Rapid and efficient design methods of axle housing are an increasingly important area for the lightweight design of heavy-duty trucks. Here, a database-driven lightweight design method for axle housing is developed. A theoretical analysis is conducted to explore the relationship between the shape and maximum stress of cross-sections. Subsequently, A double super-ellipse equation is applied to generate massive cross-sections with different shapes and sizes. The secondary development module is constructed to achieve the parametric modeling, finite element analysis, and result analysis of axel housing. Finally, the proposed database-driven lightweight design is employed to carry out the lightweight design of axel housing under different design criteria. The results show that compared to their counterpart, the mass of axel housing using Q345 and Q420 is decreased by 10.3% and 18.9%, respectively, and their maximum stress are declined by 32.9% and 20.8%, respectively, under the design criteria of equal strength. Under the design criteria of specified safety factors, the mass and maximum stress of axel housing are decreased by 11.7% and 25.0%, respectively. This investigation offers some important insights into the lightweight design of tube components.
LiQZhangYZhangC, et al. Analysis method and case study of the lightweight design of automotive parts and its influence on carbon emissions. Processes2022; 10: 2560.
2.
LiXWuHHuY, et al. Assessing the environmental benefits of battery packs from multi-vehicle and multi-region perspective: aiming for lightweight and carbon neutrality. Environ Prog Sustainable Energy2022; 41: e13892.
3.
WangDZhangTGuoX, et al. The potential of 3D printing in facilitating carbon neutrality. J Environ Sci2023; 130: 85–91.
4.
ChenYLawellCYWangY.The Chinese automobile industry and government policy. Res Transp Econ2020; 84: 100849.
5.
SunZZachariasJ. Do housing tenure and public transport provision matter in automobile use in bedroom suburban communities? Evidence from Beijing. J Housing Built Environ2021; 36: 241–262.
6.
LiuBZhangQLiuJ, et al. The impacts of critical metal shortage on China’s electric vehicle industry development and countermeasure policies. Energy2022; 248: 123646.
7.
LiuQ. Electric car with solar and wind energy may change the environment and economy: a tool for utilizing the renewable energy resource. Earths Fut2014; 2: 7–13.
8.
ZhangWXuJ. Advanced lightweight materials for automobiles: a review. Mater Design2022; 221.
9.
XuFXZhangXZhangH. A review on functionally graded structures and materials for energy absorption. Eng Struct2018; 171: 309–325.
10.
SivanurKUmanandaKVPaiD. Advanced materials used in automotive industry – a review. In: 3rd International Conference on Advancements in Aeromechanical Materials for Manufacturing (ICAAMM), Hyderabad, India, 2020.
11.
TangH. Latest advances in joining technologies for automotive body manufacturing. Int J Veh Design2010; 54: 1–25.
12.
WangYLangLSherkatghanadE, et al. Rigid-flexible coupling forming process for aluminum alloy automobile body panels. Int J Adv Manuf Technol2018; 95: 3905–3918.
13.
ZhangJNingLHaoY, et al. Topology optimization for crashworthiness and structural design of a battery electric vehicle. Int J Crashworthiness2021; 26: 651–660.
14.
KimKJ. Light-weight design and fatigue characteristics of automotive knuckle by using finite element analysis. J Mech Sci Technol2021; 35: 2989–2995.
15.
SeralathanSPrasannaHArunJ, et al. Finite element analysis of hybrid composite material based leaf spring at various load conditions. In: International Conference on NanoTechnology – Ideas, Innovation and Industries, Anna University, Syed Ammal Engineering College, Ramanathapuram, India, 2019, pp. 3540–3548.
16.
JinXGongYHanX, et al. A review of current state and prospect of the manufacturing and application of advanced hot stamping automobile steels. Acta Metallurgica Sinica2020; 56; 411–428.
17.
DashSSLiDJZengXQ, et al. Heterogeneous microstructure and deformation behavior of an automotive grade aluminum alloy. J Alloys Compd2021; 870: 159413.
18.
LeTWeiQWangJ, et al. Effect of different casting techniques on the microstructure and mechanical properties of AE44-2 magnesium alloy. Mater Res Express2020; 7: 116513.
19.
KrolczykGMMarudaRWKrolczykJB, et al. Ecological trends in machining as a key factor in sustainable production – a review. J Clean Prod2019; 218: 601–615.
20.
LeeCHHungCHLeePK, et al. A study on hot stamping of tailor welded blanks. J Chin Soc Mech Eng2022; 43: 431–440.
21.
HurYCKimDKimBM, et al. Measurement of weld zone properties of laser-welded tailor-welded blanks and its application to deep drawing. Int J Automot Technol2020; 21: 615–622.
22.
LiangOQ. Performance-based optimization: a review. Adv Struct Eng2007; 10: 739–753.
23.
ZhangGZhuRXieG, et al. Optimization of cooling structures in gas turbines: a review. Chin J Aeronaut2022; 35: 18–46.
24.
XiaLBreitkopf. Recent advances on topology optimization of multiscale nonlinear structures. Arch Comput Methods Eng2017; 24: 227–249.
25.
ZhuJHZhangWHXiaL. Topology optimization in aircraft and aerospace structures design. Arch Comput Methods Eng2016; 23: 595–622.
26.
SaitouKIzuiKNishiwakiS, et al. A survey of structural optimization in mechanical product development. J Comput Inf Sci Eng2005; 5: 214–226.
27.
LiZLiuDQianY, et al. Enhanced strength and weakened dynamic sensitivity of honeycombs by parallel design. Int J Mech Sci2019; 151: 672–683.
28.
XuFXWangJYHuaL. Multi-objective biomimetic optimization design of stiffeners for automotive door based on vein unit of dragonfly wing. Proc IMechE, Part C: J Mechanical Engineering Science2022; 236: 4551–4564.
29.
CerboneG. Machine learning in engineering: Techniques to speed up numerical optimization. Corvallis, OR: Oregon State University, 1992.
30.
RamuPThananjayanPAcarE, et al. A survey of machine learning techniques in structural and multidisciplinary optimization. Struct Multidiscip Optim2022; 65: 266.
BangaSGehaniHBhilareS, et al. 3D topology optimization using convolutional neural networks. arXiv preprint arXiv:1808.07440, 2018.
33.
SunGPangTFangJ, et al. Parameterization of criss-cross configurations for multiobjective crashworthiness optimization. Int J Mech Sci2017; 124: 145–157.
34.
LiuWJinLLuoY, et al. Multi-objective crashworthiness optimisation of tapered star-shaped tubes under oblique impact. Int J Crashworthiness2021; 26: 328–342.
NavickaiteKCattaniLBahlCRH, et al. Elliptical double corrugated tubes for enhanced heat transfer. Int J Heat Mass Transfer2019; 128: 363–377.
37.
DuBLiQZhengC, et al. Application of lightweight structure in automobile bumper beam: a review. Materials2023; 16: 967.
38.
MaM-TLiK-JSiY, et al. Hydrogen embrittlement of advanced high-strength steel for automobile application: a review. Acta Metallurgica Sinica-English Letters2023; 36: 1144–1158.
39.
XuTWuHXueF, et al. Structural design of stamping die of advanced high-strength steel part for automobile based on topology optimization with variable density method. Int J Adv Manuf Technol2022; 121: 8115–8125.
40.
BryantNThielJ. Critical characteristics affecting the surface finish of castings. Int J Metalcast2018; 12: 186–195.
41.
ZhouJLinLLuoY. The multi-objective optimization design of a new closed extrusion forging technology for a steering knuckle with long rod and fork. Int J Adv Manuf Technol2014; 72: 1219–1225.
42.
LeeMSMoonJHKangCG. Effect of the die temperature and blank thickness on the formability of a laser-welded blank of a boron steel sheet with removing Al-Si coating layer. Adv Mech Eng2014; 6: 925493.
43.
TangBTZhaoZWangY. One-step FEM-based evaluation of weld line movement and development of blank in sheet metal stamping with tailor-welded blanks. Int J Adv Manuf Technol2007; 35: 268–279.
