个人信息Personal Information
教授
博士生导师
硕士生导师
性别:男
毕业院校:大连理工大学
学位:博士
所在单位:机械工程学院
办公地点:机械东楼
电子邮箱:sunwei@dlut.edu.cn
Surrogate-based weight reduction optimization of forearm of bucket-wheel stacker reclaimer
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论文类型:期刊论文
发表时间:2020-03-01
发表刊物:STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
收录刊物:EI、SCIE
卷号:61
期号:3
页面范围:1287-1301
ISSN号:1615-147X
关键字:Weight reduction optimization; Forearm of bucket-wheel stacker reclaimer; Surrogate model; Morris method; Sequential multi-point infill criterion
摘要: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.