Generative Design of Brake Calipers with 3D Deep Learning

3D deep-learning generative design for automotive brake calipers with structural performance prediction and optimization

Generative Design of Brake Calipers with 3D Deep Learning — Narnia Labs case study (originally: 3D 딥러닝 기반 제너레이티브 디자인을 이용한 제동 캘리퍼 형상 구현)

Project Overview

Developed a 3D deep-learning framework for the design of automotive brake calipers. The system proposes thousands of candidate geometries, predicts their structural performance on the fly, and optimizes for a target trade-off between strength and weight — enabling design exploration well beyond the historical repertoire of human engineers.

My Role

Project Manager, Narnia Labs

  • Led problem definition and scoping in collaboration with the client team
  • Owned client-facing communication and delivery milestones throughout the engagement
  • Drove hands-on technical work on the 3D deep-learning generative-design framework integrating a generator, evaluator, and optimizer

The Challenge

Existing brake-caliper design relies on repeated manual CAD + physical / CAE test cycles:

  • Repetitive design–test loop: Reaching a design that goes beyond historical templates typically requires many rounds of test, hurting both cost and lead time.
  • Manual-process resource waste: Engineers hand-design a candidate, then run structural analyses and physical tests until requirements are met — a highly iterative and expensive workflow.

Technical Approach

Built an integrated AI framework combining generation, evaluation, and optimization:

  • AI-based design assistant: A 3D deep-learning model trained on existing designs and the underlying physics can propose tens of thousands of caliper designs and immediately estimate their performance.
  • Generator + Evaluator loop: Rather than only drawing candidate shapes, the system actively generates new designs and pre-evaluates their structural response, closing the automation loop.
  • Optimizer: A dedicated optimization stage searches for shapes that simultaneously maximize stiffness and minimize weight — targets that the human designer struggles to balance manually.

Impact

  • High-stiffness, lightweight caliper designs: Structural performance is preserved (or improved) while weight is reduced, cutting development cost at the same time.
  • Creative, non-standard geometries: The AI proposes topologies that a human engineer would rarely intuit, expanding the reachable design space.
  • Faster, cheaper development: Structurally sound, novel caliper designs are delivered significantly faster and at lower cost than the traditional manual workflow.

Client: Automotive parts manufacturer (undisclosed) Organization: Narnia Labs Category: Automobile

Source: Narnia Labs Case Study #59