Utilizing Artificial Intelligence and Knowledge-Based Engineering Techniques in Shipbuilding: Practical Insights and Viability

IF 0.5 4区 工程技术 Q4 ENGINEERING, MARINE
Tufail Shahzad, Peng Wang, Peter van Lith, Jacques Hoffmans
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Abstract

_ This paper delves into the technical aspects and viability of integrating artificial intelligence (AI) and knowledge-based engineering (KBE) tools in practical design. The goal is to digitally embed the hands-on expertise and technical boundaries set by seasoned professionals during intricate engineering and preparatory phases. We showcase how AI/KBE tools might emulate human cognitive processes to make well-informed choices. The article also probes the prospective economic and modernization repercussions of this innovation. Our findings suggest that such an integration is feasible and can amplify the decision-making efficacy and advance the sophistication of CAD/CAM systems in the shipbuilding realm. Furthermore, this investigation underscores the promising future of AI/KBE tools in ship design and advocates for continued exploration and innovation in this sector to fully harness its advantages. Introduction Shipbuilding has long been intertwined with CAD/CAM technologies. As technology evolves, so does the landscape of ship design and manufacturing (Ross, 1950). Traditionally, ship design leaned heavily on seasoned engineers and designers, whose insights were cultivated over years of experience. However, with the rising demand for ships and an aging workforce, there’s a pressing need for enhanced design methodologies. Enter the era of artificial intelligence (AI) and knowledge-based engineering (KBE), which promise to revolutionize ship design by integrating practical knowledge and technical constraints. In today’s shipbuilding scenario, younger engineers often handle detailed engineering stages, a shift from when experienced professionals dominated the shop floor (Moyst and Das, 2005). Our research aims to assess the feasibility of AI KBE systems in enhancing the ship design process during these stages, by virtualizing the knowledge of experienced workers.
在造船中应用人工智能和基于知识的工程技术:实践见解和可行性
_本文探讨了在实际设计中整合人工智能(AI)和知识工程(KBE)工具的技术方面和可行性。目标是数字化嵌入由经验丰富的专业人员在复杂的工程和准备阶段设置的实践专业知识和技术界限。我们展示了AI/KBE工具如何模仿人类的认知过程来做出明智的选择。文章还探讨了这一创新对经济和现代化的潜在影响。我们的研究结果表明,这种整合是可行的,可以扩大决策效能,并提高CAD/CAM系统在造船领域的复杂性。此外,这项调查强调了AI/KBE工具在船舶设计中的美好未来,并倡导在该领域继续探索和创新,以充分利用其优势。长期以来,造船一直与CAD/CAM技术交织在一起。随着技术的发展,船舶设计和制造领域也在不断发展(Ross, 1950)。传统上,船舶设计主要依赖于经验丰富的工程师和设计师,他们的见解是在多年的经验中培养出来的。然而,随着船舶需求的增加和劳动力的老龄化,迫切需要改进设计方法。进入人工智能(AI)和知识工程(KBE)时代,它们有望通过整合实践知识和技术限制来彻底改变船舶设计。在今天的造船场景中,年轻的工程师经常处理详细的工程阶段,从经验丰富的专业人员主导车间的转变(Moyst和Das, 2005)。我们的研究旨在评估AI KBE系统在这些阶段通过虚拟化经验丰富的工人的知识来提高船舶设计过程的可行性。
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来源期刊
CiteScore
1.10
自引率
0.00%
发文量
19
期刊介绍: Original and timely technical papers addressing problems of shipyard techniques and production of merchant and naval ships appear in this quarterly publication. Since its inception, the Journal of Ship Production and Design (formerly the Journal of Ship Production) has been a forum for peer-reviewed, professionally edited papers from academic and industry sources. As such it has influenced the worldwide development of ship production engineering as a fully qualified professional discipline. The expanded scope seeks papers in additional areas, specifically ship design, including design for production, plus other marine technology topics, such as ship operations, shipping economics, and safety. Each issue contains a well-rounded selection of technical papers relevant to marine professionals.
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