BROWSE

Related Researcher

Author's Photo

Kim, Namhun
UNIST Computer-Integrated Manufacturing Lab (UCIM)
Research Interests
  • Additive Manufacturing (3D Printing), Manufacturing Systems, Agent-based Simulation

ITEM VIEW & DOWNLOAD

Forecasting Warping Deformation Using Multivariate Thermal Time Series and K-Nearest Neighbors in Fused Deposition Modeling

Cited 0 times inthomson ciCited 0 times inthomson ci
Title
Forecasting Warping Deformation Using Multivariate Thermal Time Series and K-Nearest Neighbors in Fused Deposition Modeling
Author
Song, DonghwanChung Baek, Adrian MatiasKoo, JageonBusogi, MoiseKim, Namhun
Issue Date
2020-12
Publisher
MDPI
Citation
Applied Sciences-basel, v.10, no.24, pp.8951
Abstract
Over the past decades, additive manufacturing has rapidly advanced due to its advantages in enabling diverse material usage and complex design production. Nevertheless, the technology has limitations in terms of quality, as printed products are sometimes different from their desired designs or are inconsistent due to defects. Warping deformation, a defect involving layer shrinkage induced by the thermal residual stress generated during manufacturing processes, is a major factor in lowering the quality and raising the cost of printed products. This study utilized a variety of thermal time series data and the K-nearest neighbors (KNN) algorithm with dynamic time warping (DTW) to detect and predict the warping deformation in the printed parts using fused deposition modeling (FDM) printers. Multivariate thermal time series data extracted from thermocouples were trained using DTW-based KNN to classify warping deformation. The results showed that the proposed approach can predict warping deformation with an accuracy of over 80% by only using thermal time series data corresponding to 20% of the whole printing process. Additionally, the classification accuracy exhibited the promising potential of the proposed approach in warping prediction and in actual manufacturing processes, so the additional time and cost resulting from defective processes can be reduced.</jats:p>
URI
https://scholarworks.unist.ac.kr/handle/201301/49270
URL
https://www.mdpi.com/2076-3417/10/24/8951
DOI
10.3390/app10248951
ISSN
2076-3417
Appears in Collections:
MEN_Journal Papers
Files in This Item:
applsci-10-08951.pdf Download

find_unist can give you direct access to the published full text of this article. (UNISTARs only)

Show full item record

qrcode

  • mendeley

    citeulike

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

MENU