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泵浦導輪氬焊焊道瑕疵檢測之研究 = = Research on the ...

廖浚頡

泵浦導輪氬焊焊道瑕疵檢測之研究 = = Research on the Detection of Weld Bead Defects in Argon Welding of Pump Guide Wheel /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	泵浦導輪氬焊焊道瑕疵檢測之研究 =/ 廖浚頡.
Reminder of title:	Research on the Detection of Weld Bead Defects in Argon Welding of Pump Guide Wheel /
remainder title:	Research on the Detection of Weld Bead Defects in Argon Welding of Pump Guide Wheel.
Author:	廖浚頡
Published:	雲林縣 :國立虎尾科技大學 , : 民113.06.,
Description:	[6], 48面 :圖, 表 ; : 30公分.;
Notes:	指導教授: 李廣齊.
Subject:	Defect Detection. -
Online resource:	電子資源

泵浦導輪氬焊焊道瑕疵檢測之研究 = = Research on the Detection of Weld Bead Defects in Argon Welding of Pump Guide Wheel /
廖浚頡

泵浦導輪氬焊焊道瑕疵檢測之研究 =Research on the Detection of Weld Bead Defects in Argon Welding of Pump Guide Wheel /Research on the Detection of Weld Bead Defects in Argon Welding of Pump Guide Wheel.廖浚頡. - 初版. - 雲林縣 :國立虎尾科技大學 ,民113.06. - [6], 48面 :圖, 表 ;30公分.

指導教授: 李廣齊.

碩士論文--國立虎尾科技大學自動化工程系碩士班.

含參考書目.

不鏽鋼泵浦因耐腐蝕和耐久環境下提供高效能流體運輸，而在排水系統、灌溉與其他農業應用設備以及高層建築設備領域被廣泛應用。然而焊接缺陷的發生經常由材料特性、焊接人員的焊接品質差異及製程變數的不穩定性因素進而影響不鏽鋼泵浦產品品質。由於傳統上在焊接缺陷都是依賴焊接人員進行目視檢測，容易受人為因素而誤判。本研究採用深度學習技術，開發一套基於卷積神經網路的氬焊焊道瑕疵自動檢測系統，並採用VGG、Xception、ResNet-152及DenseNet等四種模型進行訓練，可有效檢測良品及五種不同的焊接缺陷。學習資料集包含5448張299×299灰階焊接焊道圖像，劃分為80%的訓練資料和20%的驗證資料，並經過100個Epochs進行模型的訓練。實驗的結果顯示，VGG、Xception、ResNet-152和DenseNet四種模型均可準確的分類出焊接焊道類別，其中DenseNet模型，在訓練準確度最高達97.5%，在驗證準確度則達96.8%。在測試資料集包含544張299×299灰階焊接焊道圖像，模型經測試後，準確度達到了96.7%。研究結果顯示了其訓練模型具備廣泛的檢測能力，為未來實際生產中減少人工檢測負擔，同時提升不鏽鋼泵浦產品品質水準。.

(平裝)Subjects--Topical Terms:

1419456
Defect Detection.

泵浦導輪氬焊焊道瑕疵檢測之研究 = = Research on the Detection of Weld Bead Defects in Argon Welding of Pump Guide Wheel /
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245 1 0 $a 泵浦導輪氬焊焊道瑕疵檢測之研究 = $b Research on the Detection of Weld Bead Defects in Argon Welding of Pump Guide Wheel / $c 廖浚頡.
246 1 1 $a Research on the Detection of Weld Bead Defects in Argon Welding of Pump Guide Wheel.
250 $a 初版.
260 # $a 雲林縣 : $b 國立虎尾科技大學 , $c 民113.06.
300 $a [6], 48面 : $b 圖, 表 ; $c 30公分.
500 $a 指導教授: 李廣齊.
500 $a 學年度: 112.
502 $a 碩士論文--國立虎尾科技大學自動化工程系碩士班.
504 $a 含參考書目.
520 3 $a 不鏽鋼泵浦因耐腐蝕和耐久環境下提供高效能流體運輸，而在排水系統、灌溉與其他農業應用設備以及高層建築設備領域被廣泛應用。然而焊接缺陷的發生經常由材料特性、焊接人員的焊接品質差異及製程變數的不穩定性因素進而影響不鏽鋼泵浦產品品質。由於傳統上在焊接缺陷都是依賴焊接人員進行目視檢測，容易受人為因素而誤判。本研究採用深度學習技術，開發一套基於卷積神經網路的氬焊焊道瑕疵自動檢測系統，並採用VGG、Xception、ResNet-152及DenseNet等四種模型進行訓練，可有效檢測良品及五種不同的焊接缺陷。學習資料集包含5448張299×299灰階焊接焊道圖像，劃分為80%的訓練資料和20%的驗證資料，並經過100個Epochs進行模型的訓練。實驗的結果顯示，VGG、Xception、ResNet-152和DenseNet四種模型均可準確的分類出焊接焊道類別，其中DenseNet模型，在訓練準確度最高達97.5%，在驗證準確度則達96.8%。在測試資料集包含544張299×299灰階焊接焊道圖像，模型經測試後，準確度達到了96.7%。研究結果顯示了其訓練模型具備廣泛的檢測能力，為未來實際生產中減少人工檢測負擔，同時提升不鏽鋼泵浦產品品質水準。.
520 3 $a Stainless steel pumps are extensively utilized in drainage systems, irrigation and other agricultural applications, as well as in high-rise building equipment due to their high-efficiency fluid transportation in corrosive and durable environments. However, welding defects often occur due to variations in material properties, differences in the welding quality of personnel, and instability in process variables, which subsequently affect product quality. Traditionally, the identification of welding defects has relied on visual inspections by welders, which are susceptible to human error. This study employs deep learning technology to develop an automatic defect detection system for argon welding seams using convolutional neural networks. The system was trained using four models: VGG, Xception, ResNet-152, and DenseNet, which effectively detect good products and five different types of welding defects. The dataset used includes 5,448 grayscale images of welding seams, each sized at 299x299 pixels, divided into 80% training data and 20% validation data, and underwent training over 100Epochs. The experimental results demonstrate that the four models, VGG, Xception, ResNet-152, and DenseNet, can accurately classify welding seam categories, with the DenseNet model achieving the highest training accuracy of 97.5% and validation accuracy of 96.8%. The models were also tested on a dataset containing 544 grayscale welding seam images, achieving an accuracy of 96.7%. The results highlight the broad detection capabilities of the trained models, reducing the burden of manual inspections in actual production and enhancing the quality of stainless steel pump products..
563 $a (平裝)
650 # 4 $a Defect Detection. $3 1419456
650 # 4 $a Stainless Steel Pump. $3 1450025
650 # 4 $a Convolutional Neural Network. $3 1127421
650 # 4 $a Welding Defects. $3 1450024
650 # 4 $a Deep Learning. $3 1127423
650 # 4 $a 瑕疵檢測. $3 1099910
650 # 4 $a 不鏽鋼泵浦. $3 1450023
650 # 4 $a 卷積神經網路. $3 1127424
650 # 4 $a 焊接缺陷. $3 1450022
650 # 4 $a 深度學習. $3 1127425
856 7 # $u https://handle.ncl.edu.tw/11296/s7ep7a $z 電子資源 $2 http