Measurements, Models, Systems and Design

531 s.

Development of a post-form strength prediction model for a 6xxx aluminium alloy in a novel forming process

Accurate prediction of the post-form strength of structural components made from 6xxx series aluminium alloys has been a challenge, especially when the alloy undergoes complex thermo-mechanical processes such as the Fast light Alloys Stamping Technology (FAST). This process involves ultra-fast heating, high temperature plastic deformation, rapid quenching and is followed by multi-stage artificial ageing heat treatment. The strength of the material evolves with the formation of second phase precipitates during the entire process. The widely accepted precipitation sequence is SSSS - clusters - β” - β’ - β. However, due to the complexity of deformations and thermal profile during the process, the classic theory is not applicable. Therefore, in this research, precipitation behaviour during ultra-fast heating, viscoplastic behaviour, effect of residual dislocations generated during high temperature deformation, quenching sensitivity and multi-stage artificial ageing response have been comprehensively studied. A set of experiments, including ultra-fast heating tests, uniaxial tensile tests, pre-straining uniaxial tensile tests, quenching tests, artificial ageing tests and TEM observations were conducted to provide a thorough understanding of the novel forming technology. The underlying mechanisms for the FAST process were investigated through the in-depth analysis of experimental results. ·Under ultra-fast heating conditions, most of the precipitates are dissolved and the spherical pre-β” precipitates are formed and finely dispersed in the aluminium matrix, which are beneficial to accelerate the subsequent precipitation process. ·The residual dislocations, generated during plastic deformation, strengthen the material and act as nucleation sites for precipitates. The peak strength is reduced owing to the uneven accumulation of precipitates around dislocations. ·The coarse β’ and β precipitates induced due to the insufficient quenching are detrimental to precipitation response. These quench-induced precipitates consume both solute atoms and vacancies, which are unable to be reversely transferred to the preferred needle-shaped β” precipitates. Based on the scientific achievements, a mechanism-based unified post-form strength (PFS) prediction model was developed ab-initio to predict the strength evolution of the material during the entire complex FAST process with highly efficient computation. Constitutive equations were proposed to model the viscoplastic behaviour at elevated temperature. Important microstructural parameters, including dislocation density, volume fraction, radius of precipitates and solute concentration were correlated to predict the material strength. The particle size distribution (PSD) sub-model was further established to accurately interpret the detailed microstructural changes during the complex thermo-mechanical processes. Furthermore, the model has been programmed into an advanced functional module ‘Tailor’ and implemented into a cloud based FEA platform. The predictive capability of the module was verified by conducting forming tests of a U-shaped component in a dedicated pilot production line. It was found that the ‘Tailor’ module was able to precisely predict the post-form strength in agreement with experiments, with a deviation of less than 7% compared to experimental results.Open Acces

Intelligent Systems

This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper heuristics and semantic reasoning all benefit from the systems covered in this book. Intelligent systems can also be applied in education and generating the intelligent distributed eLearning architecture, as well as in a large number of technical fields, such as industrial design, manufacturing and utilization, e.g., in precision agriculture, cartography, electric power distribution systems, intelligent building management systems, drilling operations etc. Furthermore, decision making using fuzzy logic models, computational recognition of comprehension uncertainty and the joint synthesis of goals and means of intelligent behavior biosystems, as well as diagnostic and human support in the healthcare environment have also been made easier

Structural Response Evaluation Using Non-Uniform Sensor Arrays

Sensor arrays strategically deployed on various offshore structures may provide valuable information in addressing issues related to the complex dynamic response behavior due to varying environments, changing hydrodynamics and purposely attached engineering devices. The current work was devoted to developing techniques to (1) optimize the sensor array according to specific engineering goals, (2) use response data obtained from the sensors to evaluate structures‟ extreme responses, (3) extract modal parameters, and (4) analyze strength conditions. The computational tool developed in this study integrated genetic algorithms, modal recognition techniques, damage detection methods, time series and spectral analysis methods. Genetic algorithms, originally proposed for solving optimization problems based on natural selection, have demonstrated capabilities in obtaining the optimal sensor array configurations in extracting a single mode or two modes simultaneously. This finding laid the foundation for further modal recognition and damage analysis. The first application discussed herein focused on response evaluation of long and flexible subsea transmission lines; specifically, evaluating the performance of flow-induced vibration suppression devices and buoyancy elements. With laboratory data, the study demonstrated that airfoil fairings, ribbon fairings and helical strakes can all effectively suppress the undesired vibrations in a uniform current; however, the first two devices were not quite effective, especially airfoil fairings, when the structures were subjected to combined loads of current and waves (though all devices significantly increased the damping). In addition, the study showed modal parameters extracted with optimized sensor arrays can help detect, locate and size damages in a structure via numerical simulation (though the performance of the methodology may decrease with localized non-uniform strength profiles and excessive marine growth). The second application extended the methodologies from 1-D beam-like structures to 2-D plate-like structures. These studies focused on strength analyses of various ice sheet formations. The results illustrated, in spite of the exponentially increased computational volume, fine-tuned genetic algorithms can still locate near optimal sensor arrays regardless of boundary conditions and placement restrictions due to complicated Arctic environments. Furthermore, the damage detection methodology utilized herein proved to be able not only to detect weak regions but also to detect strengthened areas in ice sheets, for example an ice ridge, thus complete strength analyses of selected ice sheet formations can be conducted

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms