From model-driven to data-driven : a review of hysteresis modeling in structural and mechanical systems

Hysteresis is a natural phenomenon that widely exists in structural and mechanical systems. The characteristics of structural hysteretic behaviors are complicated. Therefore, numerous methods have been developed to describe hysteresis. In this paper, a review of the available hysteretic modeling methods is carried out. Such methods are divided into: a) model-driven and b) datadriven methods. The model-driven method uses parameter identification to determine parameters. Three types of parametric models are introduced including polynomial models, differential based models, and operator based models. Four algorithms as least mean square error algorithm, Kalman filter algorithm, metaheuristic algorithms, and Bayesian estimation are presented to realize parameter identification. The data-driven method utilizes universal mathematical models to describe hysteretic behavior. Regression model, artificial neural network, least square support vector machine, and deep learning are introduced in turn as the classical data-driven methods. Model-data driven hybrid methods are also discussed to make up for the shortcomings of the two methods. Based on a multi-dimensional evaluation, the existing problems and open challenges of different hysteresis modeling methods are discussed. Some possible research directions about hysteresis description are given in the final section

Dynamic characterization of high performance materials for application to cultural heritage

Natural hazards, such as earthquakes, can compromise the integrity of the cultural heritage with potentially devastating effects. The reduction of the seismic vulnerability of the cultural heritage constitutes a question of maximum importance especially in countries where vast cultural heritage combines with a medium or high seismic risk, such as in Italy. From the second half of the last century, the scientific community edited a number of important documents and charts for the conservation, reinforcement and restoration of the cultural heritage. The aim is to mitigate the seismic vulnerability of the cultural heritage. This research focused on high performance materials for applications aimed to structural and seismic protection of cultural heritage, with a special focus on historical masonry structures. In particular, the final aim is to define a self-diagnosis strategy for fibres, yarns and ties in view of efficient, non-invasive and reversible interventions on cultural heritage buildings. In order to set up the scene, the present thesis starts by introducing the reader to the seismic protection of cultural heritage thorough an extensive review on high performance materials, strengthening techniques and systems, taking care to highlight real world applications and limitations of their use. The second step of this work concerns in the mechanical and rheological characterization of high performance material fibres. The materials investigated are essentially Kevlar® 29 (para-aramid), Carbon and Silicon Carbide. To reach this goal, an extensive experimental testing campaign was conducted on fibres and yarns in accordance with specific protocols. A further step was defining appropriate damage indices for different materials, with a special focus on Kevlar® 29. Within the same research programme, a novel testing machine was also designed in cooperation with the Laboratory of Electronic Measurements of the Politecnico di Torino. A prototype-testing machine for dynamic testing on high resistance fibres was built using recycled materials and components. A distinctive feature of this machine is that it can apply to the sample any kind of dynamic excitation (random, impulse, harmonic etc.). A second testing campaign concerned the durability of Kevlar® 29 fibres, which are known to be sensitive to long-term exposure to UV radiation. Accordingly, for this campaign, the samples were artificially damaged by using UV lamps. The analysis of the resonance profiles allowed for the extraction of parameters such as the elastic moduli, quality factors, and non-linear coefficient for a set of fibres. In particular, non-linearity parameters derived from the Krylov-Bogoliubov method demonstrated to be consistent with the damage affecting the fibres. The final chapter of the dissertation concerns a new concept for a tie endowed with self-diagnosis properties, which are obtained by integrating a low cost testing device into the tie model. The self-diagnosis properties system of existing structures has an important role in the preservation of the cultural heritage because the best therapy is preventive maintenance. Specifically, the para-aramid tie system proposed for the reinforcement of historic building constitutes a non-invasive, reversible and repeatable intervention, as required by the main guidelines on preservation of cultural heritage

Six Decades of Flight Research: An Annotated Bibliography of Technical Publications of NASA Dryden Flight Research Center, 1946-2006

Titles, authors, report numbers, and abstracts are given for nearly 2900 unclassified and unrestricted technical reports and papers published from September 1946 to December 2006 by the NASA Dryden Flight Research Center and its predecessor organizations. These technical reports and papers describe and give the results of 60 years of flight research performed by the NACA and NASA, from the X-1 and other early X-airplanes, to the X-15, Space Shuttle, X-29 Forward Swept Wing, X-31, and X-43 aircraft. Some of the other research airplanes tested were the D-558, phase 1 and 2; M-2, HL-10 and X-24 lifting bodies; Digital Fly-By-Wire and Supercritical Wing F-8; XB-70; YF-12; AFTI F-111 TACT and MAW; F-15 HiDEC; F-18 High Alpha Research Vehicle, F-18 Systems Research Aircraft and the NASA Landing Systems Research aircraft. The citations of reports and papers are listed in chronological order, with author and aircraft indices. In addition, in the appendices, citations of 270 contractor reports, more than 200 UCLA Flight System Research Center reports, nearly 200 Tech Briefs, 30 Dryden Historical Publications, and over 30 videotapes are included

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

Friction Force Microscopy of Deep Drawing Made Surfaces

Aim of this paper is to contribute to micro-tribology understanding and friction in micro-scale interpretation in case of metal beverage production, particularly the deep drawing process of cans. In order to bridging the gap between engineering and trial-and-error principles, an experimental AFM-based micro-tribological approach is adopted. For that purpose, the can’s surfaces are imaged with atomic force microscopy (AFM) and the frictional force signal is measured with frictional force microscopy (FFM). In both techniques, the sample surface is scanned with a stylus attached to a cantilever. Vertical motion of the cantilever is recorded in AFM and horizontal motion is recorded in FFM. The presented work evaluates friction over a micro-scale on various samples gathered from cylindrical, bottom and round parts of cans, made of same the material but with different deep drawing process parameters. The main idea is to link the experimental observation with the manufacturing process. Results presented here can advance the knowledge in order to comprehend the tribological phenomena at the contact scales, too small for conventional tribology

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems