TY - GEN
T1 - Layered image collection for real-time defective inspection in additive manufacturing
AU - Song, Jinwoo
AU - Bandaru, Harika
AU - He, Xinyu
AU - Qiu, Zhenyang
AU - Moon, Young B.
N1 - Publisher Copyright:
© 2020 The Author(s). This is an Open Access article under the CC BY license.
PY - 2020
Y1 - 2020
N2 - In Additive Manufacturing (AM), detecting cyber-attacks on infill structure is difficult because interior defects can occur without affecting the exterior. To detect the infill defectives quickly, layer-by-layer image inspection in real-time can be conducted. However, collecting the layered images from the top view in real-time is challenging because the 3D printer's extruder interferes with objects from being perfectly scanned. Using a dummy model to move the extruder out of the object's layer has been proposed. However, it is not practical because it creates printing delays and wasted printing materials. To enable infill layered image collection in real-time without delays and material waste, this research proposes a layered image collection method using an algorithm identifying a pseudo area in a layered image. The algorithm detects the pseudo area-the area covered by the extruder-using an image processing technique, such as an average pooling and max pooling. It accumulates the non-pseudo areas until a complete layered image is acquired. To validate and evaluate the proposed method, captured images were evaluated with various machine learning algorithms.
AB - In Additive Manufacturing (AM), detecting cyber-attacks on infill structure is difficult because interior defects can occur without affecting the exterior. To detect the infill defectives quickly, layer-by-layer image inspection in real-time can be conducted. However, collecting the layered images from the top view in real-time is challenging because the 3D printer's extruder interferes with objects from being perfectly scanned. Using a dummy model to move the extruder out of the object's layer has been proposed. However, it is not practical because it creates printing delays and wasted printing materials. To enable infill layered image collection in real-time without delays and material waste, this research proposes a layered image collection method using an algorithm identifying a pseudo area in a layered image. The algorithm detects the pseudo area-the area covered by the extruder-using an image processing technique, such as an average pooling and max pooling. It accumulates the non-pseudo areas until a complete layered image is acquired. To validate and evaluate the proposed method, captured images were evaluated with various machine learning algorithms.
KW - 3D-printer
KW - Additive manufacturing
KW - Cyber-Attack
KW - Cyber-physical attack
KW - Cyber-physical system
KW - Image processing
KW - Infill
KW - Layered image
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85101267926&partnerID=8YFLogxK
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U2 - 10.1115/IMECE2020-23250
DO - 10.1115/IMECE2020-23250
M3 - Conference contribution
AN - SCOPUS:85101267926
T3 - ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
BT - Advanced Manufacturing
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2020 International Mechanical Engineering Congress and Exposition, IMECE 2020
Y2 - 16 November 2020 through 19 November 2020
ER -