Application of regression neuron in predicting surface roughness in end milling operations
| Autor: | Chu-Hsien Chang, 張竹賢 |
|---|---|
| Rok vydání: | 2010 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 98 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. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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