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.

The simultaneous improvement of injection molding process efficiency and product quality, as required by Industry 4.0, is a complex, non-trivial task that requires a comprehensive approach, which involves a combination of sensoring and information techniques. In this study, we investigated the suitability of in-mold pressure sensors to control the injection molding process in multi-cavity molds. We have conducted several experiments to show how to optimize the clamping force, switchover, or holding time by measuring only pressure in a multi-cavity mold. The results show that the pressure curves and the pressure integral are suitable for determining optimal clamping force. We also proved that in-channel sensors could be effectively used for a pressure-controlled SWOP. In the volume-controlled method, only the sensors in the cavity were capable of correctly detecting the end of the filling. We proposed a method to optimize the holding phase. In this method, we first determined the integration time of the area under the pressure curve and then performed a model fit using the relationship between the pressure integral and product mass. The saturation curve fitted to the pressure data can easily determine the gate freeze-off time from pressure measurements.

In all fields of industry it is important to produce parts with good quality. Injection molded parts usually have to meet strict requirements technically and aesthetically. The aim of the measurements presented in our paper is to investigate the aesthetic appearance, such as surface color homogeneity, of injection molded parts. It depends on several factors, the raw material, the colorants, the injection molding machine and the processing parameters. In this project we investigated the effects of the injection molding machine on surface color homogeneity. We focused on injection molding screw tips and investigated five screw tips with different geometries. We produced flat specimens colored with a masterbatch and investigated color homogeneity. To evaluate the color homogeneity of the specimens, we used digital image analysis software developed by us. After that we measured the plastication rate and the melt temperature of the polymer melt because mixing depends on these factors. Our results showed that the screw tips (dynamic mixers) can improve surface color homogeneity but they cause an increase in melt temperature and a decrease in the plastication rate.

We have developed a complex Thermoplastic Resin Transfer Molding procedure to create polyamide 6 composites and their multifunctional coatings in a single production cycle with low cycle time. The advantage of the new process is that it has low emission of volatile organic compounds due to the closed mold and the products can be recycled easily, decreasing the negative impact on the environment. In addition to developing the process steps and parameters, we produced samples with different curing time and fabric content of the base layer, waiting time before coating injection, surface thickness and titanium dioxide content of the surface layer. Then we analyzed the adhesion between the surface and base layers and the warpage of the complex part. SEM analysis proved that the reinforcement fabrics could be properly impregnated with the reactive thermoplastic system during the production of the base layer. Furthermore, we showed that the strength of the interlayer bonding can be improved when the base layer is properly polymerized in the first step. Using our method developed to assess warpage, we demonstrated that the warpage of the base layer can be reduced by applying the appropriate manufacturing parameters to the surface layer.