Designing an LLM-Based Copilot for Manufacturing Equipment Selection

TL;DR

This paper presents a novel LLM-based copilot that integrates retrieval-augmented generation to assist automation engineers in selecting manufacturing equipment, aiming to reduce ramp-up time and improve decision traceability.

Contribution

It introduces a factual-driven LLM copilot with structured knowledge retrieval for equipment selection, tailored for manufacturing ramp-up planning, and demonstrates its effectiveness through industrial case studies.

Findings

19 out of 22 prompts involved correct equipment selection.

6 cases fully met all requirements.

Feedback confirmed logical and actionable recommendations.

Abstract

Effective decision-making in automation equipment selection is critical for reducing ramp-up time and maintaining production quality, especially in the face of increasing product variation and market demands. However, limited expertise and resource constraints often result in inefficiencies during the ramp-up phase when new products are integrated into production lines. Existing methods often lack structured and tailored solutions to support automation engineers in reducing ramp-up time, leading to compromises in quality. This research investigates whether large-language models (LLMs), combined with Retrieval-Augmented Generation (RAG), can assist in streamlining equipment selection in ramp-up planning. We propose a factual-driven copilot integrating LLMs with structured and semi-structured knowledge retrieval for three component types (robots, feeders and vision systems), providing a guided and traceable state-machine process for decision-making in automation equipment selection. The system was demonstrated to an industrial partner, who tested it on three internal use-cases. Their feedback affirmed its capability to provide logical and actionable recommendations for automation equipment. More specifically, among 22 equipment prompts analyzed, 19 involved selecting the correct equipment while considering most requirements, and in 6 cases, all requirements were fully met.