The goal of this research is to formulate a computational design methodology that integrates human factors principles early in the design through a functional failure analysis of complex systems. This…

This is a $141,826.95 grant for research awarded by the National Aeronautics and Space Administration (NASA) to Oregon State University, a public research university in Corvallis, Oregon. The goal of the research is to develop a computational design methodology that integrates human factors principles early in the design process through functional failure analysis of complex systems. The research aims to optimize human well-being and overall system performance by recognizing humans as a fundamental part of complex systems. The proposal introduces a human-in-the-loop design framework to validate error mitigation strategies and reduce latent and catastrophic failures early in the design stage. This grant does not have a set-aside designation. Oregon State University has extensive experience partnering with federal agencies like NASA, the Department of Defense, and the Department of Energy on a variety of research and development contracts and grants.

The goal of this research is to formulate a computational design methodology that integrates human factors principles early in the design through a functional failure analysis of complex systems. This research places human users as a fundamental part of complex systems and optimizes human well-being as well as the overall systems performance. Design changes made at later design stages, during or after prototyping, can be very costly and time consuming compared to design changes made during conceptualization. Often, detailed models of system components and parameters are not available at early design stages. Instead, intended system functions exist in the form of functional models. Thus, utilizing the functional representation of systems, specifically human-machine interactions, at early design stages, can help prevent failures. Existing failure analysis methods fail to provide insight on how human-machine interactions cause failures, and how these failures propagate and affect the system overall. This proposal aims to formulate a computational failure analysis methodology to identify what human actions contribute towards producing errors. To that end, this proposal recognizes humans as a fundamental part of the complex systems and introduces a human-centered methodology to identify potential failure scenarios early in design using functional failure analysis approach. The proposal introduces a human-in-the-loop design framework to validate error mitigation strategies to reduce latent and catastrophic failures early in design. The specific objectives are to: (1) generate action sequence graphs, (2) create action classifications, (3) develop action simulations, and (4) validate and derive mitigate failure mitigating strategies through human-in-the-loop evaluation. The proposed method integrates system model, CAD models and anthropometric DHM mannequins to recreate a design scenario. We will use design examples (e.g., provided by NASA aosp) as a case study. The proposed research is especially important for the identification and validation of human errors in large-scale systems, where human-subject data collection and construction of full-scale prototyping is costly, time-consuming or hardly possible. This proposal utilizes digital human modeling (DHM) to inject human factors engineering principles into functional failure analysis to assist designers in understanding failure scenarios, human errors, their propagation over time, and their combined effects on the overall system. In the case of designing complex products, where humans are an integral part of the systems both as designers, users, and maintainers, identifying how human errors occur and how they propagate throughout the systems are critical steps towards formulating mitigation strategies. The ultimate goal of the proposed research is to integrate human-factors design principles with functional failure analysis into a computational design and prototyping environment to identify and validate human-product interactions early in the design of large-scale systems.