Generating Explainable and Verified Product Information Models From Industrial Documentation

Abstract

Abstract. The design and management of complex products require engineers to synthesize vast amounts of information from disparate sources like design manuals, specification sheets, and technical reports. Manually constructing coherent and structured product information models is a primary bottleneck in the product development lifecycle. Product information modeling captures functional requirements, physical component hierarchies, interconnections, and critical parameters. This process is not only time-consuming and labor-intensive but also hinders scalability. To address this critical engineering challenge, this study explores the integration of large language models (LLMs) with expert-guided verification to streamline the product information modeling process. Our approach aims to automatically extract and structure product knowledge from large volumes of unstructured technical documents, providing both formal semantics and visual graphical representations to support domain engineers and IT professionals. We demonstrate the approach through a case study on the Tennessee Eastman Process (TEP), a well-established benchmark in process control and industrial systems research. The results highlight the method’s effectiveness in capturing relevant product knowledge and aligning it with design intent. Guided by a defined set of evaluation metrics, further experimental results validate a substantial increase in performance, evidenced by an 82% saving in modeling time and comprehensive information coverage (100% recall). This work paves the way for automated, knowledge-driven product modeling and offers promising advancements in the design and management of complex, large-scale products.