Implementation of Visual Clustering Strategy in Self-Organizing Map for Wear Studies Samples Printed Using FDM
Implementation of Visual Clustering Strategy in Self-Organizing Map for Wear Studies Samples Printed Using FDM
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DOI：
10.18280/ts.390215
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发表时间：
2022-04-01
影响因子：
1.9
通讯作者：
Karnan, Balamurugan
中科院分区：
文献类型：
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作者：
Pugazhendhi, Latchoumi Thamarai;Kothandaraman, Raja;Karnan, Balamurugan
一般来说，视觉聚类优于大型数据集；这是一种利用聚类技术来降低小数据集的数学复杂性的尝试。为了确定在小数据集中实现聚类技术的可能性，考虑了使用熔融沉积模型（FDM）打印的PLA/Cu复合材料样品的磨损观察结果。在本研究中，使用自组织映射（SOM）工具作为非监督神经网络（NN）来可视化数据。本文采用矢量量化和投影相结合的SOM方法，对不同FDM条件下打印的新型复合材料长丝的磨损可加工性参数进行识别或排序。SOM中的竞争层将在任意维数上将磨损机的给定参数（向量）分类为几组层神经元。SOM的限制是地图的大小，不能超过1000个训练单元。但是，对于考虑的小数据集，这些限制的范围不会影响性能。为研究磨损而开发的SOM算法提供了可接受范围内的出口。此外，对输出响应进行线性回归分析，以测量加工观察的磨损特性。
In general, visual clusters are preferred over large data sets; this is an attempt to take advantage of cluster techniques to reduce the mathematical complexity of small data sets. To identify the possibility of implementing the clustering technique in a small dataset, the wear observations of PLA/Cu composite samples printed using the Fused Deposition Model (FDM) is taken into consideration. In this study, the Self Organizing Map (SOM) tool as a non-supervised Neural Network (NN) is used to visualize the data. Here, SOM combinations with vector quantification and projection are used to identify or rank the wear machinability parameters on the new composite filament printed under different FDM conditions. The competitive layer in SOM will classify the given parameters of the wear machine (vectors) at any number of dimensions may be into several groups of layer neurons. The limitation of SOM is map size which cannot exceed 1000 units of training. However, for the small data set under consideration, the extent of these limits will not affect performance. The SOM algorithm developed for the study of wear provides the outlet within the acceptable range. In addition, the linear regression analysis is carried out for the output response to measure the wear characteristics of the machining observation.