This paper focuses on the crystalline structure of injection moulding grade poly(lactic acid) (PLA) and the effect of crystalline structure on the processing. The research is induced by the significant differences in crystallinity of the pure PLA resin, and the injection moulded product, and thus the reprocessing of PLA products. 2 mm thick PLA sheets were injection moulded and re-crystallized in a conventional oven at 60–140°C, for 10–60 minutes to achieve various crystalline contents. The properties of these sheets were investigated by dynamic mechanical analysis (DMA), differential scanning calorimetry (DSC), and wide angle X-ray diffraction (WAXD). In a processing plant the rejected parts are recycled and reused as raw material for further cycles, accordingly the various crystalline content PLA products were reprocessed as a resin, to investigate the processing itself. When PLA products are reprocessed, due to the adherent feature of amorphous PLA processing difficulties may occur. This adherent effect of the amorphous PLA was investigated and characterized

The recent trend in plastic production dictated by Industry 4.0 demands is to acquire a great deal of data for manufacturing process control. The most relevant data about the technological process itself come from the mold cavity where the plastic part is formed. Manufacturing process data in the mold cavity can be obtained with the help of sensors. Although many sensors are available nowadays, those appropriate for in-mold measurements have certain peculiarities. This study presents a comprehensive overview of in-mold process monitoring tools and methods for injection molding process control. It aims to survey the recent development of standard sensors used in the industry for the measurement of in-mold process parameters, as well as research attempts to develop unique solutions for solving certain research and industrial problems of injection molding process monitoring. This review covers the established process monitoring techniques—direct temperature and pressure measurement with standard sensors and with the newly developed sensors, as well as techniques for the measurement of indirect process parameters, such as viscosity, warpage or shrinkage.

A novel method, a 3D printing technique, in particular, acrylic photopolymer material-based Rapid Prototyping (RPT) have been used to 1) fabricate molds for PDMS (PolyDiMethylSiloxane) casting; 2) fabricate RPT-based microfluidics and 3) fabricate RPT-based research instrument platforms. 3D RPT-based molds have been fabricated in order to cast a PDMS flow cell for a Surface Plasmon Resonance (SPR) instrument, and to cast a PDMS chamber for cell lysis in a nanobiological sensor. 3D printing has been utilized to create and test several acrylic photopolymer resin-based prototypes for different microfluidic structures (chaotic mixers, reagent and buffer reservoirs, fluid homogenizers) to be deployed for gynecological cervical sample preparation. Besides, RPT technique has been used also to fabricate platform elements for various microfluidic applications

The production and consumption of polymer composites has grown continuously through recent decades and has topped 10 Mt/year. Until very recently, polymer composites almost exclusively had non-recyclable thermoset matrices. The growing amount of plastic, however, inevitably raises the issue of recycling and reuse. Therefore, recyclability has become of paramount importance in the composites industry. As a result, thermoplastics are coming to the forefront. Despite all their advantages, thermoplastics are difficult to use as the matrix of high-performance composites because their high viscosity complicates the impregnation process. A solution could be reactive thermoplastics, such as PA-6, which is synthesized from the ε-caprolactam (ε-CL) monomer via anionic ring opening polymerization (AROP). One of the fastest techniques to process PA-6 into advanced composites is thermoplastic resin transfer molding (T-RTM). Although nowadays T-RTM is close to commercial application, its optimization and control need further research and development, mainly assisted by modeling. This review summarizes recent progress in the modeling of the different aspects of the AROP of ε-CL. It covers the mathematical modeling of reaction kinetics, pressure-volume-temperature behavior, as well as simulation tools and approaches. Based on the research results so far, this review presents the current trends and could even plot the course for future research.

One of the essential requirements of injection molding is to ensure the stable quality of the parts produced. However, numerous processing conditions, which are often interrelated in quite a complex way, make this challenging. Machine learning (ML) algorithms can be the solution, as they work in multidimensional spaces by learning the structure of datasets. In this study, we used four ML algorithms (kNN, naïve Bayes, linear discriminant analysis, and decision tree) and compared their effectiveness in predicting the quality of multi-cavity injection molding. We used pressure-based quality indexes (features) as inputs for the classification algorithms. We proved that all the examined ML algorithms adequately predict quality in injection molding even with very little training data. We found that the decision tree algorithm was the most accurate one, with a computational time of only 8–10 s. The average performance of the decision tree algorithm exceeded 90%, even for very little training data. We also demonstrated that feature selection does not significantly affect the accuracy of the decision tree algorithm.