从拆解手册自动提取拆解指令：NLP 与 LLM 两种框架对比
Automated Knowledge Acquisition Pipeline for Disassembly Instructions: A Comparison of NLP and Generative AI Frameworks
制造业想搞拆解自动化的人可以看看：作者用真实拆解手册实测了规则式 NLP 和 LLM 两套方案，直接告诉你把手册转成机器可读指令该选哪条路，中小企业落地循环经济正好用得上。
一篇发表于 arXiv 的论文提出并对比了两个自动获取拆解指令的知识获取框架，目标是将非结构化的拆解手册文本转化为结构化、机器可读的格式。一个框架采用基于规则的传统 NLP 流水线，另一个框架利用 LLM 的生成式 AI 能力完成同一任务。
研究团队使用真实世界中的拆解手册对两种方法进行了实测比较，评估各自在从自然语言文本中抽取拆解步骤、零部件信息等指令要素上的表现。整个流程实现了从文本输入到经整理的结构化输出的自动化。
这项工作的背景是循环经济与 Re-X（再使用、再制造等）原则对中小企业的压力——产品复杂度不断上升，使拆解自动化难以落地。论文提供的结构化数据基础，可直接用于后续拆解流程的开发与自动化，属于面向制造业可持续场景的应用型研究。
Automated Knowledge Acquisition Pipeline for Disassembly Instructions: A Comparison of NLP and Generative AI Frameworks
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.
This report is categorized under AITOP's "Papers" desk, focusing on Europe's AI governance and technology ecosystem. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include LLM, NLP, 循环经济, 拆解自动化.
Source attribution: curated from arXiv cs.AI (@Tim Schnieders, Tareq Maximilian Gabriel Ahrari, Oliver Petrovic, Christian Brecher).