Optimizing the design of automotive S-rail using grey relational analysis coupled with grey entropy measurement to improve crashworthiness

Proceedings of the Institution of Mechanical Engineers, Part D:

et al. 2018

Self Cite

This study deals with the multi-objective lightweight optimization of the front end structure of an automobile body, as the main assembly to withstand impact force and protect occupants from injuries in frontal collision, based on entropy-based grey relational analysis (EGRA). First, basic noise, vibration, and harshness (NVH) models of the automobile body and crashworthiness models of the vehicle are established and then validated by corresponding actual experiments; hence the lightweight controlling quotas are extracted. Next, the contribution analysis method determines the final parts for lightweight optimization, for which both continuous thickness variables and discrete material variables are simultaneously taken into account. Subsequently, design of experiment (DoE) using the optimal Latin hypercube sampling (OLHS) method is carried out, considering the total mass and the torsional stiffness of the automobile body, the maximum intrusion deformation on the firewall, the maximum impact acceleration at lower end of the B-pillar, and the total material cost of the selected optimization parts as five competing optimization objectives. After that, the optimal combination of thickness and material parameters of the optimization parts is determined using EGRA and confirmed by technique for order preference by similarity to ideal solution (TOPSIS). Finally, a comparison between the original design and the post-lightweight design, namely the optimized design, further confirms the effectiveness of the lightweight optimization. According to the outcomes, the automobile body is lightweight optimized with a mass decrease of 4.98 kg on the basis of well guaranteeing other relevant mechanical performance. Accordingly, the EGRA could be well employed to the multi-objective lightweight optimization of the automobile body.

Section: Entropy Weight Methodsmentioning

confidence: 99%

“…51 Recently, a portion of studies have made innovative attempts to employ the entropy method to GRA. 54–58…”

Section: Methodsmentioning

confidence: 99%

Lightweight optimization of the front end structure of an automobile body using entropy-based grey relational analysis

Xiong

Proceedings of the Institution of Mechanical Engineers, Part D:

et al. 2018

Self Cite

“…The grey theory [27] is a new strategy of the multiobjective optimization. The main idea of the grey theory is that when a multi-objective optimization is performed, the optimal solution of each single-objective optimization is as a reference sequence and the sequence is optimized as an objective sequence.…”

Section: Multi-objective Optimization Modelmentioning

confidence: 99%

Multi-objective parameters optimization design of self-excited oscillation pulsed atomizing nozzle

J Braz. Soc. Mech. Sci. Eng.

Sun

Chen

2019

The self-excited oscillation pulsed atomizing nozzle can effectively and evenly spray the high-speed solid cone jet without any extra power. The primary atomization of the jet at the outlet of nozzle directly affects the final spray quality, and the turbulence and cavitation at the outlet of the atomizing nozzle are the other two main factors affecting the atomization. In this work, multi-objective optimization depending on nozzle parameters was established by using mathematical optimization techniques and computational fluid dynamics to improve the jet atomization quality at the outlet of the nozzle. The central composite design method and the response surface method were used to obtain the approximate mathematical model of the primary atomization quality of the jet at the outlet of nozzle. Finally, the non-dominated sorting genetic algorithm with elitist strategy (NSGA-II) and the grey theory were used in combination to optimize the nozzle parameters. Through combining with the NSGA-II and the grey theory, the nozzle parameters were optimized in order to obtain the best primary atomization at the outlet area of nozzle. The optimization results verified the nozzle design with multi-objective optimization method. The optimized values of the turbulent kinetic energy F 1 and the vapor volume fraction F 2 increased by 28.26% and 5.56%, respectively, and the corresponding nozzle parameters of the chamber diameter D, the lower nozzle diameter d 2 and the upper nozzle inlet pressure P in were, respectively, optimized to 28.056 mm, 5.472 mm and 3.999 Mpa.

“…To present precisely various materials in different parts that have superior crashworthiness and lightweight performance, various part models are analysed and compared by Hosseini-Tehrani and Nikahd. 5 Experiment and model simulation on automobiles based on various types of material are conducted by Zhou et al, 6 Cai and Wang, 7 Long, 8 and Meschut et al 9 The experimental test and simulation models are analysed and then compared to verify the accuracy of the numerical model. Therefore, the various types of materials for different parts have the potential to be adopted in improving lightweight and crashworthiness of automobiles.…”

Section: Introductionmentioning

confidence: 99%

A multi-objective optimization approach for simultaneously lightweighting and maximizing functional performance of vehicle body structure

Proceedings of the Institution of Mechanical Engineers, Part D:

Xie

2019

Self Cite

This study presents a hybrid approach to integrate the comprehensive sensitivity analysis method, support vector machine technology, modified non-dominated sorting genetic algorithm-II method and the technique for order preference by similarity to ideal solution, which have been applied to multi-objective lightweight optimization of the B-pillar structure of an automobile. First, numerical models of the static–dynamic stiffness and the crashworthiness performance of automobile are established and validated by experimental testing. Then, the comprehensive sensitivity analysis method is used to define the final optimization variables. Experimental design and support vector machine based surrogate model techniques are introduced to establish the approximate model; subsequently, the modified non-dominated sorting genetic algorithm-II algorithm is applied to the multi-objective lightweight optimization design of the B-pillar structure, and the non-dominated solution set is determined. The principal component analysis method is applied to determine the weight of each objective. Finally, the technique for order preference by similarity to ideal solution method is used to rank Pareto front from best to worst to obtain the optimal solution; furthermore, a comparison between the original model and optimized design denotes that the mass of the B-pillar being reduced by 22.55% under the other impacting indicators is well guaranteed. Therefore, the proposed hybrid approach provided promising prospects in the lightweight and crashworthiness optimization application of the B-pillar.