Human-artificial intelligence teaming for scientific information extraction from data-driven additive manufacturing research using large language models

TL;DR

This paper presents a framework leveraging large language models to automate and expedite the extraction of scientific information from data-driven additive manufacturing literature, facilitating collaboration between AM and AI experts.

Contribution

It introduces a novel framework and demonstration tool that enable continuous, automated extraction of AM-related scientific information using large language models.

Findings

LLMs can effectively extract relevant AM research information

The framework reduces manual effort in literature review

Potential to extend to broader engineering literature

Abstract

Data-driven research in Additive Manufacturing (AM) has gained significant success in recent years. This has led to a plethora of scientific literature to emerge. The knowledge in these works consists of AM and Artificial Intelligence (AI) contexts that have not been mined and formalized in an integrated way. It requires substantial effort and time to extract scientific information from these works. AM domain experts have contributed over two dozen review papers to summarize these works. However, information specific to AM and AI contexts still requires manual effort to extract. The recent success of foundation models such as BERT (Bidirectional Encoder Representations for Transformers) or GPT (Generative Pre-trained Transformers) on textual data has opened the possibility of expediting scientific information extraction. We propose a framework that enables collaboration between AM and AI experts to continuously extract scientific information from data-driven AM literature. A demonstration tool is implemented based on the proposed framework and a case study is conducted to extract information relevant to the datasets, modeling, sensing, and AM system categories. We show the ability of LLMs (Large Language Models) to expedite the extraction of relevant information from data-driven AM literature. In the future, the framework can be used to extract information from the broader design and manufacturing literature in the engineering discipline.