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AI-based Computer Aided Engineering for automated product design - A first approach with a Multi-View based classification

机译:基于AI的自动化产品设计计算机辅助工程 - 一种具有基于多视图的分类的第一种方法

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Today’s success of industrial companies is largely determined by engineering competence and the digitization of all corporate processes. The design process and know-how of engineers is strongly individual and a rule-based description of their approach can often not be done at all or only with high effort. Existing knowledge can therefore only be passed on to other engineers with difficulty, which in particular increases the effort required for familiarisation. A further problem is the lack of an overview of existing components within a company, which very often leads to multiple designs and unnecessary waste of time for the engineer.The aim of this approach is to extract the implicit knowledge from existing CAD models with the aid of machine learning methods and thus to make it formalizable. In addition, a suitable classification and similarity analysis should quickly point out existing components. For this purpose, an AI-based assistance system is to be created. Based on the existing database, the assistant first points out to the engineer already existing, but very similar components. For that, the component type currently in construction firstly is identified and then very similar components are searched within the detected scale that are finally suggested to the engineer. The engineer now only has to parameterize the proposed components according to his application. In a further step, the assistant should also be able to suggest useful next design steps, which it has learned on the basis of the CAD data already available and their design history. The implicit experience knowledge that is contained in the existing CAD models thus ensures a design suitable for production and the avoidance of errors in the design.
机译:今天的工业公司的成功基本上由工程能力和所有公司流程的数字化决定。工程师的设计流程和专业知识是强烈的个人,他们的方法的基于规则描述通常不能完全或仅努力完成。因此,只有现有的知识只能通过困难的其他工程师传递给其他工程师,特别是提高了熟悉所需的努力。另一个问题是缺乏公司内部现有组件的概述,这通常会导致多种设计和工程师不必要的浪费时间。这种方法的目的是通过援助从现有的CAD模型中提取隐式知识机器学习方法,从而使其可编程。此外,合适的分类和相似性分析应快速指出现有组件。为此目的,要创建基于AI的辅助系统。基于现有数据库,助手首先指出已经存在的工程师,但非常相似的组件。为此,首先识别目前施工的组件类型,然后在终于向工程师提出的检测量中搜索非常相似的组件。工程师现在只有必须根据他的应用程序参数化建议的组件。在另一个步骤中,助手还应该能够建议有用的下一个设计步骤,它基于已经可用的CAD数据和其设计历史来学习。因此,现有CAD模型中包含的隐式体验知识可确保适合于生产的设计和设计中的错误。

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