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

开发数据库模型，通过考虑微观结构的人工神经网络 (KNN) 来预测注塑半结晶热塑性塑料的有效机械和热性能

• 批准号: 426052003
• 负责人: Professor Dr.-Ing. Christian Hopmann
• 金额: --
• 依托单位: Institut für Kunststoffverarbeitung (IKV) in Industrie und Handwerk an der RWTH Aachen
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/426052003?language=en
• 关键词: Development; databased; model; prediction; effective

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.

半晶热塑料的局部成分特性的预测是注射成型的重要挑战。为了确定这些属性是该过程的函数，通常进行注射 - 摩尔德模拟。但是，未考虑的是组件的微观结构，这两个模拟是在宏刻度上进行的。这意味着该组件内的属性被认为是均匀的，尽管实际上存在微观结构中的不均匀性。这种不均匀的微观结构会导致局部热热的不均匀性，从而导致注射型半结晶组件的机械性能。因此，在塑料加工研究所（IKV）开发了一个微观结构预测的模型。在合作中，还开发了用于所产生的微观结构均质化的附加模型，从而模拟了有效的局部特性。目前，同质化的执行伴随着高计算成本，这在工业4.0应用的实时预测方面是不切实际的。因此，在该提出的项目的背景下，它试图通过减少模型的模型大幅度减少均质化的仿真时间，而不会使模拟的预测质量恶化。为此，基于数据的方法进行了基于数据的方法，主要是用于适用性。其目的主要是通过数据库模型来构建一个通过pysection和热分析的方法来构建一个模型，以预测一个类似的喷射分析。该项目分为四个工作包。第一个工作包包括定义微观结构表征的参数，可以用作相似性分析的比较变量。在第二个工作包中，使用塑料处理研究所（IKV）开发的软件SPHAEROSIM构建了模拟微结构结构的数据库。另一方面，将创建合成结构的数据库，该数据库可以比模拟的微观结构更快地生成。第三个工作包由两个步骤组成。首先，要确定定义的比较值对有效特性的影响。通过这种方式，可以分类不当的比较值，也可以定义新的比较值。在第二步中，将对来自不同的神经网络进行训练以执行相似性分析。网络类型是复发性神经网络和卷积神经网络。为了确保保证模型的有效性，应在上一个工作包中对其进行验证。为此，对神经网络的结果与原始模型的结果进行了比较，该结果通过微结构的均质化来计算有效属性。