First special issue of case study in nondestructive testing and evaluation

The rotor blades are key components in wind turbine generators. A visual inspection of the laminated shells for delaminations, air pockets, missing/disoriented fabric etc. is in most cases also not possible due to the manufacturing process, so Non-destructive testing and evaluation (NDT & E) techniques for assessing the integrity of rotor blades structure are essential to both reduce manufacturing costs and out of service time of wind turbine generators due to maintenance. Nowadays, Infrared Thermal Wave Nondestructive Testing (Pulsed thermography) is commonly used for assessing composites. This research work utilizes Infrared Thermal Wave Nondestructive Testing system (EchoTherm, Thermal Wave Imaging, Inc.) to inspect a specimen with embedded defects (i.e. foreign matter and air inclusions) in different depth which is a part of rotor blades in wind turbine generators, we have successfully identified defects including foreign matter and air inclusions, and discovered a defective workmanship. The system software allows us to simultaneously view and analyze the results for an entire transition.

A new MFL imaging and quantitative nondestructive evaluation method in wire rope defect detection

9 - Non-destructive evaluation (NDE) of aerospace composites: methods for testing adhesively bonded composites

Nondestructive dynamic testing of apples for firmness evaluation

This paper presents Analysis and assessment of welding images and different techniques involved in detection of flaws in weldments, The efficient and reliable detection of defects is one of the important Task in Nondestructive testing (NDT) And Nondestructive evaluation (NDE) but before that concept of analysis of welded images and its classification is very important, Multiple methods are available for extraction of features. It has been widely known and also these methods serve as powerful tools for weldments. Image segmentation, Artificial neural network, Pattern recognition, Morphological processing, Using Bayesian networks and Advanced Neuro Fuzzy Techniques can be Analyzed and implemented as per the desired requirements in NDT and NDE.

Efficiency, lightweighting, and electrification endeavors have necessitated an increasing use of next-generation materials, dissimilar material joints, and unconventional stackups in automotive body-in-white assembly and resistance spot welding (RSW). These changes have brought forth novel engineering challenges which reduce the applicability of open-loop RSW control based on institutional knowledge and common RSW practices. Consequently, monitoring the RSW process, evaluating and tracking its outcomes, and leveraging massive data via analytics to derive feedback for closed-loop control have never been so crucial in automotive manufacturing. Despite these industry needs, statistical destructive and nondestructive tests still remain common. In recent years, advancements in computing, artificial intelligence (AI), and most notably deep learning (DL), have enabled the interpretation of data from nondestructive evaluation (NDE) at throughput and performance levels required by the automotive industry. DL continues to establish the state of the art in many computer vision and natural language processing applications, and consequently it continues to be increasingly studied and applied in NDE. In this work, we developed semantic segmentation approaches for post-process and in-process interpretation of ultrasonic data from RSW process monitoring. Algorithms were then developed that estimate measurements from the segmentation mask data leveraging prior knowledge about the weld. For both AI systems, correlation analyses were conducted between ultrasonically-measurable properties and physical measurements from cross-sectioning of dissimilar-material 2- and 3-sheet RSWs. This work demonstrates that AI can enhance, facilitate automation of, and provide new value to well-established NDE methodologies. AI and other smart manufacturing technologies enable new NDE use cases, enable innovative vehicle body designs, facilitate the use of novel joining technologies, improve vehicle safety, improve manufacturing efficiency, and reduce waste – consequently, they are poised to make NDE 4.0 a reality in automotive manufacturing.

Technological advances in the nondestructive evaluation of Oil Country Tubular Goods (OCTG) have brought about enhanced methods to minimize risk factors associated with the drilling or transport of oil and natural gas. This paper focuses on a multidimensional, nondestructive testing (NDT) inspection and measurement package combining wall thickness and outer diameter (OD) measurement data into a single inspection report per tubular. This method of dimensional measurement is performed on the front end of a project, providing end users with numerous benefits from a cost and time perspective, as well as the critical data needed to optimize operations. The package provides high-resolution inspection data for the manufacturer or end user, yielding higher precision burst and collapse strength predictions than previously available. Other benefits of this method include optimization of material placement in casing string design, estimation of cementing fluid volume, and downhole tool clearances among other applications. Confidence and safety levels of burst and collapse ratings are of particular interest to drilling engineers. The wall thickness variations and tubular eccentricity are measured using an automated, nondestructive ultrasonic testing system, utilizing compression wave transducers. The OD measurement is acquired using an automated, nondestructive laser-based testing system. Laser-based testing uses two laser-scan micrometers positioned horizontal to the tubular and 180° apart (Fig. 1). Each laser measures the distance between the tubular and the receiver. These two measurements are combined to calculate the tubular OD. The level of accuracy provided by this technology continues to push the industry forward with higher quality tubulars ranging from the mills to end users.

With the continuous promotion of Industry 4.0, the mass production of composites will be entering Industry 4.0 in the near future. With respect to quality control, nondestructive testing and evaluation (NDT&E) will become nondestructive testing and evaluation (NDE) 4.0 to seamlessly connect with Industry 4.0. Therefore, it is essential to develop innovative NDT&E methods and techniques for NDE 4.0. Although there are many facets of NDE 4.0, intelligent nondestructive testing and evaluation (iNDT&E) can be considered as a technical core of NDE 4.0. Therefore, we propose the development of iNDT&E techniques for composites. In this study, the development processes for NDE 1.0–4.0 are analyzed. The basic connotations of iNDT&E are discussed, and the basic framework and technical elements of iNDT&E are examined. As combined with ultrasonic iNDT&E for composites, the basic progress in iNDT&E techniques is reviewed from the perspectives of ultrasonic equipment, technology and methods, processes, and result evaluations and standards. Future development directions are presented for the iNDT&E techniques in NDE 4.0 for composites.

Composite materials/structures are advancing in product efficiency, cost-effectiveness and the development of superior specific properties. There are increasing demands in their applications to load-carrying structures in aerospace, wind turbines, transportation, medical equipment and so on. Thus, robust and reliable non-destructive testing of composites is essential to reduce safety concerns and maintenance costs. There have been various non-destructive testing methods built upon different principles for quality assurance during the whole lifecycle of a composite product. This article reviews the most established non-destructive testing techniques for detection and evaluation of defects/damage evolution in composites. These include acoustic emission, ultrasonic testing, infrared thermography, terahertz testing, shearography, digital image correlation, as well as X-ray and neutron imaging. For each non-destructive testing technique, we cover a brief historical background, principles, standard practices, equipment and facilities used for composite research. We also compare and discuss their benefits and limitations and further summarise their capabilities and applications to composite structures. Each non-destructive testing technique has its own potential and rarely achieves a full-scale diagnosis of structural integrity. Future development of non-destructive testing techniques for composites will be directed towards intelligent and automated inspection systems with high accuracy and efficient data processing capabilities.

Timber and steel H-piles have been used in the construction of many aging locks, dams, levees, and other structures in the United States. Failure of these piles may adversely affect the structural integrity, and potentially cause damage and/or catastrophic structural failure. This study aims at utilizing state-of-the-art nondestructive testing (NDT) techniques to assess the condition of steel and timber piles. Two pairs of timber piles and steel H-piles were prepared and nondestructively tested in the laboratory. The test parameters were the two types of piles and pre-planned defect sizes/locations. Four NDT methods were investigated including bending wave (BW), dispersive wave (DW), combinations of dispersive-bending wave (DW/BW), and parallel seismic (PS). Wavelet transform, an advanced signal processing method, was used to post-process the NDT data. The method known as “peak identification (PI)” was used to detect the pile tip in both BW and DW/BW tests. The results reveal that the DW/BW accurately detected the pre-planned defects for timber piles. For steel piles, the PI method could only detect deeper defects in the DW/BW tests. The reflected signals from shallower defects had a relatively high signal-to-noise ratio and were hard to interpret. The wavelet transform approach was successfully used to detect both the shallow and deep defects in all the piles. The PS tests were proved to accurately and effectively capture all the pre-planned defects in both the timber and steel H-piles.

Based on the progress of cloud computing technology and the development of nondestructive testing (NDT), some useful exploration and practice are made in the development and application of the integrated Cloud Non-Destructive Testing (CNDT). The key point of CNDT is that all the data collected by NDT sensors/transducers are sent into cloud-side, where the data is analyzed, stored, processed, evaluated, information forecasted and fed back. Bringing together a series of hardware and software resources of NDT technology, the cloud-side is a cloud computing service platform obeying related NDT standards and regulations. The client-side can share all the resources in the cloud and perform remote control and information sharing in the detection process, and can enjoy the convenient service and high efficiency of the cloud detection. In this paper, a simulation platform of CNDT is established and introduced. The platform uses X3 electromagnetic detection prototype client-side and U3 ultrasonic detection prototype client-side. Two cloud service nodes/centers are established in Xiamen and Beijing, China respectively. Based on this CNDT model, some main aspects, such as the superiority, technical points of CNDT and its possibility of further development are demonstrated and discussed.

Large structures such as nuclear power, thermal power, chemical and petroleum refining plants are drawing interest with regard to the economic aspect of extending component life in respect to the poor environment created by high pressure, high temperature, and fatigue, securing safety from corrosion and exceeding their designated life span. Therefore, technology that accurately calculates and predicts degradation and defects of aging materials is extremely important. Among different methods available, nondestructive testing using magnetic methods is effective in predicting and evaluating defects on the surface of or surrounding ferromagnetic structures. It is important to estimate the distribution of magnetic field intensity for applicable magnetic methods relating to industrial nondestructive evaluation. A magnetic camera provides distribution of a quantitative magnetic field with a homogeneous lift-off and spatial resolution. It is possible to interpret the distribution of magnetic field when the dipole model was introduced. This study proposed an algorithm for nondestructive evaluation using dipole model analysis with a scan type magnetic camera. The numerical and experimental considerations of the quantitative evaluation of several sizes and shapes of cracks using magnetic field images of the magnetic camera were examined.

ondestructive evaluation is described as nondestructive testing (NDT), nondestructive inspection (NDI), and nondestructive examination (NDE). The activities associated with the evaluation include testing, inspection, and examination and primarily involve looking at (or through) and/or measuring some characteristic of an object. Frequently there is a desire to determine some characteristic of the object or to determine whether the object contains irregularities, discontinuities, or flaws. These examinations, inspections, or tests are used to determine the physical soundness of a material without impairing its usefulness. Nondestructive evaluation is a powerful tool that can help assure safety, quality, and reliability; increase productivity; decrease liability; protect the environment; and save money. In this article, nondestructive evaluation is represented by the acronym NDE.

The issues involved in automating nondestructive evaluation (NDE) techniques are outlined. Attention is given to research focused on the application of machine learning techniques to the construction and maintenance of knowledge-based systems which are capable of evaluating the readings from nondestructive tests that have been performed on aircraft components. Preliminary results obtained from this research are described. In particular, the authors discuss the application of a symbolic machine learning algorithm, ID3, to the NDE problem. ID3 has been used by Douglas Aircraft to classify defects in sets of standard NDE reference blocks. Based on the preliminary results, a need for an improved method of distinguishing features in the test waveforms is identified. The authors also outline a feature extraction approach from pattern recognition, called scale-space filtering, which can be used to preprocess data for input into a classification algorithm such as ID3. >

In recent years, the importance of non-destructive evaluation has rapidly increased due to the collapse of large structures and the shooting up of safety accidents. The ultrasonic method. which is often used as a major non-destructive testing (NDT) technique in many engineering fields, is playing a significant role as a volumetric test regarded highly for evaluating a material’s integrity. This paper is recommended for publication the detecting any defects of the weld zone in austenitic stainless steel type 304 using ultrasonic waves, employing neural network on the basis of the detected defects and evaluating them. In detecting defects, we drew a distance amplitude curve on a standard scan sensitivity and a preliminary scan sensitivity shown in the correlation between the ultrasonic probe, the instrument and the materials on a quantitative standard, and quantitative evaluation is used to draw a distance amplitude curve. A total of 93. 3% of the defect types was distinguished by testing 30 defects after organizing a neural network system based on the defects on the ultrasonic evaluation and learning the neural network system. Thus the proposed ultrasonic wave-neural network in this work is useful for defects detection and Ultrasonic Non-Destructive Evaluation (UNDE) of the weld zone of austenitic stainless steel 304.