Tube Defect Detection Algorithm Under Noisy Environment Using Feature Vector and Neural Networks

Cao, Chi-Tho; Do, Van-Phu; Lee, Byung-Ryong

doi:10.1007/s12541-019-00023-1

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…Industrial artificial intelligence (AI) and machine learning can impact nearly every aspect of a manufacturing business, from design to production to maintenance. Among the entire spectrum of product manufacturing, machine learning models have been applied to real-time process monitoring [ 1 – 5 ]. Real-time validation of process performance is a critical factor in improving a traditional manufacturing unit that can not only meet the manufacturing requirements of products but also improve the smart factory efficiency in a self-organized way.…”

Section: Introductionmentioning

confidence: 99%

Identifying Abnormal CFRP Holes Using Both Unsupervised and Supervised Learning Techniques on In-Process Force, Current, and Vibration Signals

Svinth

Wallace

Stephenson

et al. 2022

Int. J. Precis. Eng. Manuf.

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This study aims to conduct abnormality detection by applying machine learning algorithms when drilling a carbon fiber reinforced plastic laminate. In-process signals including current, thrust force, and vibration were captured during the dry drilling experiments using a 6 mm physical vapor deposit diamond-coated drill at the consistent spindle speed of 6500 RPM and 0.05 mm/rev. Across measurements from out-of-process variables, including hole diameter, roundness, surface roughness, entry/exit delamination, and entry/exit uncut fiber area, in-process measurements were most able to find outliers with respect to diameter. Both Principal Component Analysis, an unsupervised dimensionality reduction technique, and Linear Discriminant Analysis, a supervised dimensionality reduction technique, could separate oversize or undersize holes from average-sized holes when using fast Fourier transformation data of in-process vibration. Predictive performance with k-Nearest Neighbors shows that our machine learning pipeline can predict oversized vs. non-oversized holes with over 85% accuracy in this dataset. Peak prediction performance is obtained when in-process measurement data is viewed from the frequency domain, and predictions are weighted based on the relative distances of the nearest neighbors.

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Section: Introductionmentioning

confidence: 99%

Identifying Abnormal CFRP Holes Using Both Unsupervised and Supervised Learning Techniques on In-Process Force, Current, and Vibration Signals

Svinth

Wallace

Stephenson

et al. 2022

Int. J. Precis. Eng. Manuf.

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show abstract

“…Due to the limited amount of training dataset (124 samples) and noisy environment to classify defects on surface of the tube in steel manufacturing, Chi-Tho et al, have proposed a method using feature vector and Artificial Neural Networks (ANN) [4]. They have compared the performance of kNN, SVM and ANN using the same descriptor, and concluded tha,t on average, ANN outperformed than other methods.…”

Section: Related Workmentioning

confidence: 99%

Melt-Pool Defects Classification for Additive Manufactured Components in Aerospace Use-Case

Dasari

Cheddad

Palmquist

2020

2020 7th International Conference on Soft Computing &Amp; Machine Intelligence (ISCMI)

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One of the crucial aspects of additive manufacturing is the monitoring of the welding process for quality assurance of components. A common way to analyse the welding process is through visual inspection of melt-pool images to identify possible defects in manufacturing. Recent literature studies showed the potential use of prediction models for defects classification to speed up the manual verification criteria since a huge data is generated from the additive manufacturing. Although a huge image data is available, the data needs to be labelled manually by experts which results in small sample datasets. Hence, to model small sample sizes and also to acquire the importance of parameters, we opted a traditional machine learning method, Random Forests (RF). For feature extraction, we opted for the Polar Transformation to explore its applicability using the meltpool image dataset and a publicly available shape image dataset. The results show that RF models with Polar Transformation performed the best on our case study datasets and the second-best for the public dataset when compared to the Histogram of Oriented Gradients, HARALICK, XY-projections of an image, and Local Binary Patterns methods. As such, the Polar Transformation can be considered as a suitable compact shape descriptor.

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A Deep Variational Autoencoder Based Inverse Method for Active Energy Consumption of Mining Plants and Ball Grinding Circuit Investigation

Tsagaan

Batdemberel

Renchin³

et al. 2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

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Tube Defect Detection Algorithm Under Noisy Environment Using Feature Vector and Neural Networks

Cited by 6 publications

References 11 publications

Identifying Abnormal CFRP Holes Using Both Unsupervised and Supervised Learning Techniques on In-Process Force, Current, and Vibration Signals

Identifying Abnormal CFRP Holes Using Both Unsupervised and Supervised Learning Techniques on In-Process Force, Current, and Vibration Signals

Melt-Pool Defects Classification for Additive Manufactured Components in Aerospace Use-Case

A Deep Variational Autoencoder Based Inverse Method for Active Energy Consumption of Mining Plants and Ball Grinding Circuit Investigation

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