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Abstract Within the engine room, a complex network of various equipment and pipes can be found. These components are installed and manufactured using a modular approach. Specifically, the equipment unit module comprises the equipment itself and the piping surrounding its inlet and outlet. When arranging these unit modules, engineers must consider the equipment's installation location and the interconnecting routes. Notably, a conventional piping pattern, based on expert knowledge and guidelines, is typically employed around the equipment's inlet and outlet. However, this design process heavily relies on an engineer's experience and needs more quantitative evaluation. As a result, non-experts find it challenging to understand the characteristics and design the arrangement of the equipment unit module. Moreover, due to the complexity of these patterns, the design review process requires substantial resources. To address these challenges, we propose a method for analyzing piping patterns and automatically generating optimal patterns for equipment unit modules. To identify the patterns, we preprocess the intricately interconnected piping group. Piping can be represented as connection lines since they serve the purpose of connecting equipment. By simplifying these connections, a topological approach based on equipment can be employed, enabling the analysis of overall patterns and the understanding of equipment relationships. In this study, we compared the results of each analysis using traditional techniques such as the Dijkstra and A* algorithms and the latest technique, a graph neural network (GNN) model based on deep learning. By applying the method that yielded the best results, we could develop a model capable of recommending optimal patterns. Finally, we applied the proposed method to analyze patterns and generate optimal patterns for major equipment unit modules within the engine room, verifying its effectiveness.
Publication Date 2023-10-19
Min-Chul Kong, Myung-Il Roh, Jisang Ha, Mijin Kim, Jeoungyoun Kim, "A Method for the Generation of Optimal Patterns for Equipment Unit Modules in the Engine Room", Proceedings of 10th PAAMES and AMEC 2023, Kyoto, Japan, 2023.10.18-20

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  2. In-Su Han, Myung-Il Roh, Min-Chul Kong, "A Generative AI-based Q&A System for Design Regulations", Proceedings of International Symposium on PRADS 2025, Ann Arbor, United States, pp. ??, 2025.10.19-23

  3. Seong-Won Choi, Myung-Il Roh, In-Chang Yeo, "A Method for Collision Avoidance of an Autonomous Ship in Dynamic Environments", p.?, Proceedings of ISOPE 2025, Goyang, Korea, 2025.06.01-06

  4. Do-Hyeok Ahn, Myung-Il Roh, In-Chang Yeo, Do-Hyun Chun, "A Method for Automatic Control of the Block Lifting by an Offshore Floating Crane Based on Deep Reinforcement Learning", p.?, Proceedings of ISOPE 2025, Goyang, Korea, 2025.06.01-06

  5. Yun-Sik Kim, Myung-Il Roh, Ha-Yun Kim, In-Chang Yeo, Nam-Sun Son, "A Method for Robust Tracking and Fusion of Maritime Obstacles Using Multiple Sensor Data", Proceedings of ISOPE 2025, Goyang, Korea, 2025.06.01-06

  6. Min-Chul Kong, Myung-Il Roh, In-Su Han, Seong-Won Choi, Mijin Kim, Jeoungyoun Kim, Inseok Lee, "A METHOD FOR SHIP PIPING DESIGN USING PAST DATA AND EXPERT KNOWLEDGE", Proceedings of OMAE2025, Vancouver, Canada, 2025.06.22-27

  7. Min-Chul Kong, Myung-Il Roh, In-Su Han, Mijin Kim, Jeoungyoun Kim, "A Method for Pipe Auto-routing Using Graph and Octree Structure", Proceedings of G-NAOE 2024, Southampton, UK, 2024.11.05-09

  8. Ha-Yun Kim, Myung-Il Roh, Jisang Ha, In-Chang Yeo, Nam-Sun Son, "A Learning-based Method for Tracking Maritime Obstacles in Real-time", Proceedings of ISOPE 2024, Rhodos, Greece, 2024.06.16-21

  9. In-Su Han, Myung-Il Roh, Min-Chul Kong, Jeongyoul Lee, Seoyoon Park, "A Search Method for Ship Regulations Considering Document Features", Proceedings of ICCAS 2024, Genoa, Italy, 2024.09.10-12

  10. Jin-Hyeok Kim, Myung-Il Roh, In-Chang Yeo, "A Method for the Automatic Generation of Hull Form Surfaces Based on MLP (Multi-Layer Perceptron) Considering Design Requirements", Proceedings of G-NAOE 2024, Southampton, UK, 2024.11.05-09

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