Defect Detection in Weld Joints by Infrared Thermography

The objective of this present paper is to evaluate the effectiveness of infrared thermography (IRT) as a non-contact, fast and reliable non-destructive evaluation procedure for detection and quantification of defects in weld joints. In the present work, a friction stir welded (FSW) joint of two aluminum plates and three 316 LN stain-less steel (SS) weld-joints with lack of penetration (LOP), lack of fusion (LOF) and tungsten inclusion (TI) defects respectively, were inspected using IRT and digital radio-graphy (DRG). Using active thermography methods, a sub-surface tunnel defect along the weld line was successfully detected in the FSW joint and its length and width were estimated by suitable pixel calibration. Using lock-in thermography, optimum frequencies were determined for each of the specimens and defect-depths were estimated. Tempe-rature fall of the defect region and defect-free region were monitored as a function of time and it was found that the rate of temperature fall in the former case is slower than that in the latter one. Results from both the tech-niques, i.e., IRT and DRG were found to be in good agree-ment with each other in all the cases. Advantage of IRT is that it provides depth information also

Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

The implementation of computerised condition monitoring systems for the detection cutting tools’ correct installation and fault diagnosis is of a high importance in modern manufacturing industries. The primary function of a condition monitoring system is to check the existence of the tool before starting any machining process and ensure its health during operation. The aim of this study is to assess the detection of the existence of the tool in the spindle and its health (i.e. normal or broken) using infrared and vision systems as a non-contact methodology. The application of Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) combined with neural networks are investigated using both types of data in order to establish an effective and reliable novel software program for tool tracking and health recognition. Infrared and visual cameras are used to locate and track the cutting tool during the machining process using a suitable analysis and image processing algorithms. The capabilities of PCA and Discrete Wavelet Transform (DWT) combined with neural networks are investigated in recognising the tool’s condition by comparing the characteristics of the tool to those of known conditions in the training set. The experimental results have shown high performance when using the infrared data in comparison to visual images for the selected image and signal processing algorithms

Development of high-resolution infrared thermographic imaging method as a diagnostic tool for acute undifferentiated limp in young children

Acute limp is a common presenting condition in the paediatric emergency department. There are a number of causes of acute limp that include traumatic injury, infection and malignancy. These causes in young children are not easily distinguished. In this pilot study, an infrared thermographic imaging technique to diagnose acute undifferentiated limp in young children was developed. Following required ethics approval, 30 children (mean age = 5.2 years, standard deviation = 3.3 years) were recruited. The exposed lower limbs of participants were imaged using a high-resolution thermal camera. Using predefined regions of interest (ROI), any skin surface temperature difference between the healthy and affected legs was statistically analysed, with the aim of identifying limp. In all examined ROIs, the median skin surface temperature for the affected limb was higher than that of the healthy limb. The small sample size recruited for each group, however, meant that the statistical tests of significant difference need to be interpreted in this context. Thermal imaging showed potential in helping with the diagnosis of acute limp in children. Repeating a similar study with a larger sample size will be beneficial to establish reproducibility of the results

A novel infrared video surveillance system using deep learning based techniques

This is the author accepted manuscript. The final version is available from Springer via the DOI in this record.This paper presents a new, practical infrared video based surveillance system, consisting of a resolution-enhanced, automatic target detection/recognition (ATD/R) system that is widely applicable in civilian and military applications. To deal with the issue of small numbers of pixel on target in the developed ATD/R system, as are encountered in long range imagery, a super-resolution method is employed to increase target signature resolution and optimise the baseline quality of inputs for object recognition. To tackle the challenge of detecting extremely low-resolution targets, we train a sophisticated and powerful convolutional neural network (CNN) based faster-RCNN using long wave infrared imagery datasets that were prepared and marked in-house. The system was tested under different weather conditions, using two datasets featuring target types comprising pedestrians and 6 different types of ground vehicles. The developed ATD/R system can detect extremely low-resolution targets with superior performance by effectively addressing the low small number of pixels on target, encountered in long range applications. A comparison with traditional methods confirms this superiority both qualitatively and quantitativelyThis work was funded by Thales UK, the Centre of Excellence for Sensor and Imaging System (CENSIS), and the Scottish Funding Council under the project “AALART. Thales-Challenge Low-pixel Automatic Target Detection and Recognition (ATD/ATR)”, ref. CAF-0036. Thanks are also given to the Digital Health and Care Institute (DHI, project Smartcough-MacMasters), which partially supported Mr. Monge-Alvarez’s contribution, and to the Royal Society of Edinburgh and National Science Foundation of China for the funding associated to the project “Flood Detection and Monitoring using Hyperspectral Remote Sensing from Unmanned Aerial Vehicles”, which partially covered Dr. Casaseca-de-la-Higuera’s, Dr. Luo’s, and Prof. Wang’s contribution. Dr. Casaseca-de-la-Higuera would also like to acknowledge the Royal Society of Edinburgh for the funding associated to project “HIVE”

Protocol : a method to study the direct reprogramming of lateral root primordia to fertile shoots

Background: Plants have the remarkable property to elaborate entire body plan from any tissue part. The conversion of lateral root primordium (LRP) to shoot is an ideal method for plant propagation and for plant researchers to understand the mechanism underlying trans-differentiation. Until now, however, a robust method that allows the efficient conversion of LRP to shoot is lacking. This has limited our ability to study the dynamic phases of reprogramming at cellular and molecular levels. Results: Here we present an efficient protocol for the direct conversion of LRP to a complete fertile shoot system. This protocol can be readily applied to the various ecotypes of Arabidopsis. We show that, the conversion process is highly responsive to developmental stages of LRP and changes in external environmental stimuli such as temperature. The entire conversion process can be adequately analyzed by histological and imaging techniques. As a demonstration, using a battery of cell fate specific markers, we show that confocal time-lapse imaging can be employed to uncover the early molecular events, intermediate developmental phases and relative abundance of stem cell regulators during the conversion of LRP to shoot. Conclusion: Our method is highly efficient, independent of genotypes tested and suitable to study the reprogramming of LRP to shoot in intact plants as well as in excised roots.Peer reviewe

Adaptive Polymeric Coatings with Self-Reporting and Self-Healing Dual Functions from Porous Core-Shell Nanostructures

In biological system, early detection and treatment at the same moment is highly required. For synthetic materials, it is demanding to develop materials that possess self-reporting of early damage and self-healing simultaneously. This dual function is achieved in this work by introducing an intelligent pH-responsive coatings based on poly(divinylbenzene)-graft-poly(divinylbenzene-co-methacrylic acid) (PDVB-graft-P(DVB-co-AA)) core-shell microspheres as smart components of the polymer coatings for corrosion protection. The key component, synthesized PDVB-graft-P(DVB-co-AA) core-shell microspheres are porous and pH responsive. The porosity allows for encapsulation of the corrosion inhibitor of benzotriazole and the fluorescent probe, coumarin. Both loading capacities can be up to about 15 wt%. The polymeric coatings doped with the synthesized microspheres can adapt immediately to the varied variation in pH value from the electrochemical corrosion reaction and release active molecules on demand onto the damaged cracks of the coatings on metal surfaces. It leads simultaneously to the dual functions of self-healing and self-reporting. The corrosion area can be self-reported in 6 h, while the substrate can be protected at least for 1 month in 3.5 wt% NaCl solution. These pH-responsive materials with self-reporting and self-healing dual functions are highly expected to have a bright future due to their smart, long-lasting, recyclable, and multifunctional properties.</p

Application of infrared thermography in computer aided diagnosis

The invention of thermography, in the 1950s, posed a formidable problem to the research community: What is the relationship between disease and heat radiation captured with Infrared (IR) cameras? The research community responded with a continuous effort to find this crucial relationship. This effort was aided by advances in processing techniques, improved sensitivity and spatial resolution of thermal sensors. However, despite this progress fundamental issues with this imaging modality still remain. The main problem is that the link between disease and heat radiation is complex and in many cases even non-linear. Furthermore, the change in heat radiation as well as the change in radiation pattern, which indicate disease, is minute. On a technical level, this poses high requirements on image capturing and processing. On a more abstract level, these problems lead to inter-observer variability and on an even more abstract level they lead to a lack of trust in this imaging modality. In this review, we adopt the position that these problems can only be solved through a strict application of scientific principles and objective performance assessment. Computing machinery is inherently objective; this helps us to apply scientific principles in a transparent way and to assess the performance results. As a consequence, we aim to promote thermography based Computer-Aided Diagnosis (CAD) systems. Another benefit of CAD systems comes from the fact that the diagnostic accuracy is linked to the capability of the computing machinery and, in general, computers become ever more potent. We predict that a pervasive application of computers and networking technology in medicine will help us to overcome the shortcomings of any single imaging modality and this will pave the way for integrated health care systems which maximize the quality of patient care