Utilizing Artificial Intelligence and Knowledge-Based Engineering Techniques in Shipbuilding: Practical Insights and Viability
Tufail Shahzad; Peng Wang; Peter van Lith; Jacques Hoffmans
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.
Fig. 1: AI in shipbuilding
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.
Fig.2: Ship routine with AI
Acknowledgments
We extend our gratitude to the Dutch government organization “SME Innovation Stimulation Region and Top Sectors (MIT)”
for providing funding support for this research under grant number “MTHLA15019” via the Netherlands Enterprise Agency (Rijksdienst voor Ondernemend Nederland: RVO) under project number MIT2015.1.37. Their support was vital in enabling us to carry out this research. We also want to express our appreciation to our colleagues at the R&D department of MasterShip software for their valuable feedback and support throughout this research. Their contributions were crucial in helping us achieve our research goals.
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Kind regards,
Jacques Hoffmans
+31 6 52 52 08 80
jh@mastership.nl
Tufail Shahzad
+31 6 84 01 80 90
ts@mastership.nl
www.mastership.nl