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We present the results of several machine learning (ML)- inspired data fusion algorithms applied to multi-sensory nondestructive testing (NDT) data. Our dataset consists of Impact-Echo (IE), Ultrasonic Pulse Echo (US) and Ground Penetrating Radar (GPR) data collected on large-scale concrete specimens with built–in simulated honeycombing defects. The main objective is to improve the detectability of honeycombs by fusing the information from the three different sensors. We describe normalization, feature detection and optimal feature selection. We have used unsupervised and supervised ML, i.e., classification and clustering, for data fusion. We demonstrate the advantage of data fusion in reducing the false positives up to 10% compared to the best single sensor, thus, improving the detectability of the defects. The methods were evaluated on a concrete specimen. The effectiveness of the proposed approach was demonstrated on a separate full-scale concrete specimen. The results indicate the transportability of the conclusions from one specimen to the other.
Time-of-flight secondary ion mass spectrometry (ToF-SIMS) and high-resolution scanning electron microscopy are well-acknowledged tools in materials characterization. The ability to map chemical species on the surface of an investigated sample with often low mass detection limits makes ToF-SIMS an essential tool in fields where many question marks concerning Degradation processes and damage mechanisms exist. The aim of this paper is to describe the power of data fusion of ToF-SIMS and high-resolution scanning electron microscopy results employing computational methods for multivariate data Analysis such as principal component analysis. As a case study the investigation of hydrogen distribution in an artificially charged Duplex stainless steel microstructure is presented aiming on a better understanding of hydrogen embrittlement.
We present the results of a machine learning (ML)- inspired data fusion approach, applied to multi-sensory nondestructive testing (NDT) data. Our dataset consists of Impact-Echo (IE), Ultrasonic Pulse Echo (US) and Ground Penetrating Radar (GPR) measurements collected on large-scale concrete specimens with built–in simulated honeycombing defects. In a previous study we were able to improve the detectability of honeycombs by fusing the information from the three different sensors with the density based clustering algorithm DBSCAN. We demonstrated the advantage of data fusion in reducing the false positives up to 10% compared to the best single sensor, thus, improving the detectability of the defects. The main objective of this contribution is to investigate the generality, i.e. whether the conclusions from one specimen can be adapted to the other. The effectiveness of the proposed approach on a separate full-scale concrete specimen was evaluated.
We present the results of a machine learning (ML)- inspired data fusion approach, applied to multi-sensory nondestructive testing (NDT) data. Our dataset consists of Impact-Echo (IE), Ultrasonic Pulse Echo (US) and Ground Penetrating Radar (GPR) measurements collected on large-scale concrete specimens with built–in simulated honeycombing defects. In a previous study we were able to improve the detectability of honeycombs by fusing the information from the three different sensors with the density based clustering algorithm DBSCAN. We demonstrated the advantage of data fusion in reducing the false positives up to 10% compared to the best single sensor, thus, improving the detectability of the defects. The main objective of this contribution is to investigate the generality, i.e. whether the conclusions from one specimen can be adapted to the other. The effectiveness of the proposed approach on a separate full-scale concrete specimen was evaluated.
Deuterium permeation and cracking in duplex steels as viewed by ToF-SIMS and HR-SEM with data fusion
(2016)
Better understanding of hydrogen assisted degradation and trapping mecha-nisms requires sufficient imaging techniques for respective hydrogen-microstructure interaction studies, in particular with multi-phase metallic micro-structures [1]. The present work is focusing on the elucidation of deuterium be-havior in two austenitic-ferritic duplex stainless steels (DSS) under the assumption that deuterium behaves in many ways similarly to hydrogen [2]. For case studies standard 2205 and lean 2101 DSSs were chosen due to the extensive use of these steels in industry [3]. The analyses were conducted by using a novel in-situ permeation and Time-of-Flight secondary ion mass spectrometry (ToF-SIMS) imaging technique or by ex-situ ToF-SIMS imaging following electrochemical charging experiments. Another pioneering procedure was data fusion (including chemometry) of results of powerful laterally resolved chemical analysis and high resolution structural characterization techniques .
Results for the ex-situ observations showed a different influence of deuterium loading on the two steel grades as well as different damage mechanisms in each phase. Formation of sub-surface blisters between the ferrite and austenite were obtained in both the standard and the lean DSS. In both steels, an increased deuterium concentration was observed around deformed regions such as cracks, confirming that they originate from the presence of deuterium [4]. The formation of parallel cracks was obtained only in the austenite within the standard duplex whereas in the lean duplex the highest intensity of deuterium was obtained in the austenite along the ferrite-austenite interphase.
In comparison, application of the novel in-situ permeation technique enabled to register and record the deuterium permeation through the material and the respective saturation sequence of the two phases as well as the interfaces. Faster diffusion of the deuterium was observed in the ferrite and a direct proof for deuterium enrichment at the austenite-ferrite interface has been given [1]. The integration of the specified techniques gives a better insight into the processes leading to hydrogen induced failure. These two experimental techniques provide very valuable tools for elucidation of respective metallurgical failure mechanisms that can be used for the validation of respective numerical models for hydrogen assisted cracking (HAC).
This talk demonstrates the results of the IGSTC-project entitled "NDT-Data Fusion".
Project approach:
Nondestructive testing (NDT) of concrete buildings allows to coordinate efficient repair measures. Multi-sensor platforms collect large data sets. Nevertheless, data analysis is typically performed manually. Data Fusion uses the full potential of a multi-sensory data set in order to:
improve information quality (reliability, robustness, accuracy, clarity, completeness) and enables automated algorithm based data analysis.
We present the project achievements, namely:
- Development of building scanner system for multisensory NDT
- Laboratory multi sensor investigations
- Development of data fusion concept for honeycombing and pitting corrosion
- Field testing
Half-cell potential mapping (HP) is the most popular nondestructive test (NDT)-method for the localization of corrosion damage in concrete. It is generally recognized, that HP is prone to the environmental factors that arise from salt induced deterioration, such as varying moisture and chloride gradients. Additional NDT-methods are capable to determine distinctive areas, but cannot yet be used to estimate more accurate testing results. We introduce a supervised machine learning (SML) based approach for data fusion to make use of the additional sensor information. SML are methods that explore relations between different (sensor) data from predefined data labels. We use a simple linear classifier named logistic regression to distinguish defect and intact areas. The test performance improves drastically compared to the best single method, HP. In order to generate representative, labeled data we conducted a comprehensive experiment that simulates the deterioration-cycle of a chloride-exposed building part in the lab. Our data set consist of 18 measurement campaigns, each containing HP-, ground-penetrating-radar-, microwave-moisture-, and Wenner-resistivity-data. We detail the challenges that arise with a data driven approach in NDT and how we addressed them.
Research and Development in Non-Destructive Testing in Civil Engineering at BAM has de-veloped over more than two decades. Highlights of present research are ultrasound phase array probes, ground penetrating radar applications for the assessment of existing structures and Laser Induced Breakdown Spec-troscopy (LIBS) to determine which chemical elements on the surface of a specimen are. RFID sensor devel-opments for wireless moisture monitoring and data fusion for the combined analysis of sensor data are fast developing areas. In addition, a selection of application cases is briefly described.
The use of duplex stainless steels (DSS) in energy related applications is well known. Nowadays, DSS steels become more favorable than austenitic steels due to the outstanding mechanical properties, the good corrosion resistance and the lower nickel content. However, the use of the duplex grade in acidic environments such as seawater often leads to severe degradation of the structural integrity of the steel by hydrogen-induced/assisted cracking (HAC) phenomena, which can eventually result in premature failure. Hydrogen assisted degradation and cracking of steels are active fields of research even though this topic is intensively studied for more than a century. A bottleneck is the analytical validation of the theoretical models proposed ion the literature at the sub-micron scale.
Industrial and the research communities see a need for an accurate analytical method by which it is possible to image the distribution of hydrogen in the microstructure of a steels or and other alloys. Among the very few available methods hydrogen imaging methods, Time-of-Flight secondary ion mass spectrometry (ToF-SIMS) has the principal capability for mapping of hydrogen in a steel’s microstructure. The combination of ToF-SIMS with multivariate data analysis (MVA), electron microscopy (SEM) and electron-backscattered diffraction (EBSD) is a powerful approach for providing chemical and structural information. The use of data fusion techniques has been shown recently to enhance the better understanding of the hydrogen induced degradation processes in in a DSS steel.