Friction Stir Welding Experiments on AZ31B Alloy to Analyse Mechanical Properties and Optimize Process Variables by TOPSIS Method

The present work involves friction stir welding of AZ31B magnesium alloy plates by using copper as tool pin material. The friction stir welding input factors namely tool pin profile, tool rotational speed, tool feed and tool angle were varied to find their influence on the quality of the processed zone. For mechanical and micro structural study of the processed specimens, tensile test, hardness test and microscopy tests were carried out. The Taguchi\u27s experimental combination of L18 mixed orthogonal array has been utilized for conducting experiments. ANOVA was utilized to evaluate the influence of each input factor on the response measures. TOPSIS, the multi-response optimization technique was applied to obtain the optimal setting for getting enhanced results. The experimental results of the optimum set provided a tensile strength of 206.35 MPa, Percentage elongation of 7.4% and Vickers hardness of 68 which are 88.2%, 52.9% and 79% of the corresponding property values of the base material respectively. Microstructural study revealed the refinement of grains in the processed zone. However the enhancement of properties is prevented by the occurrence of defects

Together we stand, Together we fall, Together we win: Dynamic Team Formation in Massive Open Online Courses

Massive Open Online Courses (MOOCs) offer a new scalable paradigm for e-learning by providing students with global exposure and opportunities for connecting and interacting with millions of people all around the world. Very often, students work as teams to effectively accomplish course related tasks. However, due to lack of face to face interaction, it becomes difficult for MOOC students to collaborate. Additionally, the instructor also faces challenges in manually organizing students into teams because students flock to these MOOCs in huge numbers. Thus, the proposed research is aimed at developing a robust methodology for dynamic team formation in MOOCs, the theoretical framework for which is grounded at the confluence of organizational team theory, social network analysis and machine learning. A prerequisite for such an undertaking is that we understand the fact that, each and every informal tie established among students offers the opportunities to influence and be influenced. Therefore, we aim to extract value from the inherent connectedness of students in the MOOC. These connections carry with them radical implications for the way students understand each other in the networked learning community. Our approach will enable course instructors to automatically group students in teams that have fairly balanced social connections with their peers, well defined in terms of appropriately selected qualitative and quantitative network metrics.Comment: In Proceedings of 5th IEEE International Conference on Application of Digital Information & Web Technologies (ICADIWT), India, February 2014 (6 pages, 3 figures

Self-Adaptive Autoreclosing Scheme usingI Artificial Neural Network and Taguchi's Methodology in Extra High Voltage Transmission Systems

Conventional automatic reclosures blindly operate for permanent, semi-permanent or transient faults on an overhead line without any discrimination after allowing some estimated time delay. Reclosing onto a line with uncleared fault often results in, not only loss of stability and synchronism but also damage to system equipments, as a consequence. The thesis focuses on methods to discriminate a temporary fault from a permanent one, and accurately determine fault extinctiontime in an extra high voltage (EHV) transmission line in a bid to develop a self-adaptive automatic reclosing scheme. The fault identification prior to reclosing is based on optimized artificial neural network associated with three training algorithms, namely, Standard Error Back-Propagation, Levenberg Marquardt and Resilient Back-Propagation algorithms. In addition, Taguchi's methodology is employed in optimizing the parameters of each algorithm used for training, and in deciding the number of hidden neurons of the neural network. To get data for training the neural networks, a range of faults are simulated on two case studies -single machine -infinite bus model (connected via EHVtransmission line) and a benchmark IEEE 9-bus electric system. The spectra of the fault voltage data are analyzed using Fast Fourier Transform, and it has been found out that the DC, the fundamental and the first four harmonic components can sufficiently and uniquely represent the condition of each fault. In each case study, the neural network is fed with the normalized energies of the DC, the fundamental and the first four harmonics of the faulted voltages, effectively trained with a set of training data, and verified with a dedicated testing data obtained from fault voltage signals generated on IEEE 14-bus electric system model. The results show the efficacy of the developed adaptive automatic reclosing scheme. This effectively means it is possible to avoid reclosing before any fault on a transmission line (be it temporary or permanent) is totally cleared

Designing pull production control systems:Customization and robustness

In this dissertation we address the issues of selecting and configuring pull production control systems for single-product flowlines. We start with a review of pull systems in the literature, yielding a new classification. Then we propose a novel selection procedure based on a generic system that we test on a case also studied in the literature. We further study our procedure for a variety of twelve production lines. We find new types of pull systems that perform well. Next, we raise the issue of designing pull systems under uncertainty. We propose a novel procedure to minimize the risk of poor performance. Results show that risk considerations strongly influence the selection of a specific pull system

Determination of flexibility of workers working time through Taguchi method approach

Human factor is one of the important elements in manufacturing world, despite their important role in improvement the production flow, they have been neglected while scheduling for many decades. In this paper the researchers taken the human factor throughout their job performance weightage into consideration while using job shop scheduling (JSS) for a factory of glass industry, in order to improving the workers' flexibility. In other hand, the researchers suggested a new sequence of workers' weightage by using Taguchi method, which present the best flexibility that workers can have, while decreasing the total time that the factory need to complete the whole production flow.

RF-MEMS switch actuation pulse optimization using Taguchi's method

Copyright @ 2011 Springer-VerlagReliability and longevity comprise two of the most important concerns when designing micro-electro-mechanical-systems (MEMS) switches. Forcing the switch to perform close to its operating limits underlies a trade-off between response bandwidth and fatigue life due to the impact force of the cantilever touching its corresponding contact point. This paper presents for first time an actuation pulse optimization technique based on Taguchi’s optimization method to optimize the shape of the actuation pulse of an ohmic RF-MEMS switch in order to achieve better control and switching conditions. Simulation results show significant reduction in impact velocity (which results in less than 5 times impact force than nominal step pulse conditions) and settling time maintaining good switching speed for the pull down phase and almost elimination of the high bouncing phenomena during the release phase of the switch

Comparison of Evolutionary Optimization Algorithms for FM-TV Broadcasting Antenna Array Null Filling

Broadcasting antenna array null filling is a very challenging problem for antenna design optimization. This paper compares five antenna design optimization algorithms (Differential Evolution, Particle Swarm, Taguchi, Invasive Weed, Adaptive Invasive Weed) as solutions to the antenna array null filling problem. The algorithms compared are evolutionary algorithms which use mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. The focus of the comparison is given to the algorithm with the best results, nevertheless, it becomes obvious that the algorithm which produces the best fitness (Invasive Weed Optimization) requires very substantial computational resources due to its random search nature

ADAPTIVE SEARCH AND THE PRELIMINARY DESIGN OF GAS TURBINE BLADE COOLING SYSTEMS

This research concerns the integration of Adaptive Search (AS) technique such as the Genetic Algorithms (GA) with knowledge based software to develop a research prototype of an Adaptive Search Manager (ASM). The developed approach allows to utilise both quantitative and qualitative information in engineering design decision making. A Fuzzy Expert System manipulates AS software within the design environment concerning the preliminary design of gas turbine blade cooling systems. Steady state cooling hole geometry models have been developed for the project in collaboration with Rolls Royce plc. The research prototype of ASM uses a hybrid of Adaptive Restricted Tournament Selection (ARTS) and Knowledge Based Hill Climbing (KBHC) to identify multiple "good" design solutions as potential design options. ARTS is a GA technique that is particularly suitable for real world problems having multiple sub-optima. KBHC uses information gathered during the ARTS search as well as information from the designer to perform a deterministic hill climbing. Finally, a local stochastic hill climbing fine tunes the "good" designs. Design solution sensitivity, design variable sensitivities and constraint sensitivities are calculated following Taguchi's methodology, which extracts sensitivity information with a very small number of model evaluations. Each potential design option is then qualitatively evaluated separately for manufacturability, choice of materials and some designer's special preferences using the knowledge of domain experts. In order to guarantee that the qualitative evaluation module can evaluate any design solution from the entire design space with a reasonably small number of rules, a novel knowledge representation technique is developed. The knowledge is first separated in three categories: inter-variable knowledge, intra-variable knowledge and heuristics. Inter-variable knowledge and intra-variable knowledge are then integrated using a concept of compromise. Information about the "good" design solutions is presented to the designer through a designer's interface for decision support.Rolls Royce plc., Bristol (UK

Continuous improvement: A bibliography with indexes, 1989-1991

This bibliography contains 198 annotated references to reports and journal articles entered into the NASA Scientific and Technical Information Data base during 1989 to 1991