Sizing Subsurface Defects in Metallic Plates by Square Pulse Thermography Using an Oriented Gradient Map

Xie, Jing; Xu, Changhang; Gong, Xumei; Huang, Weiping; Chen, Guo-Ming

doi:10.3390/app6120389

Cited by 5 publications

(4 citation statements)

References 33 publications

(19 reference statements)

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“…A research team led by Xie et al [ 23 ] came up with a new approach to establishing the size of subsurface defects of metallic sheets by square pulse thermography. The tool of their choice was the oriented gradient of histograms.…”

Section: Introductionmentioning

confidence: 99%

Application of Innovative Methods of Predictive Control in Projects Involving Intelligent Steel Processing Production Systems

et al. 2021

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This paper describes the enhancement of the existing predictive system of quality management in the processes of metallurgic manufacturing. Specifically, it addresses steel-strip manufacturing. The main quality management innovation is the transition from the current methodological process of a single-step defect evaluation to a two-step evaluation. A two-step defect check of the strip’s surface involves checking for defects during the hot-rolling process first, and double-checking it during the process of pickling. These defects are detected in a well-established process of camera imaging in the production process. The recorded image is then processed mathematically to find the degree of defect correlation in those processes. The two-step evaluation enables a more detailed focus on a particular defect and its position on the strip. Decisions concerning further processing are based on defect evaluation, for instance, whether a rework is necessary to maximize the product utilization and minimize the eventual negative impact of the defect on production equipment. A crucial aspect is also the reduced probability of failures in the manufacturing process.

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Section: Introductionmentioning

confidence: 99%

Application of Innovative Methods of Predictive Control in Projects Involving Intelligent Steel Processing Production Systems

et al. 2021

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“…The location and size of the flaws were used to classify and label the data. However, flaws appear blurry and therefore larger than their true size in TNDT images [30]. Therefore, the flaw sizes were increased by 20.0 % during the labelling process.…”

Section: B Data Preparationmentioning

confidence: 99%

Transient Thermography for Flaw Detection in Friction Stir Welding: A Machine Learning Approach

Atwya

Panoutsos

2020

IEEE Trans. Ind. Inf.

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A systematic computational method to simulate and detect sub-surface flaws, through non-destructive transient thermography, in aluminium sheets and friction stir welded sheets is proposed. The proposed method relies on feature extraction methods and a data-driven machine learning modelling structure. In this work, we propose the use of a multi-layer perceptron feed-forward neural-network with feature extraction methods to improve the flaw-probing depth of transient thermography inspection. Furthermore, for the first time, we propose Thermographic Signal Linear Modelling (TSLM), a hyper-parameterfree feature extraction technique for transient thermography. The new feature extraction and modelling framework was tested with out-of-sample experimental transient thermography data and results show effectiveness in sub-surface flaw detection of up to 2.3 mm deep in aluminium sheets (99.8 % true positive rate, 92.1 % true negative rate) and up to 2.2 mm deep in friction stir welds (97.2 % true positive rate, 87.8 % true negative rate).

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“…In recent decades, different types of instruments and techniques have been utilized for structural damage detection and health monitoring. These techniques can be classified into acoustic emission [6][7][8][9][10][11][12][13], electromagnetic techniques [14,15], ultrasonic inspection [16][17][18], microscopy [19], sensors [20][21][22][23][24][25][26][27][28][29][30] and thermography [31][32][33][34][35][36][37]. For example, Santosa monitored the impact damage on fiberglass composite plates based on surface lava wave propagation [38].…”

Section: Introductionmentioning

confidence: 99%

Detection of Impact Damage on PVA-ECC Beam Using Infrared Thermography

et al. 2018

Applied Sciences

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The main purpose of the current research is to pilot study the impact damage detection in a beam structure using infrared thermography. In this study, a beam structure, made of polyvinyl alcohol fiber reinforced engineering cementitious composite (PVA-ECC) was subjected to multiple low-velocity impacts at a constant energy level. After each impact, the structure was heated by means of halogen lamp, and acquisition of thermal images was conducted simultaneously. Sequences of thermal images were acquired with starting and ending time sets so as to include the entire evolution of thermal phenomenon, during both heating to cooling processes. Based on the relationship between the damage and the temperature variation under the thermal excitation, different damages in the impacted structures were analyzed in the thermographs. Through experimental investigation, the results demonstrated that different degrees of damage correspond to different infrared thermal characteristics. The generation and evolution of thermal signatures revealed the initiation and propagation of impact damages. It further illustrated that the proposed method is an innovative and effective approach to detect impact damage.

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Sizing Subsurface Defects in Metallic Plates by Square Pulse Thermography Using an Oriented Gradient Map

Cited by 5 publications

References 33 publications

Application of Innovative Methods of Predictive Control in Projects Involving Intelligent Steel Processing Production Systems

Application of Innovative Methods of Predictive Control in Projects Involving Intelligent Steel Processing Production Systems

Transient Thermography for Flaw Detection in Friction Stir Welding: A Machine Learning Approach

Detection of Impact Damage on PVA-ECC Beam Using Infrared Thermography

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