平面銑削之灰色即時可調式學習表面粗糙度預測系統開發

The Development of a Gray In-process Adaptive Learning Surface Roughness Prediction System in Milling Operations

張黃傑(Huang-Jie Chang)

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(2012年)

https://doi.org/10.6840/cycu201300859

摘要

隨著企業之間的競爭，成本及品質的管控也日漸成為必然的，而對於生產前的預測行為也於近年來備受重視，因此預測系統的影響也逐漸重要，其中模擬建模、統計預測、迴歸分析及各軟性計算等已被廣泛運用於各預測系統中，其精準度也日漸提升，但皆需要大量數據才得以建構，對於少批量生產及運用於生產新產品上，則難以使用，故本研究希望建構一小樣本即時預測系統，運用在生產初期樣本數不足的情況，並可依據不同規格的加工參數即時建置不同之模型。

本研究以開發GIALSR預測系統，透過灰色關聯分析選定輸入因子，並利用灰色理論之小樣本預測特性做為基本架構，建構即時預測系統，灰色預測是透過因子與結果之累加生成序列、灰色背景值及因子參數加以運算，並利用數據間的趨勢特性建立小型模型預測資料，而本研究將運用該系統於銑削加工的表面粗糙度預測實例中，透過探討銑削加工時之即時切削力與粗糙度的關係，找出關聯度較高之切削力，並利用試產階段之少數資料建立可調式粗糙度即時預測模型。

為了證明提出之方法的可靠性與準確性，本研究運用新發展的GIALSR預測系統於銑削加工的表面粗糙度預測實例中，建置出即時表面粗糙度預測系統，並與傳統大樣本模型之類神經模型比較其差異性，驗證即時建構之小樣本模型與大樣本模型並無顯著差異，進而得到小型預測系統的可行性，而研究結果顯示，透過因子探討及運用灰色理論所建置之GIALSR預測系統，在精準度上與大樣本建模並無顯著差異，對於即時建置GIALSR預測系統之方法是可行的。

關鍵字

表面粗糙度 ； 灰色關聯分析 ； 灰色預測

並列摘要

With competitions between enterprises, it is importance and necessary to control costs and quality of products. Therefore, the development of prediction system is tending to become a significant role in last decades. The models of the system including model simulation, statistical prediction, regression analysis and various soft computing have been generally used in the prediction system. its accuracy is also increasing gradually. However, the major component of building the models is a large number of data. Under the circumstance, the prediction system is difficult to be implemented under the small batch and new producing lines situations. The purpose of this study is to develop a real-time adaptive learning prediction system, which can be utilized in a small sample, such as production shortfall in the early stages of producing. Moreover, this model can be adjusted in accordance with different specifications of operation conditions.

This research is to develop a gray prediction system associated with gray relational analysis, which is use select the input factors of the system. Furthermore, a gray prediction theory would work as framework to build a real-time adaptive learning prediction system with a small number of data. The gray prediction implemented the result of accumulated generating operation, the calculation of gray values, effect data and establishes information anticipation with a small-scale model through the trend of characteristics of. This study applied the model to predict the surface roughness in milling operations. The correlation between cutting forces and surface roughness has been analyzed. With proper cutting force as input factors, a gray real time adaptive learning surface roughness prediction system in milling operations is built. The system does not need any training data in advance to construct the prediction model. It would only use limited data from trial production or set up to create the real-time prediction model for each production batch.

To evaluate reliability and accuracy of the system, the model would be compared with a traditional neural network prediction model of a large sample. The result of comparison between these two models shows no significant difference. The time and cost of collecting and data and training model are significantly reduced.

並列關鍵字

Surface roughness ； Grey relational analysis ； Grey prediction

參考文獻

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