Metamodel-based lightweight design of B-pillar with TWB structure via support vector regression

Pan, Feng; Zhu, Ping; Zhang, Yu

doi:10.1016/j.compstruc.2009.07.008

Cited by 151 publications

(54 citation statements)

References 25 publications

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“…New advances in tailor-welded blank (TWB) technology allow effective combinations of different materials and different thicknesses to better reduce weight and increase crashworthiness [18][19][20][21][22]. The maximum weight saving could be possibly achieved through intelligent combinations of two or more of light-weight materials and/or discrete thicknesses.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study on Crashworthiness of Functionally Graded Thickness Thin-Walled Tubular Structures

Tian

2015

Exp Mech

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This paper is an experimental investigation of the crashworthiness behavior of functionally graded thickness (FGT) thin-walled tubular structures. Aluminum alloy AA6061-T5 was chosen because of its high strength to weight ratio and high stiffness. A series of FGT tubes with thicknesses varying linearly from one end to the other were evaluated in quasi-static crushing and three-and four-point bending tests for the purpose of evaluating their energy absorption characteristics. AA6061-T5 FGT tubes with four thickness distributions were considered, namely: t top =0.6, 0.8, 1.0, 1.2 mm, t bottom =1.5 mm. Specific energy absorption (SEA) and crush force efficiency (CFE) under maximum deformed displacement (δ max ) were measured from the tests to infer crashworthiness of the FGT tubes. It was found that the FGT tubes exhibit superior performance relative to a uniform thickness (UT) counterpart especially at the highest displacements and deformed more stably in overall crashing behaviors or collapsed modes. For example, the CFE of the AA6061-T5 FGT tube with t top =0.6 mm at δ=80 mm was~105 %; however, that for an AA6061-T5 UT counterpart with a 1.5 mm thickness tube is only 62 %. This suggests the possibility that crash energy absorption management in ground transportation vehicles may be enhanced through the use of the FGT tube designs.

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Section: Introductionmentioning

confidence: 99%

Experimental Study on Crashworthiness of Functionally Graded Thickness Thin-Walled Tubular Structures

Tian

2015

Exp Mech

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“…Then, as a first attempt, 35 sampling points (laminates) were selected for the training step for both metamodels. These quantities are based on the studies of Pan et al (2010) and Yang and Gu (2004). They used 5…”

Section: Latin Hypercube Sampling Datamentioning

confidence: 99%

“…Pan et al (2010) stated that LH is one of the "space-filling" methods in which all regions of design space are considered equal, and the sampled points fill the entire design space uniformly. Forrester et al (2008) explained that LH sampling generates points by stratification of sampling plan on all of its dimensions.…”

Section: Latin Hypercube Designmentioning

confidence: 99%

Laminated Composites Buckling Analysis Using Lamination Parameters, Neural Networks and Support Vector Regression

Koide

Ferreira

Luersen

2015

Lat. Am. j. solids struct.

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This work presents a metamodel strategy to approximate the buckling load response of laminated composite plates. In order to obtain representative data for training the metamodel, some laminates with different stacking sequences are generated using the Latin hypercube sampling plan. These stacking sequences are converted into lamination parameters so that the number of inputs of the metamodel becomes constant. The buckling load for each laminate of the training set are computed using finite elements. In this way the inputs-outputs metamodel training pairs are the lamination parameters and the corresponding bucking load. Neural network and support vector regression metamodels are employed to approximate the buckling load. The performances of the metamodels are compared in a test case and the results are shown and discussed.

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“…Among various systems of a vehicle, body-inwhite (BiW) is found as the most significant portion representing about 30% of the weight of a vehicle, and has tremendous potential in reducing weight of the whole vehicle [2]. Figure 1 shows the breakdown of vehicle weight by systems and components.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, it covers the electrical wiring and connections and spots for the passenger seatbelts. It also provides structural support against lateral or roof impact [2]. Figure 2 shows the B-pillar in a passenger car.…”

Section: Introductionmentioning

confidence: 99%

Reverse engineering of B-pillar with 3D optical scanning for manufacturing of non-uniform thickness part

Abdullah

Mahmud

2016

MATEC Web Conf.

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Abstract. This paper presents reverse engineering (RE) of a complex automobile structural part, B-pillar. As a major part of the automobile body-in white (BiW), B-pillar has substantial opportunity for weight reduction by introducing variable thickness across its sections. To leverage such potential, an existing B-pillar was reverse engineered with a 3D optical scanner and computer aided design (CAD) application. First, digital data (i.e. in meshes) of exiting B-pillar was obtained by the scanner, and subsequently, this information was utilized in developing a complete 3D CAD model. CATIA V5 was used in the modeling where some of the essential work benches were ''Digitized Shape Editor'', ''Quick Surface Reconstruction'', ''Wireframe and Surface Design'', ''Freestyle'', ''Generation Shape Design'' and "Part design". In the final CAD design, five different thicknesses were incorporated successfully in order to get a B-pillar with non-uniform sections. This research opened opportunities for thickness optimization and mold tooling design in real time manufacturing.

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Metamodel-based lightweight design of B-pillar with TWB structure via support vector regression

Cited by 151 publications

References 25 publications

Experimental Study on Crashworthiness of Functionally Graded Thickness Thin-Walled Tubular Structures

Experimental Study on Crashworthiness of Functionally Graded Thickness Thin-Walled Tubular Structures

Laminated Composites Buckling Analysis Using Lamination Parameters, Neural Networks and Support Vector Regression

Reverse engineering of B-pillar with 3D optical scanning for manufacturing of non-uniform thickness part

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