Knowledge Graph Fusion with Large Language Models for Accurate, Explainable Manufacturing Process Planning

• URL: http://arxiv.org/abs/2506.13026v1
• Date: Mon, 16 Jun 2025 01:26:08 GMT
• Title: Knowledge Graph Fusion with Large Language Models for Accurate, Explainable Manufacturing Process Planning
• Authors: Danny Hoang, David Gorsich, Matthew P. Castanier, Farhad Imani,
• Abstract summary: We present Augmented Retrieval Knowledge Network Enhanced Search & Synthesis (ARKNESS)<n>ARKNESS fuses zero-shot Knowledge Graph construction with retrieval-augmented generation to deliver verifiable, numerically exact answers for CNC process planning.<n> Benchmarked on 155 industry-curated questions spanning tool sizing and feed-speed optimization, ARKNESS matches GPT-4o accuracy while achieving a +25 percentage point gain in multiple-choice accuracy.
• Score: 0.8874671354802572
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Precision process planning in Computer Numerical Control (CNC) machining demands rapid, context-aware decisions on tool selection, feed-speed pairs, and multi-axis routing, placing immense cognitive and procedural burdens on engineers from design specification through final part inspection. Conventional rule-based computer-aided process planning and knowledge-engineering shells freeze domain know-how into static tables, which become limited when dealing with unseen topologies, novel material states, shifting cost-quality-sustainability weightings, or shop-floor constraints such as tool unavailability and energy caps. Large language models (LLMs) promise flexible, instruction-driven reasoning for tasks but they routinely hallucinate numeric values and provide no provenance. We present Augmented Retrieval Knowledge Network Enhanced Search & Synthesis (ARKNESS), the end-to-end framework that fuses zero-shot Knowledge Graph (KG) construction with retrieval-augmented generation to deliver verifiable, numerically exact answers for CNC process planning. ARKNESS (1) automatically distills heterogeneous machining documents, G-code annotations, and vendor datasheets into augmented triple, multi-relational graphs without manual labeling, and (2) couples any on-prem LLM with a retriever that injects the minimal, evidence-linked subgraph needed to answer a query. Benchmarked on 155 industry-curated questions spanning tool sizing and feed-speed optimization, a lightweight 3B-parameter Llama-3 augmented by ARKNESS matches GPT-4o accuracy while achieving a +25 percentage point gain in multiple-choice accuracy, +22.4 pp in F1, and 8.1x ROUGE-L on open-ended responses.

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