Generative Artificial Intelligence: Analyzing Its Future Applications in Additive Manufacturing
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Published:2024-07-06
Issue:7
Volume:8
Page:74
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ISSN:2504-2289
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Container-title:Big Data and Cognitive Computing
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language:en
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Short-container-title:BDCC
Author:
Westphal Erik1ORCID, Seitz Hermann12ORCID
Affiliation:
1. Chair of Microfluidics, University of Rostock, 18059 Rostock, Germany 2. Department Life, Light & Matter, University of Rostock, 18059 Rostock, Germany
Abstract
New developments in the field of artificial intelligence (AI) are increasingly finding their way into industrial areas such as additive manufacturing (AM). Generative AI (GAI) applications in particular offer interesting possibilities here, for example, to generate texts, images or computer codes with the help of algorithms and to integrate these as useful supports in various AM processes. This paper examines the opportunities that GAI offers specifically for additive manufacturing. There are currently relatively few publications that deal with the topic of GAI in AM. Much of the information has only been published in preprints. There, the focus has been on algorithms for Natural Language Processing (NLP), Large Language Models (LLMs) and generative adversarial networks (GANs). This summarised presentation of the state of the art of GAI in AM is new and the link to specific use cases is this first comprehensive case study on GAI in AM processes. Building on this, three specific use cases are then developed in which generative AI tools are used to optimise AM processes. Finally, a Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis is carried out on the general possibilities of GAI, which forms the basis for an in-depth discussion on the sensible use of GAI tools in AM. The key findings of this work are that GAI can be integrated into AM processes as a useful support, making these processes faster and more creative, as well as to make the process information digitally recordable and usable. This current and future potential, as well as the technical implementation of GAI into AM, is also presented and explained visually. It is also shown where the use of generative AI tools can be useful and where current or future potential risks may arise.
Funder
European Regional Development Fund the Ministry for Economics, Employment and Health of Mecklenburg-Vorpommern, Germany
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