Computational human factors tools are often not fully-integrated during the early phases of product design. Often, conventional ergonomic practices require physical prototypes and human subjects which are costly in terms of finances and time. Ergonomics evaluations executed on physical prototypes has the limitations of increasing the overall rework as more iterations are required to incorporate design changes related to human factors that are found later in the design stage, which affects the overall cost of product development. This paper proposes a design methodology based on Digital Human Modeling (DHM) approach to inform designers about the ergonomics adequacies of products during early stages of design process. This proactive ergonomics approach has the potential to allow designers to identify significant design variables that affect the human performance before full-scale prototypes are built. The design method utilizes a surrogate model that represents human product interaction. Optimizing the surrogate model provides design concepts to optimize human performance. The efficacy of the proposed design method is demonstrated by a cockpit design study.
This paper presents a manifesto for the future design of human-made systems and engage this reflection by promoting a computational human factors engineering (HFE) approach. In this paper, we concentrate on the fact that human well-being and sustainability objectives are inseparable. Thus, this paper's primary focus is to demonstrate a simulation-based computational design methodology, Digital Human-in-the-loop (D-HIL), which utilizes the Digital Human Modeling (DHM) platform to inject HFE design principles early in design. The D-HIL framework enables human, product, and systems data to be coupled with computational toolkits; thus, bringing opportunities for facilitating ergonomics decision-making to be part of the Digital Twin driven design. We presented two frameworks originating from our recent work in D-HIL research: (1) Prototyping Toolbox and (2) Human Error and Functional Failure Reasoning (HEFFR) as means for realizing sustainability goals through Digital Twin-based design. While our preliminary studies show that both tools show promise in terms of realizing the sustainability goals, they are limited to only goals that relate to cost. In our future work, we plan to explore models and computational tools from non-traditional engineering domains to expand our frameworks' scope to realizing sustainability goals that go beyond the cost estimations.
Detection of potential failures and human error and their propagation over time at an early design stage will help prevent system failures and adverse accidents. Hence, there is a need for a failure analysis technique that will assess potential functional/component failures, human errors, and how they propagate to affect the system overall. Prior work has introduced functional failure identification and propagation (FFIP), which considers both human error and mechanical failures and their propagation at a system level at early design stages. However, it fails to consider the specific human actions (expected or unexpected) that contributed toward the human error. In this paper, we propose a method to expand FFIP to include human action/error propagation during failure analysis so a designer can address the human errors using human factors engineering principals at early design stages. The capabilities of the proposed method is presented via a hold-up tank example, and the results are coupled with digital human modeling to demonstrate how designers can use these tools to make better design decisions before any design commitments are made.
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