機器學習輔助自沖鉚釘於鋁合金6061和雙相鋼(DP780、DP980、1180MS)端到端製程中之品質評估方法(鉚接品質和機械性質)

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姓名

曾煒策(Wei-Tse Tseng) 查詢紙本館藏

畢業系所

機械工程學系

論文名稱

機器學習輔助自沖鉚釘於鋁合金6061和雙相鋼(DP780、DP980、1180MS)端到端製程中之品質評估方法(鉚接品質和機械性質)
(Machine learning assisted evaluation methodology of selfpiercing rivets end-to-end process chain quality (joint quality and mechanical properties) for aluminum alloy 6061 and dual-phase steels (DP780,980,1180))

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至系統瀏覽論文 (2026-8-1以後開放)

摘要(中)

本論文以不同模具設計與不同材料及厚度的鉚接為研究，進行了模擬驗證、鉚接與拉伸實驗，並以機器學習的人工神經網路(ANN)為輔助，以歸納出何種因素會對鉚接品質與機械性質造成影響。而詳細研究中我們使用了各種的雙相鋼(DP780、DP980、1180MS)和不同深度的模具以及板材的厚度進行實驗，由於傳統的點焊對於不同種類的金屬進行焊接是一件難以達成的目標，故此研究提出使用鉚接的方式，搭配有限元素法(FEM)對於鉚接時的材料流動及有效應力分布進行分析模擬驗證其金屬流動的過程，並提供品質表現指標(PM)與拉伸實驗後所得到的機械性質與能量吸收作為評估方式，最後將以上連結成一條龍式的製程，透過訓練模型，使未來無須透過實際實驗便能得出詳細的結果以避免不必要的成本浪費。
本研究的最佳組合為厚度1mm的1180MS搭配2mm的Al6061對應到2.25mm深度的下模具，其能夠承受的最大拉力為9.26kN，而透過計算所得出的能量吸收為36.02J，而ANN模型將各項組合的數據進行匯入後訓練出的成果包含幾何尺寸的互鎖尺寸(interlock)、殘餘厚度(remaining thickness)以及作為品質的PM值，還有機械性質相關的最大承受拉力和能量吸收後，可以對使用者輸入的材料、厚度、模具深度進行預測，而針對上面所提到的輸入透過計算所得到的平均絕對誤差(MAE，Mean Absolute Error)分別為：0.031mm、0.051mm、0.029、0.714kN和3.38J，對應到的平均絕對百分比誤差(MAPE，Mean Absolute Percentage Error)為8.53%、12.99%、16.8%、11.22%和13.96%。

摘要(英)

In this study, we investigate the effects of different designs on the quality and strength of self-piercing riveting (SPR) in dissimilar metals. Specifically, we explore a wider range of dimensions that contribute to the overall strength of the joints. For this purpose, we prepared various dual-phase sheets of steel (DP780, DP980) and high-carbon steel (1180MS) to compare their strengths. Aluminum plates (Al6061) were also used consistently. We examine the shape and quality of the joints after riveting, and verify the trends of the important indicator, interlock, through simulations. Additionally, shear tests are conducted on the same samples to confirm the physical strength properties. Finally, the primary contribution of this study is to establish a correlation between the end-to-end process chain quality and shear test evaluations for self-piercing rivets of dissimilar metals, using Performance Metrics (PM) and mechanical properties, as well as energy absorption, as validation indicators. This study determined that the optimal combination is a die with a depth of 2.25mm, paired with a thickness of 1mm of 1180MS and 2mm of Al6061, capable of withstanding a maximum tensile force of 9.26kN and absorbing a maximum energy of 36.02 (J). This research employed an Artificial Neural Network (ANN) as a model to predict the quality and mechanical properties of riveting. The predicted outputs include interlock, remaining thickness, PM value, Maximum force, and energy absorption, serving as five reference indicators. The mean absolute errors (MAE) between the experimental and prediction results for the interlock, remaining thickness, PM value, maximum force and energy absorption reached 0.031mm, 0.051mm, 0.029, 0.714kN and 3.38J respectively, and the corresponding mean absolute percentage errors (MAPE) were 8.53 %, 12.99 %, 16.8 %, 11.22% and 13.96%. These results indicate a high level of prediction accuracy. Through validation against actual results, the study discusses the interrelationships between different riveting parameters and joint quality as well as mechanical properties.

關鍵字(中)

★ 自沖鉚釘
★ 異質材
★ 拉伸試驗
★ 端到端製程

關鍵字(英)

★ Self-piercing rivet
★ Dissimilar metals
★ Shear test
★ End-to-end process chain quality

論文目次

摘要 I
ABSTRACT III
致謝 V
表目錄 XI
第一章緒論 1
1-1前言 1
1-2研究動機與方法 3
第二章文獻回顧 4
2-1 SPR加工製程 4
2-2 有限元素分析 4
2-3 材料特性 5
2-3-1 鋁合金特性 5
2-3-2 雙相鋼特性 5
2-4 機器學習 6
2-4-1 ANN模型(人工神經網路) 6
2-4-2機器學習之目標 7
第三章材料與實驗設置 8
VIII
3-1 實驗材料 8
3-2 實驗設備 9
第四章模擬和預測結果與實驗探討 16
4-1 鉚接之實驗設計 16
4-1-1鉚接之實驗設計與模擬 16
4-2 鉚接結果分析 21
4-2-1鉚接之剖面品質分析 21
4-2-2拉伸結果與破壞模式分析 29
4-3 ANN模型建立與預測結果討論 38
第五章結論 47
參考文獻 49

參考文獻

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指導教授

傅尹坤(Yiin-Kuen Fuh)

審核日期

2023-8-8

推文