Current State and Benchmarking Generative Artificial intelligence for Additive Manufacturing

Published

April 22, 2024

Author(s)

Nowrin Akter Surovi, Paul Witherell, Kumara Sundar, Vinay Saji Mathew

Abstract

Additive Manufacturing (AM) is becoming increasingly popular in academia and industry due to its cost-effectiveness and time-saving benefits. However, AM faces several challenges that must be addressed to enhance its efficiency. While Machine Learning (ML) can address various AM challenges, it is limited to tackles specific issues, often requiring multiple models for different problems. Conversely, Generative Artificial Intelligence (GenAI) holds potential in reducing instance-specific bias because of its broader training. In this paper, we present a comprehensive methodology to evaluate the capabilities of various existing GenAI tools in addressing diverse AM-related tasks. We propose three categories of metrics, totalling 35 metrics, namely agnostic, domain task, and problem task metrics. Additionally, we introduce a scoring matrix to assess the responses of different GenAI tools. The study involves data collection from diverse published papers, which are used to create inquiries for GenAI tools. The results show that transformer-based models, such as multi-modal GPT-4 and Gemini (prev. BARD), can handle both AM image and text data. In contrast, uni-modals such as GPT-3 and Llama 2 are proficient in processing AM text data. Furthermore, image-based models such as DALL·E 3 and Stable Diffusion can accept AM text data and generate images. It is also observed that the performance of these models varies across different AM-related tasks. The variation in their performance may be due to their underlying architecture and the training dataset.

Conference Dates

August 25-28, 2024

Conference Location

Washington DC, DC, US

Conference Title

International Design Engineering Technical Conferences & Computers and Information in Engineering Conference

Pub Type

Conferences

Keywords

Additive manufacturing, GenAI, artificial intelligence

Citation

Surovi, N. , Witherell, P. , Sundar, K. and Mathew, V. (2024), Current State and Benchmarking Generative Artificial intelligence for Additive Manufacturing, International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Washington DC, DC, US (Accessed November 22, 2024)