Abstract
A systematic approach for multi-objective optimisation in machine design is presented and further demonstrated through a case study on a delta Coordinate Measurement Machine (CMM). Employing the Non- dominated Sorting Genetic Algorithm II (NSGA-II) [1], the methodology aims to balance competing objectives like measurement accuracy, motion resolution, and machine size. Through an iterative process and simulation- guided parameter refinement, new Pareto optimal solutions are identified at concurrent decision-making steps to reach a final design solution. The results from four concurrent simulations are presented, where each simulation is used to reduce the input range of a specific design variable. The visualizations reveal complex relationships between the design variables and outlier clusters are identified and excluded from the solution space. Furthermore, the results demonstrate how the solution set is systematically reduced to reach a Pareto optimal design. Overall, the proposed process offers a structured framework for addressing the complexities of multi-objective machine design, as evidenced by its successful application in optimising a delta CMM.
Published Version
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