TY - GEN
T1 - Automated Quality Control of 3D Printed Tensile Specimen via Computer Vision
AU - Ullah, Rizwan
AU - Gebrehiwot, Silas
AU - Patabendige, Thumula Madduma
AU - Espinosa-Leal, Leonardo
PY - 2024
Y1 - 2024
N2 - This research explores the integration of a robotic arm using computer vision for automated quality control for sorting 3D printed tensile specimens. The study, conducted, focuses on utilizing a Niryo NED-2 robotic arm with a vision system. The robotic arm captures cross-sections of tensile specimen, and a Python program processes vision feeds, filtering images based on 2D contours. Tensile samples were manufactured using Fused Deposition Modeling (FDM) with PLA material, incorporating known offsets (both positive and negative). Their dimensions were predicted and compared with the actual geometrical measurements. Experimental results showcase the system's accuracy in measuring specimen dimensions, demonstrating low error rates. The study highlights the potential for automated quality control in additive manufacturing, presenting a valuable tool for Industry 4.0. The robotic arm's vision system proves effective in enhancing efficiency and reliability in 3D printing quality inspection processes.
AB - This research explores the integration of a robotic arm using computer vision for automated quality control for sorting 3D printed tensile specimens. The study, conducted, focuses on utilizing a Niryo NED-2 robotic arm with a vision system. The robotic arm captures cross-sections of tensile specimen, and a Python program processes vision feeds, filtering images based on 2D contours. Tensile samples were manufactured using Fused Deposition Modeling (FDM) with PLA material, incorporating known offsets (both positive and negative). Their dimensions were predicted and compared with the actual geometrical measurements. Experimental results showcase the system's accuracy in measuring specimen dimensions, demonstrating low error rates. The study highlights the potential for automated quality control in additive manufacturing, presenting a valuable tool for Industry 4.0. The robotic arm's vision system proves effective in enhancing efficiency and reliability in 3D printing quality inspection processes.
KW - Additive manufacturing
KW - Industry 4.0
KW - Machine vision
KW - Robots
UR - http://www.scopus.com/inward/record.url?scp=85196178537&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-61891-8_24
DO - 10.1007/978-3-031-61891-8_24
M3 - Conference article in proceedings
SN - 978-3-031-61904-5
VL - 2
T3 - Lecture Notes in Networks and Systems
SP - 245
EP - 252
BT - Smart Technologies for a Sustainable Future
A2 - Auer, Michael E.
A2 - Langmann, Reinhard
A2 - May, Dominik
A2 - Roos, Kim
PB - Springer
CY - Cham
T2 - 21st International Conference on Smart Technologies & Education (STE-2024)
Y2 - 6 March 2024 through 8 March 2024
ER -
