Toward Enabling a Reliable Quality Monitoring System for Additive
Manufacturing Process using Deep Convolutional Neural Networks
- URL: http://arxiv.org/abs/2003.08749v1
- Date: Fri, 6 Mar 2020 20:49:20 GMT
- Title: Toward Enabling a Reliable Quality Monitoring System for Additive
Manufacturing Process using Deep Convolutional Neural Networks
- Authors: Yaser Banadaki, Nariman Razaviarab, Hadi Fekrmandi, and Safura Sharifi
- Abstract summary: We propose an automated quality grading system for the Additive Manufacturing (AM) process using a deep convolutional neural network (CNN) model.
The CNN model is trained offline using the images of the internal and surface defects in the layer-by-layer deposition of materials and tested online by studying the performance of detecting and classifying the failure in AM process at different extruder speeds and temperatures.
The proposed online model adds an automated, consistent, and non-contact quality control signal to the AM process that eliminates the manual inspection of parts after they are entirely built.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Additive Manufacturing (AM) is a crucial component of the smart industry. In
this paper, we propose an automated quality grading system for the AM process
using a deep convolutional neural network (CNN) model. The CNN model is trained
offline using the images of the internal and surface defects in the
layer-by-layer deposition of materials and tested online by studying the
performance of detecting and classifying the failure in AM process at different
extruder speeds and temperatures. The model demonstrates the accuracy of 94%
and specificity of 96%, as well as above 75% in three classifier measures of
the Fscore, the sensitivity, and precision for classifying the quality of the
printing process in five grades in real-time. The proposed online model adds an
automated, consistent, and non-contact quality control signal to the AM process
that eliminates the manual inspection of parts after they are entirely built.
The quality monitoring signal can also be used by the machine to suggest
remedial actions by adjusting the parameters in real-time. The proposed quality
predictive model serves as a proof-of-concept for any type of AM machines to
produce reliable parts with fewer quality hiccups while limiting the waste of
both time and materials.
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