Inspired by the Industry 4.0 trend towards greater user-friendliness and self-optimization of machines, we present a novel approach to reducing backpressure in foam injection molding. Our method builds on the compressibility of polymer-gas mixtures to detect undissolved gas phases during processing at insufficient backpressures. Identification of a characteristic behavior of the bulk modulus upon transition from homogeneous to heterogeneous polymer-gas mixtures facilitated the determination of the minimum pressure required during production to be determined, as verified by ultrasound measurements. Optimization of the pressure conditions inside the barrel by means of our approach saves resources, making the process more sustainable. Our method yielded a 45% increase in plasticizing capacity, reduced the torque needed by 24%, and required 46% less plasticizing work and lower pressures in the gas supply chain. The components produced exhibited both improved mechanical bending properties and lower densities. From an economic point of view, the main advantages of optimized backpressures are reduced wear and lower energy consumption. The methodology presented in this study has considerable potential in terms of sustainable production and offers the prospect of fully autonomous process optimization.
The optimal machine settings in polymer processing are usually the result of time-consuming and expensive trials. We present a workflow that allows the basic machine settings for the plasticizing process in injection molding to be determined with the help of a simulation-driven machine learning model. Given the material, screw geometry, shot weight, and desired plasticizing time, the model is able to predict the back pressure and screw rotational speed required to achieve good melt quality. We show how data sets can be pre-processed in order to obtain a generalized model that performs well. Various supervised machine learning algorithms were compared, and the best approach was evaluated in experiments on a real machine using the predicted basic machine settings and three different materials. The neural network model that we trained generalized well with an overall absolute mean error of 0.27% and a standard deviation of 0.37% on unseen data (the test set). The experiments showed that the mean absolute errors between the real and desired plasticizing times were sufficiently small, and all predicted operating points achieved good melt quality. Our approach can provide the operators of injection molding machines with predictions of suitable initial operating points and, thus, reduce costs in the planning phase. Further, this approach gives insights into the factors that influence melt quality and can, therefore, increase our understanding of complex plasticizing processes.
We present a novel measurement die for characterizing the flow behavior of gas-containing polymer melts. The die is mounted directly on the injection-molding cylinder in place of the mold cavity and thus enables near-process measurement of viscosity (i.e., under the conditions that would be present were a mold attached). This integration also resolves the issue of keeping gas-containing polymer melts under pressure during measurement to prevent desorption. After thermal characterization of the die, various correction approaches were compared against each other to identify the most suitable one for our case. We conducted measurements using polypropylene in combination with two different chemical blowing agents. Increasing the blowing-agent content to up to 6% revealed an interestingly low influence of gases on melt viscosity, which was confirmed by elongational viscosity measurements. For verification, we compared our results to corresponding measurements taken on a high-pressure capillary rheometer and found that they were in excellent agreement. Our die cannot only be used for rheological characterization. Combined with ultrasound sensors, it provides an innovative way of measuring the volumetric flow rate. This development represents an important step in improving the sustainability of gas-containing polymer processing.
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