AI-Driven MCCB Design System

Generative AI-based design optimization for LS ELECTRIC molded case circuit breakers

Project Overview

This project developed a generative AI model for optimizing the design of Molded Case Circuit Breaker (MCCB) injection-molded components in collaboration with LS ELECTRIC. The 2-year initiative demonstrated that AI-driven design can successfully integrate with mass production processes, from initial concept through mold fabrication, prototype production, and physical testing.

My Role

Project Manager & Technical Lead

  • Defined core problem statements and project scope
  • Led technical implementation and hands-on development
  • Coordinated cross-functional collaboration between AI research team and manufacturing engineers
  • Managed the entire product development cycle from AI model development to physical validation

Key Achievements

AI-generated MCCB design optimization process

Performance Improvements

  • 19% increase in fracture strength while maintaining the same component weight
  • 50x increase in design alternatives: Generated and evaluated ~1,000 design options (vs. ~20 in traditional workflows)
  • Design review time reduced to ~30 seconds through AI-powered performance prediction
  • 75% reduction in design iteration cycles expected for future projects

Technical Innovation

  • Developed hybrid AI system combining generative AI for design synthesis and predictive AI for performance analysis
  • Delivered designs in editable CAD format fully compatible with injection molding manufacturing constraints
  • Established end-to-end workflow from AI design generation to physical prototype validation

Technical Approach

The project integrated two complementary AI technologies:

  1. Generative AI Model: Creates diverse design alternatives optimized for mechanical performance while respecting manufacturing constraints
  2. Predictive AI Analysis: Rapidly evaluates structural performance of generated designs, replacing time-intensive finite element analysis

This combination enabled rapid exploration of the design space while ensuring manufacturability and performance requirements.

Impact

The successful completion of this project demonstrated the viability of AI-driven design for precision manufacturing components. The methodology and tools developed can be applied to other electromechanical components, potentially transforming traditional design workflows in the electrical equipment industry.

Project Duration: January 2024 - February 2026 Partner: LS ELECTRIC Organization: Narnia Labs

Source: 현대경제신문 기사