Novel Approaches for Nondestructive Testing and Evaluation

Nondestructive testing and evaluation (NDT&E) is one of the most important techniques for determining the quality and safety of materials, components, devices, and structures. NDT&E technologies include ultrasonic testing (UT), magnetic particle testing (MT), magnetic flux leakage testing (MFLT), eddy current testing (ECT), radiation testing (RT), penetrant testing (PT), and visual testing (VT), and these are widely used throughout the modern industry. However, some NDT processes, such as those for cleaning specimens and removing paint, cause environmental pollution and must only be considered in limited environments (time, space, and sensor selection). Thus, NDT&E is classified as a typical 3D (dirty, dangerous, and difficult) job. In addition, NDT operators judge the presence of damage based on experience and subjective judgment, so in some cases, a flaw may not be detected during the test. Therefore, to obtain clearer test results, a means for the operator to determine flaws more easily should be provided. In addition, the test results should be organized systemically in order to identify the cause of the abnormality in the test specimen and to identify the progress of the damage quantitatively

Image-Based Feature Tracking Algorithms for Real-Time Clad Height Detection in Laser Cladding

In laser cladding, a material, usually in the form of powder, is deposited on a substrate. Powder particles are intermingled with inert gas and fed by a powder feeder system on the substrate. Laser is employed to melt the additive material and a small layer of surface of the substrate simultaneously. While the powder is being deposited, the laser melts the powder particles and the melted powder particles join the melt pool on the substrate beneath the laser beam. Generating relative motion between the laser focal point and the substrate will result in moving melt pool on the substrate. This will lead to addition of a desired material to the substrate with desired thickness and good bonding as well as minimum dilution. In addition, by producing clads beside and on the top of each other a functional component can be built in a layer by layer fashion. Despite many advantages of laser cladding, it is highly sensitive to internal and external disturbances. This makes a closed-loop control system for laser cladding inevitable. Utilizing a closed-loop control system in laser cladding makes the system insensitive to external and internal disturbances. Having a closed-loop control system for laser cladding would contribute to substantial improvement in clad quality and cost reduction. Feedback sensor is an essential part in a closed-loop control system. Among different parameters that can be used as feedback signals in a closed-loop control of laser cladding, melt pool geometry and in particular clad height is of great importance specifically for the purpose of rapid prototyping. This thesis presents novel algorithms for real-time detection of clad height in laser cladding. This is accomplished by the following: Tackling the issues pertinent to image acquisition in the presence of harsh and intensive light is scrutinized. Important parameters of digital cameras related to selection of proper type of CCD cameras in order to overcome the existent harsh condition are presented. Also, the existent light in laser cladding arisen from different sources is analyzed and based upon that proper bandpass filters and neutral filters are selected. All these lead to capture relatively sharp and clear images of the melt pool. Capturing good quality pictures potentially would provide valuable information about the process. This information could include, but is not limited to, melt pool geometry (i.e., melt pool height, width, melt pool profile, and wet angle), angle of solidification, melt pool temperature, and melt pool temperature distribution. Furthermore, the issues regarding path dependency of the melt pool image are addressed by using a trinocular cameras configuration. By utilizing this, always two cameras monitor the front end of the melt pool regardless of the direction of the clad. Image analysis of the grabbed images is also discussed. Image thresholding is one of the most formidable tasks in image processing and this difficulty is intensified due to characteristics of the grabbed images of the melt pool (e.g., surrounding hazy area around the melt pool). Applying hard partitioning thresholding method did not lead to detec- tion of the melt pool accurately. As a result, fuzzy thresholding by minimizing of the measure of fuzziness is developed and its performance is investigated. The effect of three important membership functions, triangular, Gaussian, and generalized Bell on the performance of the thresholding method is investigated. Also, Image thresholding by utilizing fuzzy c-means clustering is developed. Applying the developed thresholding methods show promising results. Among the developed thresholding methods, fuzzy thresholding with minimizing the measure of fuzziness with Gaussian membership function is selected for the implementation in the algorithm. Finally, Image feature tracking module is presented. The detected borders of the melt pool images are transformed from image plane to the world plane by using a perspective transformation. Four features of the elliptical features of the projected melt pool borders are selected. These four features along with the angle of tangential path vector with respect to the corresponding right hand side camera's axis are fed into an Elman recurrent neural network. The proposed algorithms and the trained neural network are utilized in the process resulting in acceptable detection of the clad height in deposition of straight clads for a specific direction. It is concluded that the system can detect the clad height with about ±0.15 mm maximum error

Heat Assisted Machining of Nickel Base Alloys: Experimental and Numerical Analysis

Nickel base alloys are frequently used in aerospace industries, marine, biomedical application and other demanding industries due to their high strength, high hardness, resistant to corrosion and ability to withstand at elevated temperature. But machining of these materials in conventional way impairs severely their machinability due to certain inherent properties like low thermal conductivity, high chemical affinity and presence of hard particles in the microstructure etc. Therefore, tool life is reduced, due to the abrasion wear from the hard particles and high temperature of the tool-chip interface due to diffusion wear during machining of nickel base alloys. In this work, hot machining is introduced for processing of nickel base alloys like Inconel 718, Inconel 625, and Monel 400. In this technique, heating on the workpiece is combined with conventional turning process was used to enhanced machinability of nickel base alloy without compromise quality and productive. The study revealed that the influence of the workpiece temperature on the workpiece surface enhanced machining performance in terms of better surface finish, MRR, and reduction of forces, wear compared to conventional turning process. The surface integrity has been studied in terms of surface roughness, and microhardness beneath the machined surface in hot machining operation. Finite element modeling was also employed to prediction of cutting force, temperature distribution, stress, in hot turning of Inconel 718. The finite element results were compared with the experimental value and close agreement was found. In any industries production of parts along with tool life, surface finish is the major concern. In order to optimize the machining of nickel base alloys, optimization technique was performed using desirability and principal component analysis. Finally, machinability comparison was made between three materials, in order to understand effect of machining parameters along with workpiece temperature. In the literature, no research studies were found on flame heat machining of nickel base alloys (Inconel 625, Inconel 718 and Monel 400). The research led to various contributions to finding in terms of experimental investigation, optimization and FEM modeling. The contribution of the thesis should be of interest who works in the areas of machining of hard materials

Engineering Principles

Over the last decade, there has been substantial development of welding technologies for joining advanced alloys and composites demanded by the evolving global manufacturing sector. The evolution of these welding technologies has been substantial and finds numerous applications in engineering industries. It is driven by our desire to reverse the impact of climate change and fuel consumption in several vital sectors. This book reviews the most recent developments in welding. It is organized into three sections: “Principles of Welding and Joining Technology,” “Microstructural Evolution and Residual Stress,” and “Applications of Welding and Joining.” Chapters address such topics as stresses in welding, tribology, thin-film metallurgical manufacturing processes, and mechanical manufacturing processes, as well as recent advances in welding and novel applications of these technologies for joining different materials such as titanium, aluminum, and magnesium alloys, ceramics, and plastics

Self-healing of Concrete Under Diverse Environmental Exposure

Self-healing efficiency of cement-based materials has so far been evaluated mostly through the healing of surface cracks, without adequately capturing the dominant effects of environmental exposure or accurately quantifying the volume of cracks healed. In addition, the effects of diverse additions such as silica-based materials, swelling agents, superabsorbent polymers, and carbonating minerals on self-healing performance under different environmental exposure, remain largely unexplored. In this dissertation, multiple test methods were used to investigate self-healing of cracks in cement mortar incorporating metakaolin, bentonite, fly ash, superabsorbent polymers, and calcium carbonate microfiller under different environmental exposure (i.e. cold and hot temperatures, high and low humidity, wet and dry cycles, and continuous underwater submersion). Change in crack width was monitored using optical microscopy. Scanning electron microscopy coupled with energy disperse X-ray analysis was used to identify healing compounds. Mercury intrusion porosimetry and water absorption were employed to assess porosity. X-ray computed micro-tomography (X-ray µCT) with 3-dimensional image processing was used to segment and quantify cracks before and after healing. The findings should stimulate concerted research efforts to bridge the gap between ideal laboratory conditions and realistic field exposure in future self-healing research endeavors. Furthermore, an attempt was made to develop a hybrid artificial intelligence-based model to accurately predict the ability of concrete to heal its own cracks. A comprehensive database of concrete crack healing was created and used to train the proposed GA–ANN model. The results showed that the proposed GA–ANN model can capture the complex effects of various self-healing agents (e.g. biochemical material, silica-based additive, expansive and crystalline components) on the self-healing performance in cement-based materials. This could allow tailoring self-healing strategies for enhancing the durability design of concrete, thus leading to reduced maintenance and repair costs of concrete civil infrastructure

Empowering Materials Processing and Performance from Data and AI

Third millennium engineering address new challenges in materials sciences and engineering. In particular, the advances in materials engineering combined with the advances in data acquisition, processing and mining as well as artificial intelligence allow for new ways of thinking in designing new materials and products. Additionally, this gives rise to new paradigms in bridging raw material data and processing to the induced properties and performance. This present topical issue is a compilation of contributions on novel ideas and concepts, addressing several key challenges using data and artificial intelligence, such as:- proposing new techniques for data generation and data mining;- proposing new techniques for visualizing, classifying, modeling, extracting knowledge, explaining and certifying data and data-driven models;- processing data to create data-driven models from scratch when other models are absent, too complex or too poor for making valuable predictions;- processing data to enhance existing physic-based models to improve the quality of the prediction capabilities and, at the same time, to enable data to be smarter; and- processing data to create data-driven enrichment of existing models when physics-based models exhibit limits within a hybrid paradigm

IN-SITU CHARACTERIZATION OF SURFACE QUALITY IN γ-TiAl AEROSPACE ALLOY MACHINING

The functional performance of critical aerospace components such as low-pressure turbine blades is highly dependent on both the material property and machining induced surface integrity. Many resources have been invested in developing novel metallic, ceramic, and composite materials, such as gamma-titanium aluminide (γ-TiAl), capable of improved product and process performance. However, while γ-TiAl is known for its excellent performance in high-temperature operating environments, it lacks the manufacturing science necessary to process them efficiently under manufacturing-specific thermomechanical regimes. Current finish machining efforts have resulted in poor surface integrity of the machined component with defects such as surface cracks, deformed lamellae, and strain hardening. This study adopted a novel in-situ high-speed characterization testbed to investigate the finish machining of titanium aluminide alloys under a dry cutting condition to address these challenges. The research findings provided insight into material response, good cutting parameter boundaries, process physics, crack initiation, and crack propagation mechanism. The workpiece sub-surface deformations were observed using a high-speed camera and optical microscope setup, providing insights into chip formation and surface morphology. Post-mortem analysis of the surface cracking modes and fracture depths estimation were recorded with the use of an upright microscope and scanning white light interferometry, In addition, a non-destructive evaluation (NDE) quality monitoring technique based on acoustic emission (AE) signals, wavelet transform, and deep neural networks (DNN) was developed to achieve a real-time total volume crack monitoring capability. This approach showed good classification accuracy of 80.83% using scalogram images, in-situ experimental data, and a VGG-19 pre-trained neural network, thereby establishing the significant potential for real-time quality monitoring in manufacturing processes. The findings from this present study set the tone for creating a digital process twin (DPT) framework capable of obtaining more aggressive yet reliable manufacturing parameters and monitoring techniques for processing turbine alloys and improving industry manufacturing performance and energy efficiency