Title

應用迴歸類神經預測銑削加工之表面粗糙度

Translated Titles

Application of regression neuron in predicting surface roughness in end milling operations

DOI

10.6840/CYCU.2010.00393

Authors

張竹賢

Key Words

迴歸分析 ; 類神經網路 ; 預測 ; 表面粗糙度 ; 銑削加工 ; regression ; surface roughness ; predict ; end milling ; artificial neural network

PublicationName

中原大學工業與系統工程研究所學位論文

Volume or Term/Year and Month of Publication

2010年

Academic Degree Category

碩士

Advisor

黃博滄

Content Language

繁體中文

Chinese Abstract

現階段全球面臨金融海嘯的衝擊,各產業為因應此一衝擊多以降低成本、減少浪費以提高生產效率和利潤,來幫助企業達到本身目標與願景,此時在正確的時候做正確的事以及減少浪費就成了各大企業首要達成的目標。為了達成此目標,整合品質管控是在各企業製造過程中一直是很重要的一門。然而有效的達到品質的管控,必須利用方法來降低不良率、提高生產力,如QC七大手法、6標準差、QCC…等,能有效善用這些方法來達到品質的管控最常使用的方法是透過品質的量測。 在製造工業中,表面粗糙度是評量產品品質的一項重要指標。表面粗糙度直接影響著零件表面的耐磨性、可靠性、疲勞強度、密封性、導熱性和傳動精度等等,因此表面粗糙度是評定零件表面粗糙狀況、反映零件質量優劣的一項重要指標,尤其在機械加工中已成為零件生產加工中一項必不可少的質量要求。 在過去的研究中,建構出一套精準的預測決策系統不勝凡舉,該如何提高決策系統的預測精準度一直是研究人員的目標。本論文利用迴歸分析與類神經網路的結合,發展出一套迴歸類神經網路預測系統,目的是有效的預測工件加工後的產品表面粗糙度,經由迴歸分析解釋變數的能力與分類結合類神經網路的自我學習預測能力,降低單一類神經網路預測時資料的變異程度,提升整體預測系統的精準度。

English Abstract

Every industry hopes to reduce the cost, the waste and promote the productivity and efficiency to achieve the goal and vision. For achieving this goal, quality control is the most important role in manufacturing processing. Therefore, how to reach quality control efficiency must using some kinds of method to reduce the fail rate and promote the productivity, ex: Quality 7 Tools, 6 sigma and QCC etc. However, using this methods to achieve quality control, we usually through workpiece measurement. In the manufacturing industry, the surface roughness is an important index to evaluate product quality. Surface roughness direct impacting surface wear resistance, fatigue strength, reliability and leak-proof quality, thermal conductivity and drive precision and so on, so the surface roughness is an important index assessing the surface conditions, reflecting the quality, especially in the machinery processing has become an essential quality requirements. In the past, has many of constructing a prediction decision-making system research. But how to promote the decision-making system precision is researchers hope. This research using combination of Regression and Neural Network to develop a regression neural network prediction model, the purpose is predicting the surface roughness efficiency, reducing the variation of data and promote system precision.

Topic Category 電機資訊學院 > 工業與系統工程研究所
工程學 > 工程學總論
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