Increasing Quality Control of Ultrasonically Welded Joints Through Gaussian Process Regression

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Due to the recent advances in digitisation of the manufacturing industry and the generation of manufacturing data, there is increasing interest to integrate machine learning on the shop floor to improve efficiency and quality control. Ultrasonic welding is an emerging joining process used in various manufacturing industries, and is an energy efficient, cost-effective method of joining similar or dissimilar materials. However, the quality of the joint achievable is heavily dependent on process input parameters. In this study, a Gaussian Process Regression (GPR) model is developed to map the relationship between process parameters and joint performance for ultrasonically welded aluminium joints, with a view to improving quality control in a manufacturing setting. Initially, a 33 full factorial design of experiments is conducted to investigate the influential parameters, then a GPR model is trained on the experimental data. In-process sensor data is also used to infer process performance. To assess the prediction performance of the model, ten unseen parameter combinations are predicted and compared to their respective experimental result. The model demonstrates a high level of accuracy producing a Pearson’s correlation coefficient of 0.982 between the predicted and actual results for all data. The mean relative predictive error for unseen data is 7.35%.

Original languageEnglish
Title of host publicationFlexible Automation and Intelligent Manufacturing
Subtitle of host publicationThe Human-Data-Technology Nexus - Proceedings of FAIM 2022
EditorsKyoung-Yun Kim, Jeremy Rickli, Leslie Monplaisir
PublisherSpringer Science and Business Media Deutschland GmbH
Pages368-378
Number of pages11
ISBN (Print)9783031176289
DOIs
Publication statusPublished - 2023
Event31st International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022 - Detroit, United States
Duration: 19 Jun 202223 Jun 2022

Publication series

NameLecture Notes in Mechanical Engineering
ISSN (Print)2195-4356
ISSN (Electronic)2195-4364

Conference

Conference31st International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022
Country/TerritoryUnited States
CityDetroit
Period19/06/2223/06/22

Keywords

  • Gaussian process regression
  • Machine learning
  • Quality control
  • Ultrasonic welding

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