AI Guided Design for Additive Manufacturing for Lightweight Automotive Brackets

DOI

10.2991/978-94-6239-750-7_43How to use a DOI?

Keywords

Additive manufacturing; automotive components; AI-guided design; design automation; lightweight structures; sustainable manufacturing; topology optimization; machine learning; reinforcement learning; generative design; and advanced manufacturing

Abstract

The increasing need for automotive parts that are lightweight, high-performing, energy-efficient, and incorporate advanced additive manufacturing (AM) and intelligent design techniques. Traditional practices in the design of brackets involve significant manual revisions and time-consuming and expensive simulations. This results in longer cycle times for the development of brackets, and often, a smaller reduction in overall weight. This paper presents the AIMS-Design framework as a solution to these issues. AIMS-Design (Artificial Intelligence-Driven Manufacturing-Aware Structural Design) is an automated design framework that combines several design methodologies and machine learning, and incorporates AM into a single, cohesive workflow. To generate a design, the framework employs a deep neural surrogate model to anticipate and quantify the structural responses (stress, deflection, and fatigue life) of the structural model. This eliminates the need for numerous finite element analyses (FEA) of the model early in the design cycle. A reinforcement learning agent refines bracket geometries for multiple design objectives, including weight, stiffness, and energy efficiency, and incorporates manufacturability and efficiency. Design constraints that are specific to AM, including those that support structures, build orientation, and overhangs, are incorporated in the optimization loop to yield a design that is ready for printing and requires little to no post-processing. The brackets, made from aluminium alloy, were subjected to experimental validation and selective laser melting, which resulted in weight reductions between 38 and 45 percent and a 25 percent improvement in strength-to-weight ratio compared to standard designs, all while cutting design time by 60 percent. Lifecycle energy assessments show less material consumption, less carbon emission, and greater overall sustainability with this approach. With this experimental validation, it can be concluded that the AIMS-Design framework fosters the intelligent, sustainable, and efficient development of complex components for advanced automotive manufacturing systems.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Sanjay Kumar
AU  - Sapna Bawankar
PY  - 2026
DA  - 2026/08/31
TI  - AI Guided Design for Additive Manufacturing for Lightweight Automotive Brackets
BT  - Proceedings of the International Conference on Advanced Design, Manufacturing, and Sustainable Energy Systems (ICADMSES 2026)
PB  - Atlantis Press
SP  - 592
EP  - 603
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6239-750-7_43
DO  - 10.2991/978-94-6239-750-7_43
ID  - Kumar2026
ER  -