TY - JOUR
T1 - Analytical modelling of in situ layer-wise defect detection in 3D-printed parts
T2 - additive manufacturing
AU - Bowoto, Oluwole K.
AU - Oladapo, Bankole I.
AU - Zahedi, S. A.
AU - Omigbodun, Francis T.
AU - Emenuvwe, Omonigho P.
N1 - Funding Information:
We appreciate the funding/financial support received from the Higher Education Innovation Fund (HEIF) of De Montfort University, Leicester, United Kingdom, under Research Project No.0043.06.
Publisher Copyright:
© 2020, Springer-Verlag London Ltd., part of Springer Nature.
PY - 2020/12
Y1 - 2020/12
N2 - This study analyzes a software algorithm developed on MATLAB, which can be used to examine fused filament fabrication–based 3D-printed materials for porosity and other defects that might affect the mechanical property of the final component under manufacture or the general aesthetic quality of a product. An in-depth literature review into the 3D-printed materials reveals a rapidly increasing trend in its application in the industrial sector. Hence, the quality of manufactured products cannot be compromised. Despite much research found to be done on this subject, there is still little or no work reported on porosity or defect detection in 3D-printed components during (real-time) or after manufacturing operation. The algorithm developed in this study is tested for two different 3D object geometries and the same filament color. The results showed that the algorithm effectively detected the presence or absence of defects in a 3D-printed part geometry and filament colors. Hence, this technique can be generalized to a considerable range of 3D printer geometries, which solve material wastages by spotting defects during the workpieces layer-wise manufacturing process, thereby improving the economic advantages of additive manufacturing.
AB - This study analyzes a software algorithm developed on MATLAB, which can be used to examine fused filament fabrication–based 3D-printed materials for porosity and other defects that might affect the mechanical property of the final component under manufacture or the general aesthetic quality of a product. An in-depth literature review into the 3D-printed materials reveals a rapidly increasing trend in its application in the industrial sector. Hence, the quality of manufactured products cannot be compromised. Despite much research found to be done on this subject, there is still little or no work reported on porosity or defect detection in 3D-printed components during (real-time) or after manufacturing operation. The algorithm developed in this study is tested for two different 3D object geometries and the same filament color. The results showed that the algorithm effectively detected the presence or absence of defects in a 3D-printed part geometry and filament colors. Hence, this technique can be generalized to a considerable range of 3D printer geometries, which solve material wastages by spotting defects during the workpieces layer-wise manufacturing process, thereby improving the economic advantages of additive manufacturing.
KW - 3D printing
KW - Defect detection
KW - Image processing
KW - MATLAB
KW - Real-time monitoring
UR - http://www.scopus.com/inward/record.url?scp=85094139892&partnerID=8YFLogxK
U2 - 10.1007/s00170-020-06241-6
DO - 10.1007/s00170-020-06241-6
M3 - Article
AN - SCOPUS:85094139892
SN - 0268-3768
VL - 111
SP - 2311
EP - 2321
JO - The International Journal of Advanced Manufacturing Technology
JF - The International Journal of Advanced Manufacturing Technology
ER -