Predictive Monitoring of Wirebond Ultrasonic Signal on Electrical Test Result Using Machine Learning

This study presents the development of a cost-effective predictive monitoring system interfaced to an existing wirebonding machine to determine its corresponding electrical test result. The system was tested on one production lot with the data gathered subjected to a machine learning process to pred...

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Main Authors: Haldos, Reymart Rio C, Reyes, Rosula SJ, Abu, Patricia Angela R, Oppus, Carlos M
Format: text
Published: Archīum Ateneo 2021
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Online Access:https://archium.ateneo.edu/discs-faculty-pubs/237
https://ieeexplore.ieee.org/abstract/document/9616687
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Institution: Ateneo De Manila University
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spelling ph-ateneo-arc.discs-faculty-pubs-12272022-01-31T06:20:28Z Predictive Monitoring of Wirebond Ultrasonic Signal on Electrical Test Result Using Machine Learning Haldos, Reymart Rio C Reyes, Rosula SJ Abu, Patricia Angela R Oppus, Carlos M This study presents the development of a cost-effective predictive monitoring system interfaced to an existing wirebonding machine to determine its corresponding electrical test result. The system was tested on one production lot with the data gathered subjected to a machine learning process to predict if the gathered signal will be rejected and will become a possible failure at later electrical testing. Three classification algorithms: Logistic Regression, Decision Tree, and Support Vector Machine were used to evaluate which prediction algorithm provides the expected electrical results. The study shows that 98% to 99 % accuracy was achieved in order to predict whether the lot will produce high production yield or not. Its embedded design approach lend itself well to real-time operation. More importantly this study would provide a way for legacy manufacturing equipment to be upgraded and thus be integrated into other system that are designed for “Industry 4. 0” implementation. 2021-01-01T08:00:00Z text https://archium.ateneo.edu/discs-faculty-pubs/237 https://ieeexplore.ieee.org/abstract/document/9616687 Department of Information Systems & Computer Science Faculty Publications Archīum Ateneo support vector machines machine learning algorithms production machine learning prediction algorithms acoustics manufacturing ultrasonic signal prediction embedded system Computer Sciences Databases and Information Systems
institution Ateneo De Manila University
building Ateneo De Manila University Library
continent Asia
country Philippines
Philippines
content_provider Ateneo De Manila University Library
collection archium.Ateneo Institutional Repository
topic support vector machines
machine learning algorithms
production machine learning
prediction algorithms
acoustics
manufacturing
ultrasonic signal
prediction
embedded system
Computer Sciences
Databases and Information Systems
spellingShingle support vector machines
machine learning algorithms
production machine learning
prediction algorithms
acoustics
manufacturing
ultrasonic signal
prediction
embedded system
Computer Sciences
Databases and Information Systems
Haldos, Reymart Rio C
Reyes, Rosula SJ
Abu, Patricia Angela R
Oppus, Carlos M
Predictive Monitoring of Wirebond Ultrasonic Signal on Electrical Test Result Using Machine Learning
description This study presents the development of a cost-effective predictive monitoring system interfaced to an existing wirebonding machine to determine its corresponding electrical test result. The system was tested on one production lot with the data gathered subjected to a machine learning process to predict if the gathered signal will be rejected and will become a possible failure at later electrical testing. Three classification algorithms: Logistic Regression, Decision Tree, and Support Vector Machine were used to evaluate which prediction algorithm provides the expected electrical results. The study shows that 98% to 99 % accuracy was achieved in order to predict whether the lot will produce high production yield or not. Its embedded design approach lend itself well to real-time operation. More importantly this study would provide a way for legacy manufacturing equipment to be upgraded and thus be integrated into other system that are designed for “Industry 4. 0” implementation.
format text
author Haldos, Reymart Rio C
Reyes, Rosula SJ
Abu, Patricia Angela R
Oppus, Carlos M
author_facet Haldos, Reymart Rio C
Reyes, Rosula SJ
Abu, Patricia Angela R
Oppus, Carlos M
author_sort Haldos, Reymart Rio C
title Predictive Monitoring of Wirebond Ultrasonic Signal on Electrical Test Result Using Machine Learning
title_short Predictive Monitoring of Wirebond Ultrasonic Signal on Electrical Test Result Using Machine Learning
title_full Predictive Monitoring of Wirebond Ultrasonic Signal on Electrical Test Result Using Machine Learning
title_fullStr Predictive Monitoring of Wirebond Ultrasonic Signal on Electrical Test Result Using Machine Learning
title_full_unstemmed Predictive Monitoring of Wirebond Ultrasonic Signal on Electrical Test Result Using Machine Learning
title_sort predictive monitoring of wirebond ultrasonic signal on electrical test result using machine learning
publisher Archīum Ateneo
publishDate 2021
url https://archium.ateneo.edu/discs-faculty-pubs/237
https://ieeexplore.ieee.org/abstract/document/9616687
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