Carburization level identification in industrial HP pipes using ultrasonic evaluation and machine learning

Rodrigues, Lucas F.M.; Cruz, F. C.; Oliveira, Moisés Alves de; Filho, Eduardo F. Simas; Albuquerque, Maria C.S.; Silva, Ivan C.; Farias, Cláudia Teresa Teles

doi:10.1016/j.ultras.2018.10.005

Cited by 24 publications

(7 citation statements)

References 13 publications

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“…K-nearest neighbors true(KNNtrue) is one of the non-parametric and fast forward algorithms that are used to classify unknown feature vector to a class label (Rodrigues et al , 2019; Musa et al , 2019). The number of neighbors, distance metric and distance weight function are three essential factors which directly influence the accuracy of the k-nearest neighbors classifier.…”

Section: Developed Methodsmentioning

confidence: 99%

On the hybridization of pre-trained deep learning and differential evolution algorithms for semantic crack detection and recognition in ensemble of infrastructures

Abdelkader

2021

SASBE

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PurposeCracks on surface are often identified as one of the early indications of damage and possible future catastrophic structural failure. Thus, detection of cracks is vital for the timely inspection, health diagnosis and maintenance of infrastructures. However, conventional visual inspection-based methods are criticized for being subjective, greatly affected by inspector's expertise, labor-intensive and time-consuming.Design/methodology/approachThis paper proposes a novel self-adaptive-based method for automated and semantic crack detection and recognition in various infrastructures using computer vision technologies. The developed method is envisioned on three main models that are structured to circumvent the shortcomings of visual inspection in detection of cracks in walls, pavement and deck. The first model deploys modified visual geometry group network (VGG19) for extraction of global contextual and local deep learning features in an attempt to alleviate the drawbacks of hand-crafted features. The second model is conceptualized on the integration of K-nearest neighbors (KNN) and differential evolution (DE) algorithm for the automated optimization of its structure. The third model is designated for validating the developed method through an extensive four layers of performance evaluation and statistical comparisons.FindingsIt was observed that the developed method significantly outperformed other crack and detection models. For instance, the developed wall crack detection method accomplished overall accuracy, F-measure, Kappa coefficient, area under the curve, balanced accuracy, Matthew's correlation coefficient and Youden's index of 99.62%, 99.16%, 0.998, 0.998, 99.17%, 0.989 and 0.983, respectively.Originality/valueLiterature review lacks an efficient method which can look at crack detection and recognition of an ensemble of infrastructures. Furthermore, there is absence of systematic and detailed comparisons between crack detection and recognition models.

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Section: Developed Methodsmentioning

confidence: 99%

On the hybridization of pre-trained deep learning and differential evolution algorithms for semantic crack detection and recognition in ensemble of infrastructures

Abdelkader

2021

SASBE

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“…Rodriguez et al [51] used various ML methods such as neural networks, decision tree and k-means neighbourhood analysis to estimate the carburizing level of HT steels produced using pyrolysis furnaces. Experimental pulse-echo ultrasonic tests were performed in HP steel pipes are used.…”

Section: Element Content Estimationmentioning

confidence: 99%

Machine learning for design, phase transformation and mechanical properties of alloys

Durodola

2022

Progress in Materials Science

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“…In the recent years there has been considerable interest in developing machine learning models to evaluate ultrasonic data. Examples are monitoring of mixing processes 11 , assessing carburization of industrial steel tubes 12 , and testing of concrete foundation piles 13 . We are particularly interested in applications regarding robot-guided ultrasonic testing of complex compact parts.…”

Section: Related Workmentioning

confidence: 99%

Training neural networks on domain randomized simulations for ultrasonic inspection

Schlachter¹,

Felsner²,

Zambal³

2022

Open Res Europe

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To overcome the data scarcity problem of machine learning for nondestructive testing, data augmentation is a commonly used strategy. We propose a method to enable training of neural networks exclusively on simulated data. Simulations not only provide a scalable way to generate and access training data, but also make it possible to cover edge cases which rarely appear in the real world. However, simulating data acquired from complex nondestructive testing methods is still a challenging task. Due to necessary simplifications and a limited accuracy of parameter identification, statistical models trained solely on simulated data often generalize poorly to the real world. Some effort has been made in the field to adapt pre-trained classifiers with a small set of real world data. A different approach for bridging the reality gap is domain randomization which was recently very successfully applied in different fields of autonomous robotics. In this study, we apply this approach for ultrasonic testing of carbon-fiber-reinforced plastics. Phased array captures of virtual specimens are simulated by approximating sound propagation via ray tracing. In addition to a variation of the geometric model of the specimen and its defects, we vary simulation parameters. Results indicate that this approach allows a generalization to the real world without applying any domain adaptation. Further, the trained network distinguishes correctly between ghost artifacts and defects. Although this study is tailored towards evaluation of ultrasound phased array captures, the presented approach generalizes to other nondestructive testing methods.

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Carburization level identification in industrial HP pipes using ultrasonic evaluation and machine learning

Cited by 24 publications

References 13 publications

On the hybridization of pre-trained deep learning and differential evolution algorithms for semantic crack detection and recognition in ensemble of infrastructures

On the hybridization of pre-trained deep learning and differential evolution algorithms for semantic crack detection and recognition in ensemble of infrastructures

Machine learning for design, phase transformation and mechanical properties of alloys

Training neural networks on domain randomized simulations for ultrasonic inspection

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