Development of a databased model for the prediction of effective mechanical and thermal properties of injection-moulded semi-crystalline thermoplastics by means of an artificial neural network (KNN) taking into account the microstructure

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The prediction of the local component properties of semi-crystalline thermoplastics is an important challenge in injection moulding. In order to determine these properties as a function of the process, injection-moulding simulations are often carried out. However, what is not taken into account is the microstructure of the component, the two mentioned simulations take place on the macro scale. This means that the properties within the component are considered as homogeneous, although actually there exists inhomogeneity in the microstructure . This inhomogeneous microstructure causes an inhomogeneity in the local thermal and hence mechanical properties of an injection-moulded semi-crystalline component. Therefore, a model for the prediction of the microstructure was developed at the Institute of Plastics Processing (IKV). In a cooperation an additional model for the homogenization of the resulting microstructure was also developed, which simulates the effective local properties. Currently the execution of the homogenization is accompanied by high computational cost, which is not practical in terms of real-time prediction of properties for Industry 4.0 applications. Therefore, in the context of this proposed project, it is attempted to reduce drastically the simulation time of the homogenization by means of model reduction without worsening the prediction quality of the simulation.. For this purpose data-based approaches are examined for applicability primarily.The aim is to build a databased model, which can be used to predict the effective mechanical and thermal properties of injection-moulded semi-crystalline thermoplastics by means of a similarity analysis. The project is divided into four work packages. The first work package consists of defining parameters for the characterisation of microstructure, which can be used as comparative variables for the similarity analysis. In the second work package, a database of simulated microstructure structures is built up using the software SphaeroSim developed at the Institute of Plastics Processing (IKV). On the other hand, a database of synthetic structures is to be created, which can be generated much more quickly than the simulated microstructures. The third work package consists of two steps. First, the influence of the defined comparative values on the effective properties is to be determined. In this way, inappropriate comparative values can be sorted out or new comparative values can be defined. In the second step, different neural networks from will be trained to perform the similarity analysis. The network types are a Recurrent Neural Networks and a Convolutional Neural Network. To ensure that the validity of the model is guaranteed, it should be validated in the last work package. To this end, a comparison of the results of the neural networks with the results of the original model is aimed at, which calculates the effective properties by a homogenization of the microstructure.