• Korean Journal of Computational Design and Engineering
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• v.2 no.1
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• pp.1-10
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• 1997

Finding the optimum route of ship's pipes is complicated and time-consuming process. Experience of designers is the main tool in this process. To reduce design man-hours and human errors a design expert system shell and a geometric modeler is used to automate the design process. In this paper, a framework of the intelligent CAD system for pipe auto-routing is suggested, which consists of general-purpose expert system shell and a geometric modeler. The design expert system and the geometric modeling kernel have been integrated. The CADDS5 of Computervision is used as the overall CAD environment. The Nexpert Object of Neuron Data is used as the expert system shell. The CADDS5 ISSM is used as the interface that creates and modifies geometric models of pipes. Existing algorithms for the routing problem have been analyzed. Most of them are to solve the 2-D circuit routing problems. Ship piping system, specially within the engine room, is a complicated, large scale 3-D routing problem. Methods of expert system have been used to find the route of ship pipes on the main deck.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.596-604
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• 2020

Previous studies on pipe auto-routing algorithms generally used such algorithms as A*, Dijkstra, Genetic Algorithm, Particle Swarm Optimization, and Ant Colony Optimization, to satisfy the relevant constraints of its own field and improve the output quality. On the other hand, this study aimed to significantly improve path-finding speed by applying the Jump Point Search (JPS) algorithm, which requires lower search cost than the abovementioned algorithms, for pipe routing. The existing JPS, however, is limited to two-dimensional spaces and can only find the shortest path. Thus, it requires several improvements to be applied to pipe routing. Pipe routing is performed in a three-dimensional space, and the path of piping must be parallel to the axis to minimize its interference with other facilities. In addition, the number of elbows must be reduced to the maximum from an economic perspective, and preferred spaces in the path must also be included. The existing JPS was improved for the pipe routing problem such that it can consider the above-mentioned problem. The fast path-finding speed of the proposed algorithm was verified by comparing it with the conventional A* algorithm in terms of resolution.

• International Journal of Naval Architecture and Ocean Engineering
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• v.5 no.3
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• pp.468-477
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• 2013

An automatic pipe routing system is proposed and implemented. Generally, the pipe routing design as a part of the shipbuilding process requires a considerable number of man hours due to the complexity which comes from physical and operational constraints and the crucial influence on outfitting construction productivity. Therefore, the automation of pipe routing design operations and processes has always been one of the most important goals for improvements in shipbuilding design. The proposed system is applied to a pipe routing design in the engine room space of a commercial ship. The effectiveness of this system is verified as a reasonable form of support for pipe routing design jobs. The automatic routing result of this system can serve as a good basis model in the initial stages of pipe routing design, allowing the designer to reduce their design lead time significantly. As a result, the design productivity overall can be improved with this automatic pipe routing system.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.4
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• pp.191-197
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• 2020

Pipe routing is a important part of the whole design process in the shipbuilding industry. It has a lot of constraints and many tasks that should be considered together. Also, the result of this stage affects follow-up works in a wide scope. Therefore, this part requires skilled designers and a lot of time. This study aims to reduce the workload and time during the design process by automating the pipe route design on initial stage. In this study, the reinforcement learning was used for pipe auto-routing. Reinforcement learning has the advantage of dynamically selecting routes, unlike existing algorithms. Therefore, it is suitable for the pipe routing design in ship design process which is frequently modified. At last, the effectiveness of this study was verified by comparing pipelines which were designed by piping designer and reinforcement learning results.