AI-Driven Design Automation and Vibration Prediction for Test JIGs

Deep-learning framework that generates JIG assemblies and predicts vibration performance for product testing setups

AI-Driven Design Automation and Vibration Prediction for Test JIGs — Narnia Labs case study (originally: 지그(JIG) 진동 성능 예측 및 최적 형상 제안을 통한 설계 자동화)

Project Overview

Developed a deep-learning framework that automates the design of vibration-test JIGs (fixtures used for product vibration testing). A generative model proposes JIG geometries under mounting and space constraints, and a predictive model evaluates their vibration performance directly — replacing the expert-driven, largely manual design loop.

My Role

AI Researcher, Narnia Labs

  • Individual contributor on the hands-on implementation of the assembly-generation and vibration-prediction models
  • Contributed to integrating the models into the design-engineer-facing software platform

The Challenge

Traditional JIG design leans heavily on specialist experience and manual iteration:

  • Slow arrival at an optimal shape: Deriving a JIG geometry that satisfies the required vibration behavior for a given product takes significant time.
  • Poor reuse of prior analyses: Existing CAE results are hard to reuse effectively, so similar design work is repeatedly redone.
  • Excessive manual design effort: Reliance on the engineer’s intuition and hand-modeling wastes overall engineering resources.

Technical Approach

Delivered a unified generative + predictive AI pipeline for JIG design:

  • Condition-driven shape generation: A generative AI trained on prior CAE data proposes per-component JIG geometries that respect mounting conditions and space constraints.
  • Vibration-performance predictor: A predictive model estimates the vibration response of a newly generated JIG assembly on the fly, without a full CAE run.
  • Dedicated design platform: The models are integrated into a single software platform so that design engineers can use them directly, without stepping through separate tools.

Impact

  • Sharp reduction in design man-hours: AI-assisted shape proposal replaces most of the manual iteration, saving engineering time and effort.
  • Faster vibration-testing setup: Reliable performance prediction cuts the preparation time required before running the actual vibration test.
  • Validated assembly-generation capability: Successfully demonstrated a rarely commercialized capability — multi-part assembly generation via AI — reinforcing the foundation of AI-driven engineering design automation.

Client: Electronics manufacturer (undisclosed) Organization: Narnia Labs Category: Others

Source: Narnia Labs Case Study #50