Review and Perspectives: Shape Memory Alloy Composite Systems

Following their discovery in the early 60's, there has been a continuous quest for ways to take advantage of the extraordinary properties of shape memory alloys (SMAs). These intermetallic alloys can be extremely compliant while retaining the strength of metals and can convert thermal energy to mechanical work. The unique properties of SMAs result from a reversible difussionless solid-to-solid phase transformation from austenite to martensite. The integration of SMAs into composite structures has resulted in many benefits, which include actuation, vibration control, damping, sensing, and self-healing. However, despite substantial research in this area, a comparable adoption of SMA composites by industry has not yet been realized. This discrepancy between academic research and commercial interest is largely associated with the material complexity that includes strong thermomechanical coupling, large inelastic deformations, and variable thermoelastic properties. Nonetheless, as SMAs are becoming increasingly accepted in engineering applications, a similar trend for SMA composites is expected in aerospace, automotive, and energy conversion and storage related applications. In an effort to aid in this endeavor, a comprehensive overview of advances with regard to SMA composites and devices utilizing them is pursued in this paper. Emphasis is placed on identifying the characteristic responses and properties of these material systems as well as on comparing the various modeling methodologies for describing their response. Furthermore, the paper concludes with a discussion of future research efforts that may have the greatest impact on promoting the development of SMA composites and their implementation in multifunctional structures

Aeronautical engineering: A continuing bibliography with indexes, supplement 100

This bibliography lists 295 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System in August 1978

Triboinformatic Approaches for Surface Characterization: Tribological and Wetting Properties

Tribology is the study of surface roughness, adhesion, friction, wear, and lubrication of interacting solid surfaces in relative motion. In addition, wetting properties are very important for surface characterization. The combination of Tribology with Machine Learning (ML) and other data-centric methods is often called Triboinformatics. In this dissertation, triboinformatic methods are applied to the study of Aluminum (Al) composites, antimicrobial, and water-repellent metallic surfaces, and organic coatings.Al and its alloys are often preferred materials for aerospace and automotive applications due to their lightweight, high strength, corrosion resistance, and other desired material properties. However, Al exhibits high friction and wear rates along with a tendency to seize under dry sliding or poor lubricating conditions. Graphite and graphene particle-reinforced Al metal matrix composites (MMCs) exhibit self-lubricating properties and they can be potential alternatives for Al alloys in dry or starved lubrication conditions. In this dissertation, artificial neural network (ANN), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), gradient boosting machine (GBM), and hybrid ensemble algorithm-based ML models have been developed to correlate the dry friction and wear of aluminum alloys, Al-graphite, and Al-graphene MMCs with material properties, the composition of alloys and MMCs, and tribological parameters. ML analysis reveals that the hardness, sliding distance, and tensile strength of the alloys influences the COF most significantly. On the other hand, the normal load, sliding speed, and hardness were the most influential parameters in predicting wear rate. The graphite content is the most significant parameter for friction and wear prediction in Al-graphite MMCs. For Al-graphene MMCs, the normal load, graphene content, and hardness are identified as the most influential parameters for COF prediction, while the graphene content, load, and hardness have the greatest influence on the wear rate. The ANN, KNN, SVM, RF, and GBM, as well as hybrid regression models (RF-GBM), with the principal component analysis (PCA) descriptors for COF and wear rate were also developed for Al-graphite MMCs in liquid-lubricated conditions. The hybrid RF-GBM models have exhibited the best predictive performance for COF and wear rate. Lubrication condition, lubricant viscosity, and applied load are identified as the most important variables for predicting wear rate and COF, and the transition from dry to lubricated friction and wear is studied. The micro- and nanoscale roughness of zinc (Zn) oxide-coated stainless steel and sonochemically treated brass (Cu Zn alloy) samples are studied using the atomic force microscopy (AFM) images to obtain the roughness parameters (standard deviation of the profile height, correlation length, the extreme point location, persistence diagrams, and barcodes). A new method of the calculation of roughness parameters involving correlation lengths, extremum point distribution, persistence diagrams, and barcodes are developed for studying the roughness patterns and anisotropic distributions inherent in coated surfaces. The analysis of the 3×3, 4×4, and 5×5 sub-matrices or patches has revealed the anisotropic nature of the roughness profile at the nanoscale. The scale dependency of the roughness features is explained by the persistence diagrams and barcodes. Solid surfaces with water-repellent, antimicrobial, and anticorrosive properties are desired for many practical applications. TiO2/ZnO phosphate and Polymethyl Hydrogen Siloxane (PMHS) based 2-layer antimicrobial and anticorrosive coatings are synthesized and applied to steel, ceramic, and concrete substrates. Surfaces with these coatings possess complex topographies and roughness patterns, which cannot be characterized completely by the traditional analysis. Correlations between surface roughness, coefficient of friction (COF), and water contact angle for these surfaces are obtained. The hydrophobic modification in anticorrosive coatings does not make the coated surfaces slippery and retained adequate friction for transportation application. The dissertation demonstrates that Triboinformatic approaches can be successfully implemented in surface science, and tribology and they can generate novel insights into structure-property relationships in various classes of materials

Deep Learning of Microstructures

The internal structure of materials also called the microstructure plays a critical role in the properties and performance of materials. The chemical element composition is one of the most critical factors in changing the structure of materials. However, the chemical composition alone is not the determining factor, and a change in the production process can also significantly alter the materials\u27 structure. Therefore, many efforts have been made to discover and improve production methods to optimize the functional properties of materials. The most critical challenge in finding materials with enhanced properties is to understand and define the salient features of the structure of materials that have the most significant impact on the desired property. In other words, by process, structure, and property (PSP) linkages, the effect of changing process variables on material structure and, consequently, the property can be examined and used as a powerful tool in material design with desirable characteristics. In particular, forward PSP linkages construction has received considerable attention thanks to the sophisticated physics-based models. Recently, machine learning (ML), and data science have also been used as powerful tools to find PSP linkages in materials science. One key advantage of the ML-based models is their ability to construct both forward and inverse PSP linkages. Early ML models in materials science were primarily focused on process-property linkages construction. Recently, more microstructures are included in the materials design ML models. However, the inverse design of microstructures, i.e., the prediction of vii process and chemistry from a microstructure morphology image have received limited attention. This is a critical knowledge gap to address specifically for the problems that the ideal microstructure or morphology with the specific chemistry associated with the morphological domains are known, but the chemistry and processing which would lead to that ideal morphology are unknown. In this study, first, we propose a framework based on a deep learning approach that enables us to predict the chemistry and processing history just by reading the morphological distribution of one element. As a case study, we used a dataset from spinodal decomposition simulation of Fe-Cr-Co alloy created by the phase-field method. The mixed dataset, which includes both images, i.e., the morphology of Fe distribution, and continuous data, i.e., the Fe minimum and maximum concentration in the microstructures, are used as input data, and the spinodal temperature and initial chemical composition are utilized as the output data to train the proposed deep neural network. The proposed convolutional layers were compared with pretrained EfficientNet convolutional layers as transfer learning in microstructure feature extraction. The results show that the trained shallow network is effective for chemistry prediction. However, accurate prediction of processing temperature requires more complex feature extraction from the morphology of the microstructure. We benchmarked the model predictive accuracy for real alloy systems with a Fe-Cr-Co transmission electron microscopy micrograph. The predicted chemistry and heat treatment temperature were in good agreement with the ground truth. The treatment time was considered to be constant in the first study. In the second work, we propose a fused-data deep learning framework that can predict the heat treatment time as well as temperature and initial chemical compositions by reading the morphology of Fe distribution and its concentration. The results show that the trained deep neural network has the highest accuracy for chemistry and then time and temperature. We identified two scenarios for inaccurate predictions; 1) There are several paths for an identical microstructure, and 2) Microstructures reach steady-state morphologies after a long time of aging. The error analysis shows that most of the wrong predictions are not wrong, but the other right answers. We validated the model successfully with an experimental Fe-Cr-Co transmission electron microscopy micrograph. Finally, since the data generation by simulation is computationally expensive, we propose a quick and accurate Predictive Recurrent Neural Network (PredRNN) model for the microstructure evolution prediction. Essentially, microstructure evolution prediction is a spatiotemporal sequence prediction problem, where the prediction of material microstructure is difficult due to different process histories and chemistry. As a case study, we used a dataset from spinodal decomposition simulation of Fe-Cr-Co alloy created by the phase-field method for training and predicting future microstructures by previous observations. The results show that the trained network is capable of efficient prediction of microstructure evolution

Control of structures using SMA wires and piezoelectric patches

Smart materials and structures systems are increasingly being developed to handle more complex problems. One of the main research schemes is the augmentation of the control authority of the smart actuators used in such systems. The augmentation can be obtained by constructing hybrid and multi- smart materials actuator systems and/or by the optimization of the location and orientation of those actuators. In the first part of this study, the alteration of the natural frequency of composite structures using Nitinol-based Shape Memory Alloy (SMA) wires will be presented using the analyses of strain energy perturbations on a plate. These governing strain equations were solved analytically and numerically to show the effect of point forces acting in a distributive manner and the subsequent effect it has on the plate's stiffness and hence it's natural frequency. In the second part of the thesis, a more complex loading condition is considered to investigate piezoceramic actuator control authority in relation to wing flutter control. The advancement in the application of active material induced-strain actuation such as piezoelectric materials in suppression of structural vibrations drew wide interest in its use for wing flutter control. Higher flutter speed and hence wider operating envelope was achieved by delaying the coalescence of the eigenvalues for plunge and twist modes. . This delay is obtained by adding more strain energy to the system as a result of the activation of the piezoelectric actuators. Most of the studies done were by controlling the plunge/bending motion, where the piezoelectric actuators are bonded longitudinally to produce bending moments. In this study, the control of the pitch/twisting motion was investigated and it showed better control of flutter by using simultaneous multi-actuations compared to single piezo actuations. It was shown that within the scope of the angular orientations of the piezoelectric patches investigated in this study, piezoelectric patches oriented about +150 from the beam's longitudinal ax is resulted in the most optimal piezo-configuration. This was corroborated by both the numerical flutter speed and actuator moment evaluations. In addition, the orientation of the piezoelectric patches was shown to significantly affect the pitch angle of the beam relative to each other. The damping ratio was also investigated and this showed greater instability for piezoelectric patches oriented at negative angles, thus further supporting the finding of the aforementioned optimal orientation of +150. These findings confirmed the dominance of the base (closest to the fixed portion of the beam) piezo when actuated with other piezos

Smart Material Wing Morphing for Unmanned Aerial Vehicles.

Morphing, or geometric adaptation to off-design conditions, has been considered in aircraft design since the Wright Brothers’ first powered flight. Decades later, smooth, bio-mimetic shape variation for control over aerodynamic forces still remains elusive. Unmanned Aerial Vehicles are prime targets for morphing implementation as they must adapt to large changes in flight conditions associated with locally varying wind or large changes in mass associated with payload delivery. The Spanwise Morphing Trailing Edge (SMTE) concept is developed to locally vary the trailing edge camber of a wing or control surface, functioning as a modular replacement for conventional ailerons without altering the wing’s spar box. The SMTE design was realized utilizing alternating active sections of Macro Fiber Composites (MFCs) driving internal elastomeric compliant mechanisms and passive sections of anisotropic, elastomeric skin with tailorable stiffness, produced by additive manufacturing. Experimental investigations of the modular design via a new scaling methodology for reduced-span test articles revealed that increased use of more MFCs within the active section did not increase aerodynamic performance due to asymmetric voltage constraints. The comparative mass and aerodynamic gains for the SMTE concept are evaluated for a representative finite wing as compared with a conventional, articulated flap wing. Informed by a simplistic system model and measured control derivatives, experimental investigations identified a reduction in the adaptive drag penalty up to 20% at off-design conditions. To investigate the potential for augmented aeroelastic performance and actuation range, a hybrid multiple-smart material morphing concept, the Synergistic Smart Morphing Aileron (SSMA), is introduced. The SSMA leverages the properties of two different smart material actuators to achieve performance exceeding that of the constituent materials. Utilizing the relatively higher work density and phase transformation of Shape-Memory Alloys combined with the larger bandwidth and conformal bending of MFCs, the resultant design is demonstrated to achieve the desired goals while providing additional control authority at stall and for unsteady conditions through synergistic use of reflex actuation. These advances highlight and motivate new morphing structures for the growing field of UAVs in which adaptation involves advanced compliance tailoring of complex geometry with synergistic actuation of embedded, smart materials.PhDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111533/1/alexmp_1.pd

Remanufacturing and Advanced Machining Processes for New Materials and Components

"Remanufacturing and Advanced Machining Processes for Materials and Components presents current and emerging techniques for machining of new materials and restoration of components, as well as surface engineering methods aimed at prolonging the life of industrial systems. It examines contemporary machining processes for new materials, methods of protection and restoration of components, and smart machining processes. • Details a variety of advanced machining processes, new materials joining techniques, and methods to increase machining accuracy • Presents innovative methods for protection and restoration of components primarily from the perspective of remanufacturing and protective surface engineering • Discusses smart machining processes, including computer-integrated manufacturing and rapid prototyping, and smart materials • Provides a comprehensive summary of state-of-the-art in every section and a description of manufacturing methods • Describes the applications in recovery and enhancing purposes and identifies contemporary trends in industrial practice, emphasizing resource savings and performance prolongation for components and engineering systems The book is aimed at a range of readers, including graduate-level students, researchers, and engineers in mechanical, materials, and manufacturing engineering, especially those focused on resource savings, renovation, and failure prevention of components in engineering systems.

Adaptiver Suchansatz zur multidisziplinären Optimierung von Leichtbaustrukturen unter Verwendung hybrider Metaheuristik

Within the last few years environmental regulations, safety requirements and market competitions forced the automotive industry to open up a wide range of new technologies. Lightweight design is considered as one of the most innovative concepts to fulfil environmental, safety and many other objectives at competitive prices. Choosing the best design and production process in the development period is the most significant link in the automobile production chain. A wide range of design and process parameters needs to be evaluated to achieve numerous goals of production. These goals often stand in conflict with each other. In addition to the variation of the concepts and following the objectives, some limitations such as manufacturing restrictions, financial limits, and deadlines influence the choice of the best combination of variables. This study introduces a structural optimization tool for assemblies made of sheet metal, e.g. the automobile body, based on parametrization and evaluation of concepts in CAD and CAE. This methodology focuses on those concepts, which leads to the use of the right amount of light and strong material in the right place, instead of substituting the whole structure with the new material. An adaptive hybrid metaheuristic algorithm is designed to eliminate all factors that would lead to a local minimum instead of global optimum. Finding the global optimum is granted by using some explorative and exploitative search heuristics, which are intelligently organized by a central controller. Reliability, accuracy and the speed of the proposed algorithm are validated via a comparative study with similar algorithms for an academic optimization problem, which shows valuable results. Since structures might be subject to a wide range of load cases, e.g. static, cyclic, dynamic, temperature-dependent etc., these requirements need to be addressed by a multidisciplinary optimization algorithm. To handle the nonlinear response of objectives and to tackle the time-consuming FEM analyses in crash situations, a surrogate model is implemented in the optimization tool. The ability of such tool to present the optimum results in multi-objective problems is improved by using some user-selected fitness functions. Finally, an exemplary sub-assembly made of sheet metal parts from a car body is optimized to enhance both, static load case and crashworthiness.Die Automobilindustrie hat in den letzten Jahren unter dem Druck von Umweltvorschriften, Sicherheitsanforderungen und wettbewerbsfähigem Markt neue Wege auf dem Gebiet der Technologien eröffnet. Leichtbau gilt als eine der innovativsten und offenkundigsten Lösungen, um Umwelt- und Sicherheitsziele zu wettbewerbsfähigen Preisen zu erreichen. Die Wahl des besten Designs und Verfahrens für Produktionen in der Entwicklungsphase ist der wichtigste Ring der Automobilproduktionskette. Um unzählige Produktionsziele zu erreichen, müssen zahlreiche Design- und Prozessparameter bewertet werden. Die Anzahl und Variation der Lösungen und Ziele sowie einige Einschränkungen wie Fertigungsbeschränkungen, finanzielle Grenzen und Fristen beeinflussen die Auswahl einer guten Kombination von Variablen. In dieser Studie werden strukturelle Optimierungswerkzeuge für aus Blech gefertigte Baugruppen, z. Karosserie, basierend auf Parametrisierung und Bewertung von Lösungen in CAD bzw. CAE. Diese Methodik konzentriert sich auf die Lösungen, die dazu führen, dass die richtige Menge an leichtem / festem Material an der richtigen Stelle der Struktur verwendet wird, anstatt vollständig ersetzt zu werden. Eine adaptive Hybrid-Metaheuristik soll verhindern, dass alle Faktoren, die Bedrohungsoptimierungstools in einem lokalen Minimum konvergieren, anstelle eines globalen Optimums. Das Auffinden des globalen Optimums wird durch einige explorative und ausbeuterische Such Heuristiken gewährleistet. Die Zuverlässigkeit, Genauigkeit und Geschwindigkeit des vorgeschlagenen Algorithmus wird mit ähnlichen Algorithmen in akademischen Optimierungsproblemen validiert und führt zu respektablen Ergebnissen. Da Strukturen möglicherweise einem weiten Bereich von Lastfällen unterliegen, z. statische, zyklische, dynamische, Temperatur usw. Möglichkeit der multidisziplinären Optimierung wurde in Optimierungswerkzeugen bereitgestellt. Um die nichtlineare Reaktion von Zielen zu überwinden und um den hohen Zeitverbrauch von FEM-Analysen in Absturzereignissen zu bewältigen, könnte ein Ersatzmodell vom Benutzer verwendet werden. Die Fähigkeit von Optimierungswerkzeugen, optimale Ergebnisse bei Problemen mit mehreren Zielsetzungen zu präsentieren, wird durch die Verwendung einiger vom Benutzer ausgewählten Fitnessfunktionen verbessert. Eine Unterbaugruppe aus Blechteilen, die zur Automobilkarosserie gehören, ist optimiert, um beide zu verbessern; statischer Lastfall und Crashsicherheit