Approach of Optimized Planning Process for Exoskeleton Centered Workplace Design

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Abstract

Exoskeleton technology is used in manufacturing [1] to support the manipulation of heavy goods in production specific environments. After selecting the most suitable exoskeleton for each specific workstation [2] the next key step includes the optimization of the workplace design and adjustment of exoskeletons according to the workstation's boundaries, conditions, and constraints. The described impacts with corresponding industry examples and methods should help to understand the influence from these new technology on strategic production objectives, e.g. sustainability, reduced cost, ergonomics, flexibility, quality, and so on. KPIs and their assessment methods are performed at all production scales: beginning from high level for whole production systems, over production processes, production lines and workplace influences, which are particularly used, applied and needed on task-level. Therefore, new exoskeleton based criteria and methods for process planning are discussed intensively. They are used to monitor and especially, to optimize the productions impacts by the planning department and responsible engineers by this presented generic approach. These work present exoskeleton specific impacts define resulting and relevant criteria and corresponding assessments methods. Based on the matching methodology ExoMatch [2] a suitable exoskeleton for the presented example workplace is (pre)selected. With known impacts, the influences and criteria as mentioned before, the novel planning and design approach helps to optimize processes and the workflow by rescheduling and in consideration of suitability, strength and weaknesses of exoskleleton centered workplaces. The content and relevance of this approach is based on experience from industry.

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APA

Dahmen, C., Hölzel, C., Wöllecke, F., & Constantinescu, C. (2018). Approach of Optimized Planning Process for Exoskeleton Centered Workplace Design. In Procedia CIRP (Vol. 72, pp. 1277–1282). Elsevier B.V. https://doi.org/10.1016/j.procir.2018.03.185

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