Wang Lintao   

Associate Professor
Supervisor of Doctorate Candidates
Supervisor of Master's Candidates

Main positions: Associate professor
Other Post: PhD supervisor

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Language:English

Paper Publications

Title of Paper:Surrogate-based weight reduction optimization of forearm of bucket-wheel stacker reclaimer

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Date of Publication:2020-03-01

Journal:STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION

Included Journals:EI、SCIE

Volume:61

Issue:3

Page Number:1287-1301

ISSN No.:1615-147X

Key Words:Weight reduction optimization; Forearm of bucket-wheel stacker reclaimer; Surrogate model; Morris method; Sequential multi-point infill criterion

Abstract:The objective of this paper is to minimize the weight of the forearm of the bucket-wheel stacker reclaimer on the premise of guaranteeing strength, stiffness, and non-resonance. Because the stacking and reclaiming are inseparable from the motion of the forearm, the consumed energy is positively proportional to the self-weight and the forearm supports a lot of loads during the working process; it is necessary to minimize the weight under the requirements of strength, stiffness, and non-resonance. However, it is a high-dimensional problem, and it is of low efficiency when the finite element model is used for optimization. Morris method is used to perform the sensitivity analysis on all the variables. Those with great influence, named main factors, are screened out in order to decrease the number of variables taken into account. Surrogate models are introduced to improve the efficiency of optimization. Herein, Kriging models of the weight, the maximum stress, the maximum displacement, the first-order nature frequency under no-load situation, and the second nature frequency under full-load situation are constructed. In order to improve the model with poor precision, the sequential multi-point infill criterion is introduced. Finally, the weight reduction optimization is performed based on the constructed Kriging models. Compared with the initial design, the weight is reduced greatly. Besides, the Kriging models are of excellent accuracy, which proves that Kriging models or surrogate models are effective substitutes for expensive finite element models to deal with optimization problems.

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