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

半结晶热塑性塑料局部组分性能的预测是注塑成型中的一个重要挑战。为了确定这些特性与工艺的关系，通常会进行注塑模拟。然而，没有考虑的是组件的微观结构，所提到的两个模拟是在宏观尺度上进行的。这意味着，尽管实际上在微观结构中存在不均匀性，但组件内的性能被认为是均匀的。这种不均匀的微结构导致注塑半结晶部件的局部热性能的不均匀性，并因此导致机械性能的不均匀性。因此，塑料加工研究所（IKV）开发了一种预测微观结构的模型。在合作的一个额外的模型，所得到的微观结构的均匀化也被开发出来，它模拟有效的本地属性。目前，均匀化的执行伴随着高计算成本，这在工业4.0应用的实时属性预测方面是不实际的。因此，在本项目的背景下，试图通过模型简化来大幅减少均匀化的模拟时间，而不会恶化模拟的预测质量。为此，主要考察基于数据的方法的适用性。目的是建立一个基于数据库的模型，该模型可用于通过相似性分析来预测注射成型半结晶热塑性塑料的有效机械和热性能。该项目分为四个工作包。第一个工作包包括定义参数的微观结构的表征，这可以被用作相似性分析的比较变量。在第二个工作包中，使用塑料加工研究所（IKV）开发的软件SphaeroSim建立了模拟微观结构的数据库。另一方面，将创建合成结构的数据库，其可以比模拟的微结构更快地生成。第三个工作包包括两个步骤。首先，确定所定义的比较值对有效属性的影响。以这种方式，可以整理出不适当的比较值，或者可以定义新的比较值。在第二步中，将训练不同的神经网络来执行相似性分析。网络类型是递归神经网络和卷积神经网络。为了保证模型的有效性，应该在最后一个工作包中对其进行验证。为此目的，神经网络的结果与原始模型的结果的比较旨在通过微观结构的均匀化来计算有效性能。

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.