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A Cluster-Profile Comparative Study on Machining AlSi 7 /63% of SiC Hybrid Composite Using Agglomerative Hierarchical Clustering and K-Means
Silicon ( IF 3.4 ) Pub Date : 2020-06-03 , DOI: 10.1007/s12633-020-00447-9
Pruthviraju Garikapati , K. Balamurugan , T. P. Latchoumi , Ramakrishna Malkapuram

Clustering techniques are used to group the data based on the structure or through classification to reduce the mathematical complexity of large datasets. The hierarchical and partitioning approach are two broad clustering/classification techniques in data-mining. An attempt has been made to study the possibilities of taking the advantages of these approaches to machining. AlSi7/63% of SiC hybrid composite prepared by stir casting technique is machined using the Abrasive Water Jet Machine (AWJM) for Taguchi’s L27 Orthogonal Array (OA). Water Pressure, cutting distance, and cutting Speed are taken as independent parameters. Material Removal Rate (MRR), Kerf Angle (KA) and Surface profile Roughness (Ra) are taken as dependent responses. Support Vector Machine (SVM) classifiers with Agglomerative Hierarchical Clustering (AHC) classifies L27 OA into three classes of nine observations each. To compare and explore the difference between the partitional clustering and hierarchical clustering techniques at the same level of class, the study on K-means value is taken as 3 because of AHC group L27 OA into three classes. The value of K is fixed with three and it group into three classes of nine observations each. XLSTAT software is used for the analysis of AHC and K-means. Further, linear regression equations are developed for each class/classification of AHC and K-means and compared with the experimental observations. The analysis reveals that K-means classification based on the partitioning approach fits best with the experimental observations. AHC develops a single equation for all the classes, whereas K-means develops individual equations for all its classes.



中文翻译:

凝聚层次聚类和K-均值法加工SiC杂化复合材料中AlSi 7/63%的簇状比较研究

聚类技术用于根据结构或通过分类对数据进行分组,以降低大型数据集的数学复杂性。分层和分区方法是数据挖掘中的两种广泛的群集/分类技术。已经尝试研究利用这些方法的优势进行加工的可能性。铝硅7使用Taguchi的L27正交阵列(OA)的磨料射流机(AWJM)对通过搅拌铸造技术制备的SiC杂化复合材料的/ 63%进行了加工。水压,切割距离和切割速度被视为独立参数。材料去除率(MRR),切尔夫角(KA)和表面轮廓粗糙度(Ra)被视为从属响应。支持聚类聚类(AHC)的支持向量机(SVM)分类器将L27 OA分为三类,每组九个观测值。为了比较和探讨同一级别上的分区聚类和分层聚类技术之间的差异,将AHC组L27 OA分为三类,将K-means值的研究作为3。K的值固定为3,并将其分为三类,每组九个观测值。XLSTAT软件用于分析AHC和K-均值。此外,针对AHC和K-均值的每个类别/分类开发了线性回归方程,并与实验观察结果进行了比较。分析表明,基于分区方法的K-means分类最适合实验观察。AHC为所有类别开发一个方程,而K-means为所有类别开发一个方程。

更新日期:2020-06-03
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