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Development of shipbuilding support system for linear heating forming of steel plates
http://hdl.handle.net/10076/0002001530
http://hdl.handle.net/10076/0002001530442e9ec6-fe85-436f-acd6-ffb789a1e2f6
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
2024DE0304.pdf (5.32 MB)
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| アイテムタイプ | 学位論文 / Thesis or Dissertation(1) | |||||||
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| 公開日 | 2025-06-12 | |||||||
| タイトル | ||||||||
| タイトル | Development of shipbuilding support system for linear heating forming of steel plates | |||||||
| 言語 | en | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| 資源タイプ | ||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_db06 | |||||||
| 資源タイプ | doctoral thesis | |||||||
| アクセス権 | ||||||||
| アクセス権 | open access | |||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||
| 著者 |
Zhang, Zhiliang
× Zhang, Zhiliang
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| 著者（ヨミ） | ||||||||
| 別名 | チョウ, シリョウ | |||||||
| 言語 | ja | |||||||
| 抄録 | ||||||||
| 内容記述タイプ | Abstract | |||||||
| 内容記述 | In the shipbuilding process, the heating and forming of steel plates is a key step that affects the structural integrity and performance of the hull. This paper proposes an innovative method based on machine learning (ML) and deep learning technology to optimize the heating line planning of steel plates in hull construction, thereby improving the accuracy and efficiency of steel plate forming. The traditional linear heating process of steel plates relies on experienced workers to plan and adjust the heating lines. This process is time-consuming and requires multiple iterations, resulting in a waste of manpower and resources. To solve this problem, this paper proposes an automated heating line planning method to achieve efficient heating of steel plates through ML and multi-objective optimization. First, this paper constructs a comprehensive steel plate heating model that takes into account the physical properties of steel plate materials (such as thickness, thermal conductivity, heating temperature, etc.), and uses finite element analysis (FEA) tools to simulate heat transfer, stress, and deformation during the heating process. Through this model, the deformation and stress distribution of steel plates under different materials and heating conditions can be accurately predicted, providing a theoretical basis for subsequent heating line planning. Next, this paper introduces a reinforcement learning algorithm (RL) to adaptively learn the optimal heating path based on the shape requirements of the target steel plate to minimize the forming error. During the RL process, the algorithm gradually optimizes the heating path through multiple iterations to ensure that the deformation of the steel plate is consistent with the target shape, thereby reducing manual intervention and improving the automation level of the heating process. In addition, in order to cope with the complexity of multidimensional data generated during the heating process, this paper combines principal component analysis (PCA) to reduce data dimensionality and simplify deformation features. The particle swarm optimization algorithm (PSO) is used to further optimize the heating route. Combined with the deep Q network (DQN) algorithm based on the gradient strategy, real-time adjustment and update of the heating process are achieved to ensure accurate control of the deformation of the steel plate during the entire heating process. After the heating is completed, the actual shape of the steel plate is measured using laser scanning technology and compared with the target shape. For areas with insufficient deformation, the model will re-plan the heating line and further heat it. Through this iterative feedback mechanism, not only the accuracy of steel plate forming is improved, but also data support is provided for further optimization of the model. In summary, this paper proposes a new steel plate heating method that integrates FEA, RL and multi-objective optimization, and dynamically feed backs the steel plate forming situation through real-time updates and laser scanning. The experimental results show that this method can significantly improve the efficiency and accuracy of steel plate forming, reduce the dependence on experienced workers, reduce energy consumption and production costs, and has important engineering application value. This method provides an efficient and intelligent solution for steel plate heating in shipbuilding, which helps promote the digital transformation and upgrading of the shipbuilding industry. |
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| 言語 | en | |||||||
| 内容記述 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | 本文/Graduate School of Engineering Mie University Japan | |||||||
| 内容記述 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | 100p | |||||||
| 書誌情報 |
発行日 2025-03-25 |
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| フォーマット | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | application/pdf | |||||||
| 著者版フラグ | ||||||||
| 出版タイプ | VoR | |||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||
| その他の言語のタイトル | ||||||||
| その他のタイトル | 鋼板の直線加熱整形における造船作業支援システムの開発 | |||||||
| 言語 | ja | |||||||
| 出版者 | ||||||||
| 出版者 | 三重大学 | |||||||
| 出版者（ヨミ） | ||||||||
| 値 | ミエダイガク | |||||||
| 学位名 | ||||||||
| 学位名 | 博士(工学) | |||||||
| 学位授与機関 | ||||||||
| 学位授与機関識別子Scheme | kakenhi | |||||||
| 学位授与機関識別子 | 14101 | |||||||
| 学位授与機関名 | 三重大学 | |||||||
| 学位授与年月日 | ||||||||
| 学位授与年月日 | 2025-03-25 | |||||||
| 学位授与番号 | ||||||||
| 学位授与番号 | 甲工学第2320号 | |||||||
| 資源タイプ（三重大） | ||||||||
| 値 | Doctoral Dissertation / 博士論文 | |||||||
