Multiple In-Mold Sensors for Quality and Process Control in Injection Molding.

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.

Machine Learning in Injection Molding: An Industry 4.0 Method of Quality Prediction.

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.

Personalized Mass Production by Hybridization of Additive Manufacturing and Injection Molding.

The new trend in the composites industry, as dictated by Industry 4.0, is the personalization of mass production to match every customer's individual needs. Such synergy can be achieved when several traditional manufacturing techniques are combined within the production of a single part. One of the most promising combinations is additive manufacturing (AM) with injection molding. AM offers higher production freedom in comparison with traditional techniques. As a result, even very sophisticated geometries can be manufactured by AM at a reasonable price. The bottleneck of AM is the production rate, which is several orders of magnitude slower than that of traditional plastic mass production technologies. On the other hand, injection molding is a manufacturing technique for high-volume production with little possibility of customization. The customization of injection-molded parts is usually very expensive and time-consuming. In this research, we offered a solution for the individualization of mass production, which includes 3D printing a baseplate with the subsequent overmolding of a rib element on it. We examined the bonding between the additive-manufactured component and the injection-molded component. As bonding strength between the coupled elements is significantly lower than the strength of the material, we proposed five strategies to improve bonding strength. The strategies are optimizing the printing parameters to obtain high surface roughness, creating an infill density in fused filament fabrication (FFF) parts, creating local infill density, creating microstructures, and incorporating fibers into the bonding area. We observed that the two most effective methods to increase bonding strength are the creation of local infill density and the creation of a microstructure at the contact area of FFF-printed and injection-molded elements. This increase was attributed to the porous structures that both methods created. The melt during injection molding flowed into these pores and formed micro-mechanical interlocking.

Development and Validation of a Test Mold for Thermoplastic Resin Transfer Molding of Reactive PA-6.

Thermoplastic resin transfer molding (T-RTM) is a cutting-edge manufacturing technique for high-volume production of composites with a recyclable thermoplastic matrix. Although a number of reactive thermoplastic matrices as well as industrial manufacturing equipment for T-RTM are commercially available today, the design of a T-RTM mold is still based on the skills and personal experience of the designer. This study summarizes the best knowledge and expertise in mold design and manufacturing and introduces an innovative mold for T-RTM. A concept and basic principles for designing a T-RTM mold are formulated in this study. The mold developed is manufactured and validated.

Modeling the Thermal Conductivity Inhomogeneities of Injection-Molded Particle-Filled Composites, Caused by Segregation.

Many applications require new materials that have good thermal conductivity, are electrical insulators and can be processed easily and with relatively little energy. A new innovative solution for this problem is thermally conductive composites, which can replace metals in many cases. Many papers have focused on the prediction of their thermal conductivity. At the same time segregation has to be taken into account in the case of composites because it affects the distribution of thermally conductive particles, and thus local thermal conductivities. In this paper, we examined and modeled segregation during injection molding and its effect on thermal conductivity. We injection-molded samples from polypropylene with glass beads of different sizes and analyzed their filler content as a function of the flow path. We described the distribution of the filler with a mathematical model. Using this, we created a new, segregation-dependent model that describes the local thermal conductivity of polymer composites as a function of filler content with great accuracy.

A Review of Thermoplastic Resin Transfer Molding: Process Modeling and Simulation.

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.

In-Mold Sensors for Injection Molding: On the Way to Industry 4.0.

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.

Thermoplastic Overmolding onto Injection-Molded and In Situ Polymerization-Based Polyamides.

We investigated products manufactured by in situ polymerization, which were reinforced with overmolded ribs. We developed our own mold and prototype product for the project. We used three different materials as preform: a material with a magnesium catalyst, manufactured by in situ polymerization, a Brüggemann AP-NYLON-based in situ polymerization material and an injection-molded PA6 (Durethan B30S, Lanxess GmbH) material. The ribs were formed from the same PA6 material (Durethan B30S, Lanxess GmbH). We examined the effect of the different technological parameters through the pull-off of the overmolded ribs. We measured the effect of melt temperature, holding pressure and holding time, and mold temperature. Considering the individual preforms, we pointed out that monomer migration and binding strength are related, which we concluded from the temperature-dependent mass loss of the materials, measured by thermogravimetric analysis (TGA). Finally, we designed a mold suitable for manufacturing overmolded parts. We designed and built pressure and temperature sensors into the mold to examine and analyze pressures and temperatures around the welding zone of the materials.

Enhanced Injection Molding Simulation of Advanced Injection Molds.

The most time-consuming phase of the injection molding cycle is cooling. Cooling efficiency can be enhanced with the application of conformal cooling systems or high thermal conductivity copper molds. The conformal cooling channels are placed along the geometry of the injection-molded product, and thus they can extract more heat and heat removal is more uniform than in the case of conventional cooling systems. In the case of copper mold inserts, cooling channels are made by drilling and heat removal is facilitated by the high thermal conductivity coefficient of copper, which is several times that of steel. Designing optimal cooling systems is a complex process; a proper design requires injection molding simulations, but the accuracy of calculations depends on how precise the input parameters and boundary conditions are. In this study, three cooling circuit designs and three mold materials (Ampcoloy 940, 1.2311 (P20) steel, and MS1 steel) were used and compared using numerical methods. The effect of different mold designs and materials on cooling efficiency were examined using calculated and measured results. The simulation model was adjusted to the measurement results by considering the joint gap between the mold inserts.