銲接件振動應力消除模擬並以類神經網路進行預估

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姓名

李柏泓(Bo-Hong Li) 查詢紙本館藏

畢業系所

機械工程學系

論文名稱

銲接件振動應力消除模擬並以類神經網路進行預估
(Simulation of Vibration Stress Relief of Weldments and Prediction with Artificial Neural Network)

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至系統瀏覽論文 (2028-6-28以後開放)

摘要(中)

本研究期望提供一個類神經網路模型可以快速預估不同工況的殘留應力在經過振動應力消除後的應力值，透過此類神經網路模型可以只需直接輸入所需少數特徵就可以快速獲得應力消除的量，以立即預估振動應力消除的效果。如何消除殘留應力一直都是金屬加工的重要課題之一，隨著技術發展，振動應力消除法(VSR)逐漸受到關注，該技術藉由疊加工件內部的殘留應力與外部施予的負載來產生塑性變形，並由此達到應力重新分布，降低材料內部應力的目的，然而其應力消除量不好量測。故本研究透過進行銲接有限元素模擬產生殘留應力，再對其施予振動應力消除，即反覆施力效果模擬，並收集有限元素模擬結果的網格節點資料形成數據庫，以該數據庫訓練類神經網路，期望能訓練出準確度高的類神經網路，期能直接以此對振動應力消除後的殘留應力進行預估。

摘要(英)

This research aims to provide an artificial neural network model that can efficiently estimate the residual stress under different operating conditions of vibration stress relief. By utilizing, it is expected to bypass the time-consuming finite element simulations. Through the direct input of the necessary features to the artificial neural network model, it is feasible to expeditiously acquire stress values after applying the vibratory force. Elimination of residual stress has always been one of the important issues in metal processing. With the development of innovative technology, Vibration Stress Relief (VSR) has gradually attracted attention. Vibration stress relief achieves because of the plastic deformation due to superimposing the residual stress and the applied cyclic load. However, there is no systematic way to estimate the stress reduction effect. This study, therefore, tried to numerically study the stress reduction resulting from the application of a cyclical load. Next the numerical data obtained from the finite element software, ANSYS, were collected. Artificial neural network model was build using these numerical data. The differences between the numerical data and the predicted data of the model were less than 5 % in Mean Absolute Percentage Error (MAPE).

關鍵字(中)

★ 振動應力消除
★ 沙博什模型
★ 有限元素模擬
★ 類神經網路

關鍵字(英)

★ Vibration stress relief
★ Chaboche model
★ Simulation using finite element software
★ Artificial neural network

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vii
表目錄 xi
第一章緒論 1
1-1 研究背景與目的 1
1-2 文獻回顧 1
第二章金屬材料基本理論 6
2-1 塑性循環負載理論 6
2-1-1 塑性變形 6
2-1-2 包辛格效應與硬化行為 7
2-1-3 材料的循環應力-應變行為 10
2-2 沙博什材料模型(Chaboche model) 12
第三章類神經網路基本理論 16
3-1 資料前處理(Data pre-processing) 16
3-1-1 特徵重要性分析 16
3-1-1-1 皮爾森相關係數(Pearson correlation coefficient) 16
3-1-1-2 隨機森林(Random forest)特徵重要性分析 17
3-1-2 特徵轉換/正規化(Normalization) 19
3-2 類神經網路結構 20
3-3反向傳播(Backpropagation) 22
3-4 過擬合(Overfitting) 22
3-4-1 三分留出法(Three-way holdout method) 22
3-5 隨機種子(Random seed) 23
第四章銲接薄平板振動應力消除模擬 24
4-1 模擬方法與流程 24
4-2 銲接模擬 24
4-2-1 SS400銲接材料參數 25
4-2-2 SUS304銲接材料參數 32
4-3 Inistate指令導入新模型 36
4-3-1 SS400銲接應力導入新模型 37
4-3-2 SUS304銲接應力導入新模型 39
4-4 Inistate新模型施作振動應力消除 41
4-4-1 SS400 Inistate新模型振動應力消除之成效 41
4-4-2 SUS304 Inistate新模型振動應力消除之成效 48
第五章以類神經網路預估銲接振動應力消除之成效 53
5-1 類神經網路模型設定 53
5-1-1 基底資料 53
5-1-2 輸入(Input)與輸出(Output) 54
5-1-3 類神經網路架構 58
5-2 類神經網路模型篩選 59
5-2-1 模型篩選基準與過程 60
5-2-2 所選擇最終模型之表現 68
第六章結論與未來展望 71
6-1 結論 71
6-2 未來展望 72
參考文獻 74

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指導教授

黃以玫(Yi-Mei Huang)

審核日期

2023-7-20

推文