Applying Clustering Analysis to Arrange Material Mixing Operation
| Autor: | Tsung-Sheng Cheng, 陳宗聖 |
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| Rok vydání: | 2011 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 99 The Cluster Analysis can be acclaimed one of the most important methodologies for database establishment and analysis. In case of the necessity of analyzing excessive volume of data or incompetence of validating the research target, such the Cluster Analysis can be applied in processing the data required to be validated for classifying the data with similar attributes into one corresponding category and for creating each individual cluster constituted of data with high similarity attributes and a mechanism that there is a low relevancy among each of all individual clusters of data. Currently, the Cluster Analysis methodology includes such varieties as Multivariate Statistics Analysis, Artificial Neural Network or Genetic Algorithm in which the Multivariate Statistics Analysis has been applied most frequently. In the tire industry, the manufacture materials mixture shall be the preliminary manufacture process. The required mixture of plastic materials and the proprietary mixture composition formula vary with the tire intended to be produced. In case of a tire manufacture necessitating arrangement for hundreds of manufacture material composition formulas, the manufacture process scheduling for producing each individual type of tire would be significantly complicated and can definitely influence the working efficiency of manufacture material mixture substantially. Under the aforesaid concern, the data analysis for some rubber manufacture materials are thus selected as the case study for this research with intention for searching the improvement measures for enhancement of the working performance and efficiency of the raw rubber manufacturing materials mixers by means of applying the aforesaid Cluster Analysis in clustering and classification of raw rubber materials mixture works in progress. The TwoStep Cluster Analysis methodology is adopted by this research in which: in the 1st Phase, hierarchical clustering method (the Nearest Neighbor method and Average Linkage Method the Wards Method) are utilized for determination of the total number of cluster classification categories; and in the 2nd Phase, the K-Means statistics methodology is performed for validating and finalizing the total required number of the cluster analysis categories. Afterwards, the Hierarchical Cluster Analysis is conducted in this research for making comparisons and thus concluding that the Wards Method demonstrates a better Cluster Analysis performance and efficiency. Therefore, it can conclude a feasibility in this research that the required mixture of raw rubber manufacture materials shall be classified into two categories in conjunction with usage of two material mixers for executing the works in progress simultaneously. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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