Automated Fiber Placement Defects: Automated Inspection and Characterization

Automated Fiber Placement (AFP) is an additive composite manufacturing technique, and a pressing challenge facing this technology is defect detection and repair. Manual defect inspection is time consuming, which led to the motivation to develop a rapid automatic method of inspection. This paper suggests a new automated inspection system based on convolutional neural networks and image segmentation tasks. This creates a pixel by pixel classification of the defects of the whole part scan. This process will allow for greater defect information extraction and faster processing times over previous systems, motivating rapid part inspection and analysis. Fine shape, height, and boundary detail can be generated through our system as opposed to a more coarse resolution demonstrated in other techniques. These scans are analyzed for defects, and then each defect is stored for export, or correlated to machine parameters or part design. The network is further improved through novel optimization techniques. New training instances can also be created with every new part scan by including the machine operator as a post inspection check on the accuracy of the system. Having a continuously adapting inspection system will increase accuracy for automated inspections, cutting down on false readings.

[1]  Klaus-Dieter Thoben,et al.  Changing States of Multistage Process Chains , 2016 .

[2]  Mohammad Reza Khosravani,et al.  Fracture mechanics and mechanical fault detection by artificial intelligence methods: A review , 2017 .

[3]  ImageNet Classification with Deep Convolutional Neural , 2013 .

[4]  S. Gholizadeh,et al.  A review of non-destructive testing methods of composite materials , 2022 .

[5]  Scott Blake.  Integrated Automatic Inspection in Robotic Composites Cells , 2017 .

[6]  Thorsten Wuest.  Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning , 2015 .

[7]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[9]  B. Denkena,et al.  Machine Learning Approach for Optimization of Automated Fiber Placement Processes , 2017 .

[10]  Todd Rudberg,et al.  AFP Automated Inspection System Performance and Expectations , 2017 .

[11]  Todd Rudberg,et al.  Automated In-Process Inspection System for AFP Machines , 2015 .

[12]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[14]  Muhammed Fatih Talu,et al.  Fabric defect detection systems and methods—A systematic literature review , 2016 .

[15]  Bernd Scholz-Reiter,et al.  Learning Defect Classifiers for Textured Surfaces Using Neural Networks and Statistical Feature Representations , 2013 .