Assessment of glass-to-glass welding by USP lasers with machine learning approaches

Yi Mo Ho, Cheng Hsun Lee, Jeng Rong Ho, Chih Kuang Lin, Pi Cheng Tung, Yuan Shin Lee

Research output: Contribution to journalArticlepeer-review

Abstract

Glass welding using ultra-short pulsed (USP) lasers has become one of the promising technologies in the past decades. With appropriate settings of parameters, USP lasers can provide many advantages for glass welding. However, there is still a lack of studies focused on predictions and relationships between successful welding and its correlates by machine learning models from glass welding experimental data, causing no guidance when implementing such experiments in the laboratory. In this study, we report the results of glass welding using a femtosecond laser system. The welding conditions (i.e., success or failure) under different process parameters such as focal position, power, and the other four parameters are analyzed by Neural Network (NN), Logistic Regression (LR), and Classification and Regression Tree (CART). The prediction accuracies of the models are from 84.3% to 97.3%. In other words, the process parameters can be applied to similar experiments to enhance the success rate of glass welding using USP lasers. Therefore, this study can fill the gap of lacking analytical results on predictions and relationships between successful welding and its process parameters in glass welding using USP lasers.

Original languageEnglish
Pages (from-to)160-165
Number of pages6
JournalManufacturing Letters
Volume35
DOIs
StatePublished - Aug 2023

Keywords

  • Femtosecond lasers
  • Glass machining
  • Glass welding
  • Machine learning

Fingerprint

Dive into the research topics of 'Assessment of glass-to-glass welding by USP lasers with machine learning approaches'. Together they form a unique fingerprint.

Cite this