Development of Virtual Milling System Using Data Fusion and Transfer Learning

We Feng Kuo, Bo Min Huang, Ching Hung Lee*

*Corresponding author for this work

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

2 Scopus citations

Abstract

This study designs the virtual milling system to estimate the practical milling accuracy and surface roughness. Since it is difficult to collect large number of measured data from milling, we collect the data is with milling and data without milling by experiment design. The proposed virtual milling system has the ability to find the relationship between computer numerical control (CNC) milling parameters and performance indexes, we utilize the back propagation neural network (BPNN) to establish the first stage virtual milling system by calculated data (without milling), then fuse the practical milling data by transfer learning approach. Finally, the experimental results are shown to demonstrate the performance of the virtual milling system.

Original languageEnglish
Title of host publicationProceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages253-257
Number of pages5
ISBN (Electronic)9781665404839
DOIs
StatePublished - Dec 2020
Event1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020 - Taipei, Taiwan
Duration: 3 Dec 20205 Dec 2020

Publication series

NameProceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020

Conference

Conference1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020
Country/TerritoryTaiwan
CityTaipei
Period3/12/205/12/20

Keywords

  • CNC
  • Milling parameter
  • fusion
  • neural network
  • transfer learning

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