運用移動全距管制於灰色即時建模表面粗糙度預測之研究

A Study of Applying MR Control in Gray Online Modeling Surface Roughness Prediction.

余忠河(Chung-He Yu)

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(2017年)

https://doi.org/10.6840/cycu201700786

摘要

現今眾多產品皆具有少量多樣、生命週期短的趨勢，對於產品的品質要求可說是越來越嚴格。銑削在加工業屬於最常見的方式，而在加工製程中，加工品表面粗糙度的優劣會反映刀具的損壞程度及製程設置的妥善率，表面粗糙度的掌控會對品質的好壞產生極大的影響，為了能得到準確的表面粗糙度，許多學者希望利用預測的方式，一方面以監控製程的變異，另一方面則能有效降低其額外成本。而在不同加工環境及設置下，有許多不可控因素是人為無法掌握的。因此，感測技術的出現得以監控製程中所產生的變異，進而分析製程因外在因素所造成的影響，不僅能夠有效降低不良率，更能有效提升預測時的準確度。

傳統的預測模型需要大量數據才有辦法進行建模，這對於現今加工的方式並不適用，甚至會因為不同加工條件需要重新調整參數，使用上極為不方便。因此，本研究
將使用灰色理論作為預測方法，透過灰關聯選取製程中關聯度最強的感測數據，並利用移動全距管制圖作為雜訊過濾的方法，最後將篩選結果投入GM(1,N)灰色預測模型，透過累加生成(Accumulated Generating Operatin,AGO)找出數據間的趨勢特性以建立預測系統。本研究則是運用於電腦數值控制器(Computer Numerical Control, CNC)銑削加工，能以少量數據去預測表面粗糙度，能夠達到即時建模的效果，管制界限的建立也能有效預防雜訊因子投入預測模型以影響預測精準度。

為了驗證本研究所提出的方法有其準確性及可行性，實驗設置兩組不同加工參數，投入預測系統少量的數據，進行表面粗糙度的預測。兩組實驗結果預測準確率分別為97.86%及98.02%，得以證實本研究提出之預測系統的準確性及可行性。

關鍵字

灰色理論 ； 銑削加工 ； CNC ； 表面粗糙度 ； 品質管制 ； 感測技術

並列摘要

In nowadays product property of diverse and few and short life time, the need of the quality becomes more and more strict. Milling in a common way in processing, and the roughness will affect the damage level of cutting tool and the rate of manufacturing plan, to grasp the roughness will affect the quality and in order to get the accuracy of roughness lots of scholar wish to use predict, monitor the process variation and decrease the cost on the other hand. Under the different process environment and plan, there are lots of element that cannot be grasped. Therefore the sensing technology cam help to monitor the variation in the process, analysis the process effect that cause by external element. This allows to decrease the defective rate and improve the accuracy of prediction.
Traditional prediction needs lot of data to model which does not fit to the present process, moreover, it needs to modify under different process. Therefore, we use Gary theory to predict and chose the strongest sensing data through the use of Grey Relational Analysis and use moving range control chart to filter the noise and put the final data in GM(1,N) and find the trend property through Accumulated Generating Operating (AGO) for prediction system. In the research we use Computer Numerical Control (CNC) milling, it is able to use less data for predicting roughness and are able to model in real time also through the setup of control boundary it will be able to prevent noise in the forecast model which effect the prediction accuracy.
In order to verify the accuracy and feasibility of this research, we use two different process parameter apply few data in the prediction system and predict the roughness. The result shows the accuracy are 97.56% and 98.02% which are able to verify the accuracy and feasibility.

並列關鍵字

Gray theory ； Milling ； CNC ； roughness ； quality control ； Sensing technology

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