Cyber System Research Group

 


In recent years, Japan's shipbuilding industry has been facing an urgent need to strengthen its international competitiveness, and it needs further improvement in the productivity of shipyards. Besides, manufacturing revolutions, such as Industry 4.0 are underway to fully digitalize the entire business process. Our group proposes a new shipbuilding industry that combines digital technologies such as AI and AR in the process of shipbuilding design and construction. We are also engaged in research on the advancement of inspection technology after service.


 




Overview of our research

 
1.  Study on construction of "Digital Shipyard"
 We are conducting research and development of the Digital Shipyard, which aims to build ships in a shorter period by creating a shipyard where everything is expressed numerically, everything is planned numerically, and everything is completed according to plan. In particular, we are focusing on information on the design and construction of shipbuilding, and are engaged in research to generate information related to the design and construction and to effectively transmit and display the generated information to designers and workers.

  1. R & D of production simulation technology that reproduces detailed movements of workers
  2. R & D of work support system for various shipbuilding works (bending, painting, etc.)
  3. R & D of AR technology and VR technology to shipbuilding
  4. R & D on shipbuilding robots (assistant work support robots, block surface plate management by drone, CFRP design CAD/CAM for shipbuilding, etc.)

 We are conducting research and development on production simulation technology to reproduce the detailed movements of workers. Fig. 1 shows a production simulation that reproduces the detailed movements of the workers at a small assembly process. The practical application of such production simulation technology enables the shipbuilding process to be precisely reproduced and helps planning worker allocation, calculating costs, and planning facility maintenance.


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Fig.1 Example of production simulation


 Shipbuilding involves various on-site operations such as bending steel plates, assembly, and painting. Workers on-site work while looking at the drawings, and we are engaged in research and development to support site operations using AR (Augmented Reality) technology as shown in Fig. 2. The aim is to shorten the construction period by making the work more efficient.

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Fig. 2 AR application to support bending process


 Ships are constructed by assembling parts of the hull structure, called blocks, on a surface plate. We have been developing the block surface management system shown in Fig. 3 to check whether the blocks under construction are manufactured as planned.

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Fig.3 Block surface plate management system


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2.  Research on design and planning using AI (noise, nesting)
 In today’s world of the third AI boom, research and development using deep learning and deep reinforcement learning, which have evolved neural network technology, is progressing. In the current shipbuilding industry, it is required to connect these AI technologies to social implementation. Therefore, research is being conducted to utilize AI technology in the early stages of shipyard design (the stage of basic design and basic planning).

  1. Onboard noise prediction using neural networks
  2. Nesting by reinforcement learning (steel placement planning)

 In the design stage, predicting the noise level inside a ship has been determined empirically and numerically. Simple and flexible empirical methods have been required to select and place components through trial and error in the design process. Neural network predictions are close to empirical judgments, so we have applied them to shipboard noise predictions. As shown in Figure 4, a web application is created and all the sample data that will improve prediction accuracy is kept and controlled.


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Fig. 4 Neural network based noise prediction web application and its operation image


 Nesting is the process of arranging parts so that the amount of waste materials is reduced as much as possible, as shown in Fig.5. We have tried to increase the yield rate at the design stage, and we have applied deep reinforcement learning, which is one of the learning methods of AI, to this task.

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Fig.5 Nesting


 Fig.6 shows an example of AI nesting results. Satisfactory results were obtained in self-learning without using results placed by humans, while by performing imitation learning using results placed by humans, we have confirmed the possibility that imitation learning can be used at a level that does not require manual correction.

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Fig. 6 Nesting results by reinforcement learning


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3.  Research on Advancement of Inspection Technology
 Visual inspection is the main form to detect structural defects and damage. Recently, drones have been used as one means to get easy access to inspection points. Our group has been working on the following research and development to apply them to the inspection of ship cargo tanks and offshore wind turbine blades.

  1. Image recognition of damage inside cargo tanks using deep learning AI
  2. An efficient method of inspecting offshore wind turbine blades

 Assuming that drones are used to inspect the ship's internal structure, we studied utilizing image recognition using deep learning (Faster R-CNN) to find cracks. Fig. 7 shows the result of damage recognition based on the results of deep learning on the damage images taken by an inspector with a digital camera. It is not useful enough to employ the current deep learning method alone for the field. It is necessary to advance this research by utilizing knowledge related to important inspection points.

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Fig. 7 Example of damage image recognition results by deep learning


 We are conducting research on efficient inspection technology for wind turbine blades in order to prevent lowering of the operating rate of offshore wind power generation, which is expected to spread in the future. One of the inspection technologies is image inspection technology by drone. Offshore wind turbines are located offshore, making them more difficult to access than onshore wind turbines. Also, because the wind turbine itself is larger than on land, the blades of the wind turbine are also in a higher position, making visual inspection more difficult. It is hoped that image observation by drone will solve these problems. As an example, at the time of inspection, the wind turbine is stopped rotating and the drone shoots with the blade stationary, but the drone flight path for inspecting the front and back and front and rear edges of the blade in a short time (Fig. 8) was examined.
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Fig.8 Inspection method for offshore wind turbine blades


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4.  Study on next-generation shipbuilding system
 We have set up a "next-generation shipbuilding system study group", "ideal of next-generation design system based on lessons of shipbuilding CIMS", "recent trends of technological innovation such as digitalization", "future image of maritime industry", We had a discussion on "Trends in other industries" and "Latest trends in CAD and other systems".
 Based on the discussions of this study group, we have compiled the following two recommendations.

  1. Recommendations for building an information collaboration platform
  2. Recommendations for building a digital twin at a shipbuilding factory

 As one means of strengthening international competitiveness, we are considering an alliance concept between shipyards and related industries as shown in Fig.9. This concept envisions ad hoc development for each project. Currently, there are various CAD systems used at each shipyard, but unless the data and information necessary for design, planning, procurement and construction can be linked, the effect of the alliance will not be exhibited. We will work to create a system that can be flexibly constructed at various stages including sales, procurement, development, design, and construction.


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Fig.9 Information Linkage System Concept


 We are also considering the concept of factory digital twin as shown in Fig.10. The factory digital twin concept requires construction simulation technology for making precise production plans. Therefore, we will incorporate the production simulation technology described above. We are also considering improving the construction simulation model based on site monitoring and evaluation analysis. We will work on the factory digital twin concept that twins the design/plan (cyber space) and the factory (physical space).
 We will proceed with these research and development focusing on improving the productivity of shipyards in order to solve the issues for strengthening the international competitiveness of our shipbuilding industry.


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Fig.10 Factory Digital Twin Concept


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