Generative Artificial Intelligence (GenAI) Prompt Engineering for Additive Manufacturing (AM)

Published

July 1, 2024

Author(s)

Nowrin Akter Surovi, Paul Witherell

Abstract

Additive manufacturing (AM) faces several challenges in achieving efficient and defect-free printing. Although traditional machine learning (ML) has proven effective in mitigating these challenges, it requires specialized models for solving specific problems with limited scopes. Generative artificial intelligence (GenAI) holds promise as a versatile tool capable of addressing multiple issues simultaneously, leveraging its expansive training data and robust problem-solving capabilities. However, getting the desired output from GenAI relies heavily on crafting effective prompts, as incorrect formulation of prompts can lead to unexpected responses. Prompt engineering is crucial for GenAI models to produce desired outputs efficiently. In our study, we explore how different prompt techniques affect the responses of GenAI tools in addressing AM problems. We examine five popular prompt engineering methods: Zero-shot, Few-shot, Chain-of-shot, React, and Directional Stimulus Prompting. We also use well-known GPT-4 models to evaluate these responses across various AM metrics.

Proceedings Title

Proceedings of the 35th Annual International
Solid Freeform Fabrication Symposium – An Additive Manufacturing Conference

Conference Dates

August 11-14, 2024

Conference Location

Austin, TX, US

Pub Type

Conferences

Keywords

Generative artificial Intelligence, ChatGPT, prompt engineering

Citation

Surovi, N. and Witherell, P. (2024), Generative Artificial Intelligence (GenAI) Prompt Engineering for Additive Manufacturing (AM), Proceedings of the 35th Annual International Solid Freeform Fabrication Symposium – An Additive Manufacturing Conference, Austin, TX, US (Accessed May 11, 2026)

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