Optimal Lightweight Material Selection for Automobile Applications Considering Multi-Perspective Indices

Pu, Yongfeng; Ma, Fangwu; Zhang, Junyuan; Yang, Meilian

doi:10.1109/access.2018.2804904

Cited by 33 publications

(12 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…They illustrated the efficiency of greybased fuzzy logic when solving complex material selection problems. Using a hybrid evaluation approach (G-TOPSIS) that integrated GRA and TOPSIS, Pu et al 114 monitored the robustness of solution ranking for material selection. Similarly, Tian et al 115 considered the robustness of ranking materials.…”

Section: Optimisation and Mathematical Methodsmentioning

confidence: 99%

A systematic review on material selection methods

Rahim

Musa

Ramesh

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part L:

View full text Add to dashboard Cite

Technological advancements and the growing material set in the world have created a large variety of options for industrial designers, but little attention has been given to the tools and methods that support material selection processes. In this paper, we report on a comprehensive systematic literature review (SLR) guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement on methods or approaches reported for supporting material selection processes. This review covers various published literature, spanning from 2000 to 2018. The purpose is to examine in detail the evolution of the extensive body of research, its research streams and to position possible areas for further research. A classification framework consisting of six categories of selection approaches was derived from the extensive literature. Additionally, a detailed analysis of predominant approaches was presented along with their advantages and limitations with respect to the material selection domain. Taken together, the insights gained from this study may be of assistance to new researchers and practitioners who are looking for potential selection methods for their specific applications. The review also found that there is an increasing trend of research in recent years in the area of OR-based method application specifically on the multi-criteria decision-making supporting material selection processes.

show abstract

Section: Optimisation and Mathematical Methodsmentioning

confidence: 99%

A systematic review on material selection methods

Rahim

Musa

Ramesh

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part L:

View full text Add to dashboard Cite

show abstract

“…Pu et al [28] put forward a comprehensive hierarchical structure with systematic multiperspective indices and applied a hybrid approach with integration of grey relational analysis (GRA) and technique for order preference by similarity to ideal solution (TOPSIS) for the determination of optimal lightweight material for vehicles. Xiong et al [29] presented an approach combining grey relational and principal component analysis for lightweight optimization of the side structure of the automobile body.…”

Section: Literature Reviewmentioning

confidence: 99%

Material Selection of Green Design Processes for Car Body via considering Environment Property

Han

et al. 2020

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

Facing serious environmental degradation and its resulting of climate warming, how to conserve energy and reduce emissions becomes a serious issue for government supervisors and modern vehicle enterprises. Reducing the mass of a vehicle is one of the most effective ways to reduce emissions and improve fuel utilization, essential to persist the low-carbon and sustainable-development bases in industrial production processes. When it comes to the selection of lightweight material for a car body in the processes of vehicle production, it is essential to comprehensively evaluate multiple relevant attributes in order to select the optimal material from several alternatives. us, it can be seen as a multicriterion decision-making (MCDM) problem. However, it is difficult to consider both the uncertainty of the expert's preference and the imprecision of the attribute estimate. Considering this, this paper uses the method integrating grey relational analysis (GRA) with analytic hierarchy process (AHP) to solve the problem of lightweight material selection for a car body. e AHP method is used to determine the weight of each attribute, and the GRA method is to select the optimal material among several alternatives. Finally, a case study is applied to verify the practicability of the proposed approach. e result shows that the proposed multicriterion decision method provides a precise and objective foundation for making decisions about the material selection issue.

show abstract

“…A higher hardness value indicates that the sheared edge undergoes severe strain hardening, which in turn means that the residual local formability is low to withstand the edge stretching during stamping and flanging process. To examine the level of the damage in the shear edge, an edge strain hardening (ESH) index is adopted as defined in (2) where HVas-received indicates the HV of as received material measured away from the sheared edge.…”

Section: Materials Hardening Datamentioning

confidence: 99%

“…GPa-grade steels which exhibit an ultimate tensile strength of at least 1,000 MPa have been widely applied to body-inwhite (BIW) structures to improve their crash worthiness and reduce their weight [1][2][3]. However, undesired fractures frequently occur during the stamping and flanging process due to the poor formability and sheared edge stretchability of GPa-grade steels, which restricts their wide application in BIW panels [4][5].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network for Predicting Edge Stretchability in Hole Expansion Test With Gpa-Grade Steel

2020

View full text Add to dashboard Cite

This paper mainly proposes an artificial neural network (ANN) model for predicting edge stretchability of GPa-grade steels, which is substantially difficult to predict due to the complex nonlinear relation among the numerous sheared edge qualities. We newly suggest the physically characterized parameters, such as material properties, deformed shape, and work hardening of sheared edge, to predict the various materials and punching methods, simultaneously. The proposed parameters are trained with the predamage strain which is calculated by inherent fracture strain and experimental results in terms of hole expansion ratio. To prevent the overfitting issues, cross validation method with additional datasets from a different kind of edge stretchability test such as sheared edge tensioning test are utilized. Experimental validations have been conducted with various GPa-grade steels and sheared edge conditions, which are compared with the proposed ANN model and numerical simulation. The proposed ANN model exhibits remarkable performance in the prediction of hole expansion ratio having a mean absolute error of 1.5% when compared to the previous studies such as numerical simulation and ANN model with utilizing the maximum hardness measured at the sheared edge. INDEX TERMS Artificial neural network, edge cracking, edge stretchability, GPa-grade steels, sheared edge quality.

show abstract

Optimal Lightweight Material Selection for Automobile Applications Considering Multi-Perspective Indices

Cited by 33 publications

References 30 publications

A systematic review on material selection methods

A systematic review on material selection methods

Material Selection of Green Design Processes for Car Body via considering Environment Property

Artificial Neural Network for Predicting Edge Stretchability in Hole Expansion Test With Gpa-Grade Steel

Contact Info

Product

Resources

About