{"title":"Generative Artificial Intelligence: Analyzing Its Future Applications in Additive Manufacturing","authors":"Erik Westphal, Hermann Seitz","doi":"10.3390/bdcc8070074","DOIUrl":null,"url":null,"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.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data and Cognitive Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/bdcc8070074","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
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.
人工智能(AI)领域的新发展正越来越多地进入工业领域,如增材制造(AM)。生成式人工智能(GAI)应用尤其在这方面提供了有趣的可能性,例如,在算法的帮助下生成文本、图像或计算机代码,并将这些作为有用的支持集成到各种增材制造工艺中。本文专门探讨了 GAI 为增材制造提供的机遇。目前,有关 GAI 在增材制造中的应用的出版物相对较少。大部分信息只发表在预印本上。其中的重点是自然语言处理 (NLP) 算法、大型语言模型 (LLM) 和生成式对抗网络 (GAN)。这是对 AM 中 GAI 技术现状的新总结,而与具体使用案例的联系则是关于 AM 过程中 GAI 的首个综合案例研究。在此基础上,还开发了三个具体的使用案例,在这些案例中,生成式人工智能工具被用于优化 AM 流程。最后,对 GAI 的一般可能性进行了优势、劣势、机会和威胁(SWOT)分析,为深入讨论在 AM 中合理使用 GAI 工具奠定了基础。这项工作的主要发现是,GAI 可以集成到 AM 流程中,作为一种有用的支持,使这些流程更快、更有创造力,并使流程信息可以数字化记录和使用。本文还直观地展示和解释了 GAI 在当前和未来的潜力,以及在 AM 中的技术应用。此外,还展示了使用生成式人工智能工具的有用之处,以及当前或未来可能出现的潜在风险。