A performance-based design framework for enhancing decision-making at the conceptual phase of a motorcycle rear suspension development

Caraballo, Sergio Corbera; Fernández, Roberto Álvarez

doi:10.1007/s11081-019-09475-w

Cited by 4 publications

(1 citation statement)

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“…NSGA II is used to solve non-convex and discrete multiobjective problems. Its main features are increased convergence, guaranteeing diversity, and non-dominated sorting without the need for constraints [34,35]. In this study, MOPSO and NSGA II are analyzed comparatively to achieve multi-objective optimization.…”

Section: Figure 3 Teardown Studies and Detailed Component Explanation...mentioning

confidence: 99%

Comparative Design Improvement of the Growing Rod for the Scoliosis Treatment Considering the Mechanical Complications

et al. 2023

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In this study, the focus is on an implant used in the treatment of early-onset scoliosis called magnetically controlled growing rods (MCGR). The primary goal of the study is to address and propose solutions for the mechanical problems reported in the literature concerning MCGR. The problems of the MCGR are mainly due to excessive stress and mechanical bearing problems. Therefore, an MCGR removed from a patient is teardown and geometrically modeled. Then, eleven design parameters are determined on the MCGR for the mechanical problems experienced and these are evaluated by mechanical analysis over 14 control points. In this study, analysis processes are carried out with L12 orthogonal array for eleven design parameters and 2 levels using Taguchi's experimental design method (DoE). With the obtained data by analyzing the experiments in L12, the fitness functions depending on the design parameters are created for 14 control points. Since the problem is multi-objective, a non-dominated sorting genetic algorithm (NSGA II) and multi-objective particle swarm optimization (MOPSO) are used to minimize stress and displacement in existing mechanical problems using fitness functions. The obtained design models from NSGA II and MOPSO are analyzed and evaluated in comparison with the existing mechanical model obtained through preoptimization teardown study of MCGR.

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Section: Figure 3 Teardown Studies and Detailed Component Explanation...mentioning

confidence: 99%