Design of automation system relies on experts’ knowledge and experience accumulated from past solutions. In designing novel solutions, however, it is difficult to apply past knowledge and achieve design right-first-time, therefore wasting valuable resources and time. SADT/IDEF0 models are commonly used by automation experts to model manufacturing systems based on the manual process. However, function generalisation without benchmarking is difficult for experts particularly for complex and highly skilled-based tasks. This paper proposes a functional task abstraction approach to support automation design specification based on human factor attributes. A semi-automated clustering approach is developed to identify key functions from an observed manual process. The proposed approach is tested on five different automation case studies. The results indicate the proposed method reduces inconsistency in task abstraction when compared to the current approach that relies on the experts, which are further validated against the solutions generated by automation experts.

自动化系统的设计依赖于专家从过去的解决方案中积累的知识和经验。在设计新颖的解决方案，但是，它是难以适用过去的知识，实现设计权-第-时间，因此浪费了宝贵的资源和时间。自动化专家通常使用SADT / IDEF0模型来基于手动过程对制造系统进行建模。但是，对于专家而言，没有基准测试的功能推广非常困难，特别是对于复杂且基于高技能的任务。本文提出了一种基于人为因素属性的功能任务抽象方法，以支持自动化设计规范。开发了一种半自动的聚类方法，以从观察到的手动过程中识别关键功能。在五个不同的自动化案例研究中测试了所提出的方法。结果表明，与依赖专家的当前方法相比，该方法减少了任务抽象中的不一致，并且针对自动化专家生成的解决方案进一步进行了验证。