Self-Organizing Map - Wear studies - FDM

Published: 6 September 2021| Version 1 | DOI: 10.17632/92ytjzwkv7.1
Balamurugan Karnan


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.


Steps to reproduce

Through customized filament extruder, PLA/CU filament are manufactured and printed using FDM with identified printing condition. the samples ae wear tested using Pin-on Disc machine and the observations are tabulated. A s


Engineering, Model-Based Clustering