Automated systems compare nlp and ai for disassembly instructions
Automated Knowledge Acquisition Pipeline for Disassembly Instructions: A Comparison of NLP and Generative AI Frameworks
Artificial Intelligence
Summary
Disassembling complex products can be hard, especially for small companies aiming to recycle or repair items easily. The paper looks at two ways to turn written manuals into clear, organized steps that machines can understand — one using strict language rules, and the other using AI that generates text understanding. The authors tested both on real manuals to see which works better. This helps create smarter tools to automate taking products apart for reuse or recycling.
What this means in practice
- •For manufacturing engineers: Enable automatic extraction of disassembly steps from manuals to streamline recycling and repair process design.
- •For software developers in industrial automation: Integrate structured disassembly instructions from text into robotics or automation systems for product refurbishment workflows.
Authors
Tim Schnieders, Tareq Maximilian Gabriel Ahrari, Oliver Petrovic, Christian Brecher
Abstract
The push towards circularity and Re-X principles presents a challenge to SMEs, particularly due to increasing product complexity that impedes the adoption of disassembly automation. This paper introduces and compares two frameworks for extracting disassembly instructions from unstructured information sources, such as disassembly manuals. Our goal is to transform natural language text into a structured, curated and machine-readable format. We implemented and tested a rule-based Natural Language Processing (NLP) framework against a generative AI framework leveraging LLMs. The performance of both approaches was compared using real-world disassembly manuals. This work provides the necessary data foundation for developing and automating future disassembly processes.