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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 楊宏智(Hong-Tsu Young) | |
dc.contributor.author | Ding-Shian Liu | en |
dc.contributor.author | 呂定憲 | zh_TW |
dc.date.accessioned | 2021-06-08T03:32:55Z | - |
dc.date.copyright | 2019-08-13 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-08-07 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/21396 | - |
dc.description.abstract | 在工業4.0與智慧製造的浪潮下,工具機逐漸由自動化邁向智慧化,因此刀具磨耗預測與換刀時間點顯得更加重要。然而,比起常見的車、銑加工製程,與內螺紋攻牙有關的研究相對較少。攻牙是一項複雜的製程,相比於其他製程更為不穩定,容易伴隨著卡屑、斷刀及螺紋不合格等問題。此外,攻牙製程常為加工的最後幾道工序,若發生問題時要補救相當耗時費力,嚴重時甚至需要將整個工件報廢,造成巨大的損失。回顧文獻中對於攻牙的研究,大多是使用動力計直接測得攻牙時之扭矩值作為參考依據,但動力計價格昂貴、剛性不佳,不易在產線上被使用,因此本研究在不外加感測器的條件下,直接從工具機控制器擷取控制馬達的扭矩指令訊號,並利用卷積神經網路進行模型建立與刀具狀態分類。卷積神經網路在圖像和聲音的辨識上有相當優秀的成果,再加上近年來硬體設備的進步與演算法的優化,使得運算速度大幅提升。
本研究利用不同方式將控制器所擷取到之攻牙扭矩指令訊號轉換成圖片後,輸入卷積神經網路中進行模型訓練,再使用此模型將圖片分類成正常、嚴重磨耗及螺紋不合格三種類別,以利找出最佳的換刀時間點,並減少檢測螺紋的時間。當訊號被判定為刀具過度磨耗或崩刃時,則代表需要更換刀具;若訊號被判定為螺紋品質不合格時,便僅需針對該螺絲孔做出相對應之處理,不必再以螺紋塞規逐一檢測螺紋。本研究搭配卷積神經網路來判斷換刀時機與減少螺紋檢測時間,確實可增加刀具使用率和降低產線上所需的人力成本,具工業應用高度潛力。 | zh_TW |
dc.description.abstract | Recently, Industry 4.0 has become a trend for manufacturing industries, and under the core concept of the machine tools industry will be gradually evolved from pure automation to smart factories. The key issue resides in the monitoring of cutting tools conditions; consequently, the timing of the cutting tools changes. However, literatures on tapping are limited. Tapping is quite a complicated process, which is an unstable process and often accompanies with chip clogging, tool breakage, bad thread quality, and other failures. Moreover, since tapping is near the finishing process, the associated repair work is very time-consuming and laborious when problems should occur. If workpiece cannot be repaired, previous efforts may come in vain. Previous research on tapping shows torque signals were collected by dynamometers. However, dynamometers are expensive and not practical to be applied in production environments. In this study, torque command signals directly acquired from CNC controller are applied with a use of convolutional neural network. The model established is shown to distinguish healthy tool from the worn tool.
In the study, various approaches were used to convert torque command signals to images, which were then imported in convolutional neural network for model training. Convolutional neural network is particularly powerful in recognizing images and voices. In particular, the improvement of hardware and algorithms dramatically speeds up computing process. By means of the above approach, best timing to change taps can be determined and the time required to check threads can be drastically reduced. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T03:32:55Z (GMT). No. of bitstreams: 1 ntu-108-R06522735-1.pdf: 3756081 bytes, checksum: 1b29d30de4fa435cd8971a07f3578609 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 口試委員會審定書 I
誌謝 II 摘要 III Abstract IV 圖目錄 VIII 表目錄 XI 第一章 緒論 1 1.1 研究背景 1 1.1.1 由自動化邁向智慧化 1 1.1.2 卷積神經網路 1 1.2 研究動機與目的 2 1.3 研究方法 4 1.4 論文架構 5 第二章 文獻回顧 7 2.1 簡介 7 2.2 刀具壽命 7 2.3 刀具磨耗機制 7 2.4 絲攻的損壞判斷 8 2.5 刀具狀態監測方法 9 2.5.1 扭矩 10 2.5.2 軸向力 10 2.5.3 振動訊號 11 2.5.4 超音波 12 2.6 攻牙製程診斷 12 2.7 扭矩與電流關係 13 2.8 小結 14 第三章 研究方法 15 3.1 簡介 15 3.2 控制器與馬達 15 3.3 卷積神經網路 16 3.3.1 輸入層(Input Layer) 16 3.3.2 卷積層(Convolution Layer) 17 3.3.3 池化層(Pooling Layer) 18 3.3.4 激勵函數(Activation Function) 19 3.3.5 全連接層(Fully Connected Layer) 20 3.4 GoogLeNet神經網路 22 第四章 實驗設備與規劃 25 4.1 簡介 25 4.2 實驗設備 25 4.2.1 工具機與控制器 25 4.2.2 鑽頭與絲攻 27 4.2.3 夾持刀柄 28 4.2.4 實驗材料 29 4.2.5 螺紋塞規 31 4.3 實驗規劃 31 4.3.1 攻牙實驗 31 4.3.2 數據分析 33 第五章 實驗結果與討論 35 5.1 Servo Guide主軸扭矩指令-時間圖 35 5.2 數據分類 41 5.3 數據擷取與處理 42 5.3.1 擷取完整攻牙循環 42 5.3.2 僅擷取進刀時訊號 44 5.3.3 進刀訊號點資料展開 45 5.3.4 進刀訊號點資料重新排列成方形圖片 47 5.4 辨識結果 49 5.4.1 完整攻牙循環訓練結果 49 5.4.2 進刀時扭矩指令-時間圖之辨識結果 51 5.4.3 進刀訊號點資料展開之辨識結果 52 5.4.4 進刀訊號點資料重新排列之辨識結果 53 5.5 螺紋品質檢測 54 5.6 結果討論 56 5.6.1 不同數據處理方式比較 56 5.6.2 數據灰階縮放 58 5.6.3 螺紋檢測 58 第六章 結論與未來展望 59 6.1 結論 59 6.2 未來展望 59 | |
dc.language.iso | zh-TW | |
dc.title | 應用卷積神經網路於CNC攻牙之刀具狀態監測 | zh_TW |
dc.title | Application of Convolutional Neural Network on Monitoring Tool Conditions in CNC Tapping | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 李貫銘,鄭憶中,洪育民 | |
dc.subject.keyword | 卷積神經網路,攻牙刀具狀態,換刀時間判斷,主軸扭矩指令,螺紋品質檢測, | zh_TW |
dc.subject.keyword | convolutional neural network,tapping tool condition,timing of changing tools,spindle torque command,thread quality inspection, | en |
dc.relation.page | 62 | |
dc.identifier.doi | 10.6342/NTU201902830 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2019-08-08 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 機械工程學研究所 | zh_TW |
顯示於系所單位: | 機械工程學系 |
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