that this structure tends to overfit.

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The effect of an in-mold static mixer on orientation of fiber-reinforced polypropylene (PP) was explored within the injection molding process. Several mold geometries and helical mixer designs were assessed via simulation to identify the mixing ability and the potential effect on fiber orientation. It was found that the static