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AI-assisted design of lightweight and strong 3D-printed wheels for electric vehicles

Author

Listed:
  • Timileyin Opeyemi Akande
  • Oluwaseyi O Alabi
  • Ali Rizwan
  • Sunday A Ajagbe
  • Amos O Olaleye
  • Mathew O Adigun

Abstract

The automotive industry is undergoing a transformative shift towards electric vehicles (EVs), driven by environmental concerns and technological advancements. One critical aspect of EV design is the development of lightweight yet robust components, including 3D vehicle wheels. This research explores the implementation of generative models in Computer-Aided Design (CAD) systems to optimize the design of 3D vehicle wheels for electric vehicles. Through the use of generative design and additive manufacturing, we aim to create vehicle wheels that are energy-efficient, aesthetically pleasing, and structurally sound. Electric vehicles are gaining popularity due to their environmental benefits and reduced operating costs, making lightweight and strong wheels an important design goal. This research proposes a novel approach for designing lightweight and strong 3D vehicle wheels for EVs using generative models. The proposed approach involves the following steps: collect and prepare data, choose a generative model architecture, train the generative model, and generate new wheel designs. The approach methods show potential to revolutionize the design and manufacturing of lightweight and strong 3D-printed wheels for electric vehicles. In conclusion, generative models can be used to design and optimize wheel designs, making it possible to create safer, more efficient, and more cost-effective wheels.

Suggested Citation

  • Timileyin Opeyemi Akande & Oluwaseyi O Alabi & Ali Rizwan & Sunday A Ajagbe & Amos O Olaleye & Mathew O Adigun, 2024. "AI-assisted design of lightweight and strong 3D-printed wheels for electric vehicles," PLOS ONE, Public Library of Science, vol. 19(12), pages 1-21, December.
    • Handle: RePEc:plo:pone00:0308004
    DOI: 10.1371/journal.pone.0308004
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    References listed on IDEAS

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    1. Dezhi Han & HongXu Zhou & Tien-Hsiung Weng & Zhongdai Wu & Bing Han & Kuan-Ching Li & Al-Sakib Khan Pathan, 2023. "LMCA: a lightweight anomaly network traffic detection model integrating adjusted mobilenet and coordinate attention mechanism for IoT," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 84(4), pages 549-564, December.
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