An Association Rule Approach for Identifying Physical System-User Interactions and Potential Human Errors Using a Design Repository
An Association Rule Approach for Identifying Physical System-User Interactions and Potential Human Errors Using a Design Repository
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DOI：
10.1115/detc2019-98424
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发表时间：
2019-08
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影响因子：
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通讯作者：
Nicol´as;F. S. Zurita;M. Tensa;Vincenzo Ferrero;Robert B. Stone;Bryony DuPont;H. Demirel;I. Tumer
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文献类型：
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作者：
Nicol´as;F. S. Zurita;M. Tensa;Vincenzo Ferrero;Robert B. Stone;Bryony DuPont;H. Demirel;I. Tumer
在设计过程中，设计人员必须满足客户需求，同时充分制定工程目标。在这些工程目标中，用户交互、安全性和舒适性等人性化考虑在设计过程中是不可或缺的。然而，传统的设计工程方法在早期设计阶段合并和理解物理用户交互方面存在很大的局限性。例如，人为因素方法使用在后期设计阶段应用于虚拟或物理原型的清单和指南来评估概念。因此，设计人员在不依赖使用详细且昂贵的原型的情况下，很难识别由用户系统交互引起的设计缺陷和潜在故障模式。功能人为误差设计方法 (FHEDM) 是一种使用功能基础方法在早期设计阶段评估物理相互作用的新颖方法。通过应用 FHEDM，设计人员可以通过使用功能模型的信息建立用户-系统关联来识别完成系统功能所需的用户交互，并区分与此类交互相关的故障模式。在本文中，我们探索使用数据挖掘技术来开发组件、功能、流程和用户交互之间的关系。我们从设计存储库中找到的一组不同的咖啡机中提取有关组件、功能、流程和用户交互的设计信息，以构建关联规则。后来，我们使用电热水壶的功能模型，将数据挖掘生成的功能、流程和用户交互关联与作者使用 FHEDM 创建的关联进行了比较。结果显示数据挖掘和 FHEDM 建立的关联之间存在显着的相似性。我们建议来自丰富数据集的设计信息可用于提取功能、流程、组件和用户交互之间的关联规则。这项工作将通过从功能模型自动识别用户交互来为设计社区做出贡献。
During the design process, designers must satisfy customer needs while adequately developing engineering objectives. Among these engineering objectives, human considerations such as user interactions, safety, and comfort are indispensable during the design process. Nevertheless, traditional design engineering methodologies have significant limitations incorporating and understanding physical user interactions during early design phases. For example, Human Factors methods use checklists and guidelines applied to virtual or physical prototypes at later design stages to evaluate the concept. As a result, designers struggle to identify design deficiencies and potential failure modes caused by user-system interactions without relying on the use of detailed and costly prototypes. The Function-Human Error Design Method (FHEDM) is a novel approach to assess physical interactions during the early design stage using a functional basis approach. By applying FHEDM, designers can identify user interactions required to complete the functions of the system and to distinguish failure modes associated with such interactions, by establishing user-system associations using the information of the functional model. In this paper, we explore the use of data mining techniques to develop relationships between component, functions, flows and user interactions. We extract design information about components, functions, flows, and user interactions from a set of distinct coffee makers found in the Design Repository to build associations rules. Later, using a functional model of an electric kettle, we compared the functions, flows, and user interactions associations generated from data mining against the associations created by the authors, using the FHEDM. The results show notable similarities between the associations built from data mining and the FHEDM. We are suggesting that design information from a rich dataset can be used to extract association rules between functions, flows, components, and user interactions. This work will contribute to the design community by automating the identification of user interactions from a functional model.