以灰色理論開發硬質合金銑削刀片壽命預測系統之研究

The development of a tool life prediction system for milling process based on Grey system theory

呂柏毅(Po-Yi Lu)

指導教授 ： 黃博滄

中原大學/電機資訊學院/工業與系統工程研究所/碩士(2015年)

https://doi.org/10.6840/cycu201500822

摘要

隨著近年來隨著市場趨勢的改變，機械加工從過去的大量生產，漸漸走向少量高品質，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，像是統計預測、模擬建模、回歸分析，來建構預測系統，且精準度都相當不錯。但這些預測模型有個的缺點，就是皆需要大量的數據進行建模，對於目前即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，就必須重新建模及調整，使用相當不便，這也是目前業界預測系統鮮少使用的原因之一。而灰色理論恰巧與上述理論相反，灰色理論能以“少數據”、“少信息”找到資料的趨勢並加以優化，故本研究將透過灰色理論，針對銑削加工建構一刀具壽命預測系統，希望透過少量數據即可預測出刀具的狀況與壽命，且不受加工設定改變的影響。
本研究所開發之灰色硬質合金刀片壽命預測系統，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，建構即時預測系統。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銑削加工，透過少量的表面粗糙度數據，去預測刀具使用壽命，不僅不需考量加工參數、加工環境，也不需要任何感測器即可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，本研究將設置三組不同加工參數進行銑削加工，並將少量表面粗糙度值投入灰色預測系統，進行表面粗糙度的趨勢預測，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。

關鍵字

表面粗糙度 ； 灰色預測 ； 捨棄式刀片 ； 刀具壽命

並列摘要

As the changes of the market trend over the years, production orientation of machining process has evolved from mass production to high-quality, small-volume production; therefore, quality control and production prediction is becoming increasingly important. Some prediction methods such as statistical forecasting, simulation modeling, regression analysis and soft computing, have been proposed in recent years to construct prediction systems, which demonstrate significant increasing in accuracy. However, there is a defect to these prediction models. That is they require large amount of data to perform modeling, which is not suitable to small-volume productions. Not only does it take too much time for these systems to collect data, but remodeling or readjustment is required every time once the settings are changed. Due to the inconvenience, these prediction systems are rarely used in the industry. While the Grey theory contrary coincided with the above theory, Grey theory can utilize "small data", "small information" to find the information trend and optimize it. Therefore, in this research, a tool life prediction system for milling process has been constructed, which is able to predict a tool condition and tool life based on only a small amount of data and without the influence of the changed process settings.
　　The Grey carbide inserts of tool life prediction system for milling operations is developed in this research through grey generating, a method which emphasizes the input data trend and uses its small sample feature as the fundamental structure for building an in-process prediction system. Grey prediction is a small-volume prediction model built upon accumulated generating sequence and the forecasting characteristics among data. It is used on CNC milling process in this research in which a tool life is predicted based on a small amount of surface roughness data. The grey prediction system can quickly predict a tool life under specific processing settings without considering the machining parameters and environment, also does not need any sensors.
　　To prove the proposed method is both accurate and reliable, three different sets of machining parameters are used to perform the milling operations. A small amount of the surface roughness data is placed in the grey prediction system for the prediction of surface roughness. Based on the information obtained, we can determine a tool life is determined and its accuracy is investigated to verify the feasibility of the prediction system.

並列關鍵字

surface roughness ； grey prediction ； throwaway tips ； tool life

參考文獻

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