J. Mech. Des. Oct 2020, 142(10): 102001 (8 pages)
Paper No: MD-19-1377 https://doi.org/10.1115/1.4046436
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Roham Sadeghi Tabar, Kristina Wärmefjord, Rikard Söderberg, Lars Lindkvist J. Mech. Des. Oct 2020, 142(10): 102001 (8 pages) Paper No: MD-19-1377 https://doi.org/10.1115/1.4046436 The availability of big data has made the role of digital twins in manufacturing more prominent. This paper introduces a geometry assurance digital twin - created from the scanned data of individual components - to define and improve an assembly’s geometrical quality. The joining sequence in a sheet metal assembly impacts geometrical quality and determining the optimal joining sequence is computationally expensive. Meta-heuristic optimization techniques like genetic algorithms often require many simulations, which can increase computational cost. This work improves the optimization process by combining a model-based heuristic algorithm - based on contact displacement minimization – with the meta-heuristic algorithm. Contact modeling avoids part penetration in adjacent areas, and the joining sequences that provide minimal penetration states are used to populate the initial solution for the meta-heuristic algorithm. This approach is demonstrated on two sheet metal assemblies and a reduction in sequence time of 60-80% is achieved. By using a digital twin, optimal joining solutions can be achieved with greater efficiency. For the full article visit ASME's Digital Collection.
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FEATURESThis section includes brief descriptions of articles soon to be or recently published by the Journal of Mechanical Design. These featured articles highlight recent research developments and emerging trends in mechanical design. For Abstracts and Full Articles please see ASME's Digital Collection. Archives
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