scholarly journals Data-Driven Microstructure and Microhardness Design in Additive Manufacturing Using a Self-Organizing Map

Engineering ◽  
2019 ◽  
Vol 5 (4) ◽  
pp. 730-735 ◽  
Author(s):  
Zhengtao Gan ◽  
Hengyang Li ◽  
Sarah J. Wolff ◽  
Jennifer L. Bennett ◽  
Gregory Hyatt ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Data Driven ◽  
Self Organizing Map ◽  
Self Organizing

Design of a Data Driven Control System with a Self-Organizing Map

2012 ◽  
Vol 132 (11) ◽  
pp. 1813-1818
Author(s):  
Yuta Mitanda ◽  
Masaru Katayama ◽  
Toru Yamamoto
Keyword(s):  
Control System ◽  
Data Driven ◽  
Self Organizing Map ◽  
Self Organizing

A Novel Parallel Model for Self-Organizing Map and its Efficient Implementation on a Data-Driven Multiprocessor

2003 ◽  
Vol 7 (3) ◽  
pp. 355-361 ◽  
Author(s):  
Ruck Thawonmas ◽  
◽  
Makoto Iwata ◽  
Satoshi Fukunaga ◽  
◽  
...  
Keyword(s):  
Large Data ◽  
Data Driven ◽  
Data Sets ◽  
Self Organizing Map ◽  
Parallel Model ◽  
Dynamic Data ◽  
Training Time ◽  
Load Imbalance ◽  
Time Critical ◽  
Self Organizing

The self-organizing map (SOM), with its related extensions, is one of the most widely used artificial neural algorithms in unsupervised learning and a wide variety of applications. Dealing with very large data sets, however, the training time on a single processor is too high to be acceptable for time-critical application domains. To cope with this problem, we present a scheme consisting of a novel parallel model and its implementation on a dynamic data-driven multiprocessor. The parallel model ensures that no load imbalance will occur, while the dynamic data-driven multiprocessor yields high scalability. We demonstrate the effectiveness of the scheme by comparing the parallel model with an existing parallel model, and the proposed implementation with an implementation on another multiprocessor.

scholarly journals Quantifying Geometric Accuracy With Unsupervised Machine Learning: Using Self-Organizing Map on Fused Filament Fabrication Additive Manufacturing Parts

2017 ◽  
Vol 140 (3) ◽  
Author(s):  
Mojtaba Khanzadeh ◽  
Prahalada Rao ◽  
Ruholla Jafari-Marandi ◽  
Brian K. Smith ◽  
Mark A. Tschopp ◽  
...  
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Process Conditions ◽  
Complex Geometries ◽  
Self Organizing Map ◽  
Geometric Accuracy ◽  
Fused Filament Fabrication ◽  
Unsupervised Machine Learning ◽  
Geometric Deviations ◽  
Self Organizing

Although complex geometries are attainable with additive manufacturing (AM), a major barrier preventing its use in mission-critical applications is the lack of geometric accuracy of AM parts. Existing geometric dimensioning and tolerancing (GD&T) characteristics are defined based on simple landmark features, and thus, need to be customized to capture the subtle difference in parts with complex geometries. Hence, the objective of this work is to quantify the geometric deviations of additively manufactured parts from a large data set of laser-scanned coordinates using an unsupervised machine learning (ML) approach called the self-organizing map (SOM). The central hypothesis is that clusters recognized by the SOM correspond to specific types of geometric deviations, which in turn are linked to certain AM process conditions. This hypothesis is tested on parts made while varying process conditions in the fused filament fabrication (FFF) AM process. The outcomes of this research are as follows: (1) visualizing and quantifying the link between process conditions and geometric accuracy in FFF and (2) significantly reducing the amount of point cloud data required for characterizing of geometric accuracy. The significance of this research is that this unsupervised ML approach resulted in less than 3% of over 1 million data points being required to fully quantify the part geometric accuracy.

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing

An Auscultaiting Diagnosis Support System for Assessing Hemodialysis Shunt Stenosis by Using Self-organizing Map

2011 ◽  
Vol 131 (1) ◽  
pp. 160-166 ◽  
Author(s):  
Yutaka Suzuki ◽  
Mizuya Fukasawa ◽  
Osamu Sakata ◽  
Hatsuhiro Kato ◽  
Asobu Hattori ◽  
...  
Keyword(s):  
Support System ◽  
Self Organizing Map ◽  
Self Organizing

Classification of Heat Wave Events in Seoul Using Self-Organizing Map

2018 ◽  
Vol 9 (3) ◽  
pp. 209-221 ◽  
Author(s):  
Seung-Yoon Back ◽  
Sang-Wook Kim ◽  
Myung-Il Jung ◽  
Joon-Woo Roh ◽  
Seok-Woo Son
Keyword(s):  
Heat Wave ◽  
Self Organizing Map ◽  
Self Organizing
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