TY - GEN
T1 - Dimensional prediction for FDM machines using artificial neural network and support vector regression
AU - Lyu, Jiaqi
AU - Manoochehri, Souran
N1 - Publisher Copyright:
Copyright © 2019 ASME.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Artificial neural network
KW - Dimensional accuracy
KW - Fused Deposition Modeling
KW - Multivariate linear regression model
KW - Support vector regression
UR - http://www.scopus.com/inward/record.url?scp=85076433013&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076433013&partnerID=8YFLogxK
U2 - 10.1115/DETC2019-97963
DO - 10.1115/DETC2019-97963
M3 - Conference contribution
AN - SCOPUS:85076433013
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 39th Computers and Information in Engineering Conference
T2 - ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2019
Y2 - 18 August 2019 through 21 August 2019
ER -