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Approach to Automation of Line Heating by Combination of Reinforcement Learning and Finite Element Method Simulation


 In recent years, ship hulls have very complicated shapes in order to reduce frictional resistance and wave resistance during navigation. In particular, in the bow and stern, curved skin plates with complex shapes are used. Line heating is used to produce such complex shapes. Line heating is a bending technique using plastic deformation due to heating. The relationship between the heat input and the deformation is nonlinear, which may lead to difficulty in making a heating plan for forming the target shape. Thus, skilled workers are necessary in line heating, and the work time and dimensional accuracy depend on their skills. Another problem is the transfer of this technique to future generations. In order to overcome these problems, automation of the line heating process has been investigated urgently. On the other hand, artificial intelligence (AI) technology has been rapidly developed in recent years. An AI system can deal with nonlinear relationships and ambiguous feature quantities, which are difficult to express mathematically. By using AI, automation of the planning of the heating line can be expected. The purpose of the present study is to obtain the optimal heat input conditions for forming an arbitrary shape in line heating. In order to accomplish this, we constructed an AI system that integrated deep layer reinforcement learning and line heating simulation. The proposed system was applied to the formation of fundamental shapes of line heating, including the bowl shape, the saddle shape, and the twisted shape. As a result, the proposed system was found to be able to generate heating plans for these shapes with fewer heating lines.