Multi-Response Optimisation of Automotive Door Using Grey Relational Analysis with Entropy Weights
Hao Chen1, Chihua Lu1,2, Zhien Liu1,2
1Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China.
Materials (Basel, Switzerland)
|August 12, 2022
Summary
This study optimized tail-welded blanks (TWBs) for automotive doors, reducing weight by 2.83 kg. The efficient method enhances structural performance and lowers computational costs for lightweight design.
Area of Science:
- Automotive Engineering
- Materials Science
- Structural Optimization
Background:
- Tail-welded blanks (TWBs) are crucial for automotive body lightweighting and structural enhancement.
- Optimizing TWB design is essential for balancing performance and weight reduction in vehicle manufacturing.
Purpose of the Study:
- To perform stiffness and modal lightweight design optimization of TWBs for automotive doors.
- To develop an efficient computational method for TWB structural optimization.
Main Methods:
- Finite element model validation through physical experiments.
- Application of L27 Taguchi orthogonal array for sample point collection.
- Transformation of multi-objective optimization into a single-objective problem using grey relational degree.
Main Results:
- Optimal structural design parameters for a tail-welded automotive door were identified.
- A weight reduction of 2.83 kg was achieved for the optimized door structure.
- The proposed optimization method demonstrated fewer iterations and reduced computational cost.
Conclusions:
- The study successfully optimized TWB design for automotive doors, achieving significant weight reduction.
- The developed grey relational degree-based optimization method is efficient and cost-effective.
- This approach facilitates the design of lightweight and structurally sound TWBs for the automotive industry.
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