Multi-objective optimization of VBHF in sheet metal deep-drawing using Kriging, MOABC, and set pair analysis

The optimization of variable blank holder force (VBHF) in sheet metal deep-drawing has important significance in improving forming part quality. This paper proposed a new multi-objective VBHF optimization method to reduce the tearing, wrinkling, and springback in the drawing process. The influence m...

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Bibliographic Details
Published inInternational journal of advanced manufacturing technology Vol. 96; no. 9-12; pp. 3127 - 3138
Main Authors Feng, Yixiong, Lu, Runjie, Gao, Yicong, Zheng, Hao, Wang, Yushan, Mo, Wenjia
Format Journal Article
LanguageEnglish
Published London Springer London 01.06.2018
Springer Nature B.V
Subjects
Algorithms
Blankholders
CAE) and Design
Computer-Aided Engineering (CAD
Deep drawing
Engineering
Forming
Industrial and Production Engineering
Kriging
Mechanical Engineering
Media Management
Metal sheets
Multiple objective analysis
Optimization
Original Article
Pareto optimization
Pareto optimum
Quality assessment
Springback
Swarm intelligence
Tearing
Wrinkling
Evaluation criterion of springback
MOABC
VBHF optimization
Kriging approximate model
SPA
Sidewall wrinkling
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Summary:The optimization of variable blank holder force (VBHF) in sheet metal deep-drawing has important significance in improving forming part quality. This paper proposed a new multi-objective VBHF optimization method to reduce the tearing, wrinkling, and springback in the drawing process. The influence mechanism of springback on forming precision is analyzed and a new evaluation criterion of springback is developed. Sidewall wrinkling is taken into account to quantify the wrinkling. Three quality evaluation criteria for ellipsoid forming part are established and a VBHF optimization model in deep-drawing is formulated by cooperated Kriging approximate model with three defects. Multi-objective artificial bee colony (MOABC) algorithm is employed to get VBHF Pareto-optimal set from the VBHF Kriging mode. Set pair analysis (SPA) is applied to select the best solution in Pareto set. An example of the head-type part deep-drawing is used to illustrate the method. The experimental results show that the VBHF multi-objective optimization problem can be solved well by proposed method.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-017-1506-4