Enhancing Surface Fault Detection Using Machine Learning for 3D Printed Products
In the era of Industry 4.0, the idea of 3D printed products has gained momentum and is also proving to be beneficial in terms of financial and time efforts. These products are physically built layer-by-layer based on the digital Computer Aided Design (CAD) inputs. Nonetheless, 3D printed products ar...
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doaj-fa38a9e8c131422888715106369bb8ba2021-06-01T00:04:47ZengMDPI AGApplied System Innovation2571-55772021-05-014343410.3390/asi4020034Enhancing Surface Fault Detection Using Machine Learning for 3D Printed ProductsVaibhav Kadam0Satish Kumar1Arunkumar Bongale2Seema Wazarkar3Pooja Kamat4Shruti Patil5Symbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune 412115, Maharashtra, IndiaSymbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune 412115, Maharashtra, IndiaSymbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune 412115, Maharashtra, IndiaSymbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune 412115, Maharashtra, IndiaSymbiosis Institute of Technology, Symbiosis International (Deemed University), Lavale, Pune 412115, Maharashtra, IndiaSymbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Lavale, Pune 412115, Maharashtra, IndiaIn the era of Industry 4.0, the idea of 3D printed products has gained momentum and is also proving to be beneficial in terms of financial and time efforts. These products are physically built layer-by-layer based on the digital Computer Aided Design (CAD) inputs. Nonetheless, 3D printed products are still subjected to defects due to variation in properties and structure, which leads to deterioration in the quality of printed products. Detection of these errors at each layer level of the product is of prime importance. This paper provides the methodology for layer-wise anomaly detection using an ensemble of machine learning algorithms and pre-trained models. The proposed combination is trained offline and implemented online for fault detection. The current work provides an experimental comparative study of different pre-trained models with machine learning algorithms for monitoring and fault detection in Fused Deposition Modelling (FDM). The results showed that the combination of the Alexnet and SVM algorithm has given the maximum accuracy. The proposed fault detection approach has low experimental and computing costs, which can easily be implemented for real-time fault detection.https://www.mdpi.com/2571-5577/4/2/34additive manufacturingfault detectionfused deposition modellingmachine learningimage analysis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Vaibhav Kadam Satish Kumar Arunkumar Bongale Seema Wazarkar Pooja Kamat Shruti Patil |
spellingShingle |
Vaibhav Kadam Satish Kumar Arunkumar Bongale Seema Wazarkar Pooja Kamat Shruti Patil Enhancing Surface Fault Detection Using Machine Learning for 3D Printed Products Applied System Innovation additive manufacturing fault detection fused deposition modelling machine learning image analysis |
author_facet |
Vaibhav Kadam Satish Kumar Arunkumar Bongale Seema Wazarkar Pooja Kamat Shruti Patil |
author_sort |
Vaibhav Kadam |
title |
Enhancing Surface Fault Detection Using Machine Learning for 3D Printed Products |
title_short |
Enhancing Surface Fault Detection Using Machine Learning for 3D Printed Products |
title_full |
Enhancing Surface Fault Detection Using Machine Learning for 3D Printed Products |
title_fullStr |
Enhancing Surface Fault Detection Using Machine Learning for 3D Printed Products |
title_full_unstemmed |
Enhancing Surface Fault Detection Using Machine Learning for 3D Printed Products |
title_sort |
enhancing surface fault detection using machine learning for 3d printed products |
publisher |
MDPI AG |
series |
Applied System Innovation |
issn |
2571-5577 |
publishDate |
2021-05-01 |
description |
In the era of Industry 4.0, the idea of 3D printed products has gained momentum and is also proving to be beneficial in terms of financial and time efforts. These products are physically built layer-by-layer based on the digital Computer Aided Design (CAD) inputs. Nonetheless, 3D printed products are still subjected to defects due to variation in properties and structure, which leads to deterioration in the quality of printed products. Detection of these errors at each layer level of the product is of prime importance. This paper provides the methodology for layer-wise anomaly detection using an ensemble of machine learning algorithms and pre-trained models. The proposed combination is trained offline and implemented online for fault detection. The current work provides an experimental comparative study of different pre-trained models with machine learning algorithms for monitoring and fault detection in Fused Deposition Modelling (FDM). The results showed that the combination of the Alexnet and SVM algorithm has given the maximum accuracy. The proposed fault detection approach has low experimental and computing costs, which can easily be implemented for real-time fault detection. |
topic |
additive manufacturing fault detection fused deposition modelling machine learning image analysis |
url |
https://www.mdpi.com/2571-5577/4/2/34 |
work_keys_str_mv |
AT vaibhavkadam enhancingsurfacefaultdetectionusingmachinelearningfor3dprintedproducts AT satishkumar enhancingsurfacefaultdetectionusingmachinelearningfor3dprintedproducts AT arunkumarbongale enhancingsurfacefaultdetectionusingmachinelearningfor3dprintedproducts AT seemawazarkar enhancingsurfacefaultdetectionusingmachinelearningfor3dprintedproducts AT poojakamat enhancingsurfacefaultdetectionusingmachinelearningfor3dprintedproducts AT shrutipatil enhancingsurfacefaultdetectionusingmachinelearningfor3dprintedproducts |
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