For the lightweight design of heavy-duty glass handling manipulators, an optimization method combining response surface methodology and multi-objective genetic algorithm (moga) was proposed. The dimensions of the robo...
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ISBN:
(数字)9781665467841
ISBN:
(纸本)9781665467841
For the lightweight design of heavy-duty glass handling manipulators, an optimization method combining response surface methodology and multi-objective genetic algorithm (moga) was proposed. The dimensions of the robotic key axes are parameterized by based on the stress and deformation contours obtained from the static analysis. The sample points are obtained by the OSF experimental design method, and the response surface model is constructed to obtain the response surface and sensitivity between each input parameter and output parameter, and to clarify the relationship between each parameter. The moga (Multi-Objective Genetic algorithm) is used to solve the response surface model and obtain a set of optimal design points that can achieve the expected goals. The analysis results show that the optimized mechanical arm's key axis deformation and quality are reduced, and the lightweight design goal is achieved.
With the increasing popularity of mass customization, most manufacturing companies begin to assemble and manufacture products in a mixed-flow production model. To solve the problem of global complete kit extending sta...
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ISBN:
(纸本)9781665437714
With the increasing popularity of mass customization, most manufacturing companies begin to assemble and manufacture products in a mixed-flow production model. To solve the problem of global complete kit extending start time of order, the complexity of complete kit of materials is getting higher and higher, no correlation between learning and forgetting effects, and moga algorithm convergent speed is slow in a manufacturer company, a classified complete kit method is put forward first. Then, an improved learning forgetting effect model that learning factor is associated with forgetting parameter is established. Based on the classified results, the cross and mutation operator of the moga algorithm is improved. A product launch rule is designed to improve the convergent speed of the algorithm. Finally, an example is used to verify the feasibility of the improved model and the improved moga algorithm.
Genetic algorithm is one of the most effective optimization algorithms, on which a lot of studies have been reported. Some studies on the application of island model, which is one of the representative methods to keep...
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ISBN:
(纸本)9781424481262
Genetic algorithm is one of the most effective optimization algorithms, on which a lot of studies have been reported. Some studies on the application of island model, which is one of the representative methods to keep a diversity of solutions, to Multi-Objective Genetic algorithm (moga) have been conducted. In moga, it is difficult to find the solutions which satisfy all objective functions because of their trade-off. Especially when there are many objective functions, it is obvious that it needs a lot of time to search for effective Pareto solutions and find them. This paper proposes the interactive way of addition and deletion of islands to the original ones based on user's requirements with the visualization of acquired solutions in island model for moga. This paper applies the proposed method to Nurse Scheduling Problem (NSP) using the visualization by Principal Component Analysis (PCA). Through the experiment, it is confirmed that an interactive tuning of the weights for the objective functions leaded to the acquisition of better Pareto solutions which a user wants while they are difficult to be acquired by the prepared weights.
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