Automated inspection in robotic additive manufacturing using deep learning for layer deformation detection

Omid Davtalab (), Ali Kazemian, Xiao Yuan and Behrokh Khoshnevis
Additional contact information
Omid Davtalab: University of Southern California
Ali Kazemian: Louisiana State University
Xiao Yuan: Contour Crafting Corporation
Behrokh Khoshnevis: Contour Crafting Corporation

Journal of Intelligent Manufacturing, 2022, vol. 33, issue 3, No 9, 784 pages

Abstract: Abstract In this paper, an automated layer defect detection system for construction 3D printing is proposed. Initially, a step-by-step procedure is implemented to develop a deep convolutional neural network that receives images as input and is able to distinguish concrete layers from other surrounding objects through semantic pixel-wise segmentation. Using data augmentation techniques, 1M labeled images are generated and used to train and test the CNN model. Then, a defect detection module is developed which is able to detect deformations in the printed concrete layers extracted from the images using the CNN model. The evaluation results based on metrics such as accuracy, F1 score, and miss rate verify the acceptable performance of the developed system.

Keywords: Deep learning; Semantic segmentation; Automated inspection; Material extrusion (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10845-020-01684-w

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