Methodology for generating cross-technology metamodels (IKTINO)

生成跨技术元模型的方法（IKTINO）

• 批准号: 441745638
• 负责人: Professor Dr.-Ing. Thomas Bergs
• 金额: --
• 依托单位: Werkzeugmaschinenlabor - WZL
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2020
• 资助国家: 德国
• 起止时间: 2019-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/441745638?language=en
• 关键词: Methodology; generating; cross; technology; metamodels; Methodology; generating; cross; technology; metamodels

For manufacturing companies, it is of high relevance to design the production of components in such a way that the required component characteristics are manufactured with minimized time and cost investments. Until now, the individual processes for manufacturing a component were often considered separately from each other and only merged at higher planning levels. However, the cross-technology consideration of process sequences offers a high optimization potential, because the individual processes and process parameters are thus coordinated in a target-oriented manner. At the same time, increasingly shorter product development cycles mean that process sequences have to be designed in a short time. Therefore, fundamental models and methods are needed that support the rapid determination of economically optimized process parameters for process sequences.So far, no approach has been developed to quickly determine economically optimized process parameters for process sequences, taking into account the dependencies between the individual manufacturing processes and component characteristics. In order to close this research gap, a methodology will be developed in the proposed research project to determine economically optimized process parameters for process sequences on the basis of cross-technology metamodels. Metamodels map input-output correlations for individual manufacturing processes, exclusively resorting to a selection of the most important parameters for the application and thus enabling relevant insights into the design of process sequences within a short period of time. One focus of the proposed research project is therefore the metamodeling of process sequences. Metamodels of individual manufacturing processes are linked to cross-technology metamodels via previously identified transfer variables between the individual processes. Subsequently, the effects of the parameter reductions resulting from the selection of the most important parameters on the prediction accuracy of the overall model will be investigated so that an optimized parameter selection can be made.The second focus of the research project is the economic optimization of cross-technology metamodels. On the one hand, a method will be developed that enables meta cost models to be generated in order to determine the effects of individual process parameters on the costs of the process sequence. On the other hand, a novel optimization algorithm based on genetic algorithms will be developed, which enables the determination of economically optimized process parameters based on cross-technology metamodels and required component characteristics. The developed methodology will then be implemented in software and validated using two case studies.

对于制造公司而言，设计组件的生产具有很高的相关性，即以最小的时间和成本投资制造所需的组件特征。到目前为止，经常将制造组件的单个过程彼此分开考虑，并且仅在较高的计划级别合并。但是，过程序列的跨技术考虑具有很高的优化潜力，因为因此以目标方式协调了各个过程和过程参数。同时，越来越短的产品开发周期意味着必须在短时间内设计过程序列。因此，需要基本模型和方法来支持过程序列的经济优化过程参数的快速确定。因此，考虑到各个制造过程和组件特征之间的依赖关系，尚无开发方法来快速确定过程序列经济优化的过程序列的过程参数。为了缩小这一研究差距，在拟议的研究项目中将开发一种方法，以根据跨技术元模型来确定过程序列经济优化的过程参数。元模型映射单个制造过程的输入输出相关性，仅诉诸于应用程序的最重要参数，从而在短时间内可以对过程序列的设计进行相关的见解。因此，提出的研究项目的重点是过程序列的元模型。单个制造过程的元模型通过先前确定的单个过程之间的转移变量与跨技术元模型有关。随后，将研究由最重要的参数选择对总体模型预测准确性产生的参数减少的影响，以便可以进行优化的参数选择。研究项目的第二个重点是跨技术metamodels的经济优化。一方面，将开发一种方法，该方法能够生成元成本模型，以确定单个过程参数对过程序列成本的影响。另一方面，将开发一种基于遗传算法的新型优化算法，该算法可以根据跨技术元模型和所需的成分特征来确定经济优化的过程参数。然后，开发的方法将在软件中实施，并使用两个案例研究验证。