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  • 學位論文

微型鑽孔刀具壽命灰色預測系統之開發

The development of a Grey tool life prediction system for micro drilling operation

指導教授 : 黃博滄
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摘要


摘 要 隨著近年來市場趨勢的改變,機械加工從過去的大量生產,漸漸演變成了少量高品質的製造取向,這也使得品質的管控與生產前的預測行為越來越受到重視,因此近年來各界紛紛提出了各種方法進行預測,像是傳統的統計預測、模擬建模、回歸分析及各種軟性計算的方式,來建構預測系統,且精準度也有明顯的上升。但這些預測模型有個極大的缺點,就是皆需要大量的數據進行建模,對於目前及時少量生產的生產模式極為不利,不僅需耗費大量的時間收集數據,且一旦加工設置改變,就必須重新建模或調整,使用極為不便,這也是目前業界預測系統極為少用的原因之一。故本研究將透過灰色理論,針對微型鑽孔建構一刀具壽命預測系統,希望透過少量數據及可預測出刀具的狀況與壽命,且不受加工設定改變的影響。 本研究所開發之刀具壽命灰色預測系統,是透過灰色生成,將輸入數據顯著化其趨勢,並利用灰色預測其小樣本預測特性作為基本架構,建構及時預測系統。灰色預測是利用累加生成序列,利用數據間的趨勢特性建立小型模型預測資料。在本研究中是運用於CNC鑽孔加工,透過少量的孔徑數據,去預測刀具使用壽命,不僅不需考量使用何種刀具、工件、加工參數,甚至加工環境,皆可迅速的預測出此加工設定下刀具的使用壽命。 為了證實所提出的方法其準確性與可靠性,本研究將設置三種不同的刀具、工件與加工參數,進行微型鑽孔加工,先透過光學測量儀測量加工孔徑,並將少量孔徑值投入灰色預測系統,進行孔徑大小的趨勢預測,藉此推斷在此設定下刀具的使用壽命,並探討其準確性,以驗證本研究所提出之預測系統其可行性。

並列摘要


Abstract As the market trend changes over the years, production orientation of machining process has evolved from mass production to high quality, small-scale production; therefore, quality control and pre-production prediction is becoming increasingly important. Various prediction methods such as traditional statistical forecasting, simulation modeling, regression analysis and soft computing, have been proposed in recent years to construct prediction systems, which demonstrate significant increase in accuracy. However, there is a huge disadvantage to these prediction models. That is they require large amount of data to perform modeling, which is unfavorable to small-scale productions. Not only does it take much time for these systems to collect data, but remodeling or readjustment is required whenever the settings are changed. Due to the inconvenience, these prediction systems are rarely used in the industry. Therefore, in this research, we hope to construct a tool life prediction system for micro drilling process, which is able to predict a tool’s condition and operating life based on only a small amount of data and without the influence of the changed process settings. The Grey Micro Drill Tool Life Prediction System proposed in this research is developed through grey generating, a method which emphasizes the input data trend and uses its small sample feature as the fundamental structure for building a real time prediction system. Grey prediction is a small-scale prediction model built upon accumulated generating sequence and the forecasting characteristics among data. It is used on CNC drilling process in this research in which a tool’s operating life is predicted based on a small amount of hole diameter data. The grey prediction system can quickly predict a tool’s operating life under specific processing settings without considering the type of tool and work part being used, the machining parameters and environment. To prove the proposed method is both accurate and reliable, three different sets of tools, work part and machining parameters are used to perform micro drilling. First, an optical instrument is used to measure the diameter of the machined holes, and a small amount of the measured data are placed in the grey prediction system for the forecasting of hole sizes. Based on the information obtained, we can determine a tool’s operating life and investigate its accuracy to verify the feasibility of the prediction system proposed in this research.

參考文獻


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被引用紀錄


林明慶(2016)。建構田口殘差修正GM(1,1)灰模型之微鑽孔刀具壽命預測系統〔碩士論文,中原大學〕。華藝線上圖書館。https://doi.org/10.6840%2fcycu201600736

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