Dimensional prediction for FDM machines using artificial neural network and support vector regression

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

13 Scopus citations

Abstract

With the development of Fused Deposition Modeling (FDM) technology, the quality of fabricated parts is getting more attention. The present study highlights the predictive model for dimensional accuracy in the FDM process. Three process parameters, namely extruder temperature, layer thickness, and infill density, are considered in the model. To achieve better prediction accuracy, three models are studied, namely multivariate linear regression, Artificial Neural Network (ANN), and Support Vector Regression (SVR). The models are used to characterize the complex relationship between the input variables and dimensions of fabricated parts. Based on the experimental data set, it is found that the ANN model performs better than the multivariate linear regression and SVR models. The ANN model is able to study more quality characteristics of fabricated parts with more process parameters of FDM.

Original languageEnglish
Title of host publication39th Computers and Information in Engineering Conference
ISBN (Electronic)9780791859179
DOIs
StatePublished - 2019
EventASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2019 - Anaheim, United States
Duration: 18 Aug 201921 Aug 2019

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume1

Conference

ConferenceASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2019
Country/TerritoryUnited States
CityAnaheim
Period18/08/1921/08/19

Keywords

  • Artificial neural network
  • Dimensional accuracy
  • Fused Deposition Modeling
  • Multivariate linear regression model
  • Support vector regression

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