Automated manufacturing machines in the discrete manufacturing domain frequently face changes in requirements, such as volatile customer demands or changes in product variants. Due to this, machines need to become more flexible to cope with these changing conditions. Therefore, manufacturing machines have to undergo adaptation processes during their operational phase. The adaptation processes might include mechanical, electrical, and software changes. In industrial practice, experts individually perform these adaptation processes without methodological support, which is time-consuming and highly error-prone. This article proposes a systematic approach for supporting the different phases of the adaptation process. The producibility check of a production request based on a suitable skill model of the system is addressed as well as the automatic generation of adaptation options. Furthermore, the article provides concepts for analyzing the impact, effort and benefit of the generated adaptation options. Additionally, a multi agent architecture is presented for the implementation of the proposed adaptation approaches. The entire assistance concept was applied to a lab-size production machine to validate the applicability of the approach.

中文翻译：

---

一种支持离散制造机器适应过程的系统方法

离散制造领域中的自动化制造机器经常面临需求变化，例如不稳定的客户需求或产品变体的变化。因此，机器需要变得更加灵活以应对这些不断变化的条件。因此，制造机器在其操作阶段必须经历适应过程。适应过程可能包括机械、电气和软件更改。在工业实践中，专家在没有方法论支持的情况下单独执行这些适应过程，这既耗时又容易出错。本文提出了一种支持适应过程不同阶段的系统方法。解决了基于合适的系统技能模型的生产请求的可生产性检查以及自适应选项的自动生成。此外，本文提供了用于分析生成的适应选项的影响、努力和收益的概念。此外，还提出了一种多代理架构，用于实现所提出的适应方法。整个辅助概念应用于实验室规模的生产机器，以验证该方法的适用性。