Simulation of friction welding of alumina and steel with aluminum interlayer

Friction welding is a complicated metallurgical process that is accompanied by frictional heat generation and plastic deformation. Since the thermal cycle of friction welding is very short, simulation becomes very significant. In the present work, a finite element-based numerical model has been developed to understand the thermo-mechanical phenomenon involved in the process of friction welding. The developed model is capable of predicting thermal distribution during friction welding of ceramics with metal using an aluminum interlayer for various time increments. Frictional heating at the interfacial region consumes the aluminum interlayer and establishes a bond between alumina and mild steel. Though there is mechanical mixing, the bond is incomplete in the aluminum-alumina interface. Due to the variation of thermal properties between alumina and mild steel, more amount of thermal stress is induced at the joint interface. Numerical simulation predicts the formation of residual stress in the alumina-mild steel side of the interface. This leads to incomplete interlocking that results in poor joint strength.     

Comparative evaluation of extraction of tetra- and hexa-valent neptunium ions by CMPO in a room temperature ionic liquid

ABSTRACT

Solvent extraction studies were performed to understand the extraction behavior of Np⁴⁺ and NpO₂ ²⁺ from acidic feeds with CMPO (octyl (phenyl)-N,N-diisobutyl carbamoyl methyl phosphine oxide) dissolved in 1-butyl-3-methylimidazolium bis(trifluoromethane sulfonyl)imide, a water immiscible ionic liquid. Slope analyses on the distribution data revealed the extraction of ML₂ type species, where M = Np⁴⁺ or NpO₂ ²⁺, and L = CMPO. Studies were also carried out with Pu⁴⁺ and UO₂ ²⁺ under identical conditions. The nature of the extracted species was found to vary with the nature of the ionic species.     

Improvement of constructive heuristics using variable neighbourhood descent for scheduling a flow shop with sequence dependent setup time

This paper deals with the problem of scheduling a flow shop operating in a sequence dependent setup time environment. The objective is to determine the sequence that minimises the makespan. Two efficient neighbourhood search-based heuristics have been developed and tested using 960 problems, and the results obtained reveal their usefulness. The algorithms make use of two existing constructive heuristics. A neighbourhood search known as variable neighbourhood descent is used to improve the two constructive heuristics. Experimentation is carried out on the 96 groups of problems with 10 problem instances in each group. Performance analysis is carried out using the relative performance improvement of each heuristic. The analysis shows a consistently better performance of the neighbourhood-based improvement heuristics. A paired comparison test is used for validating the superiority of the proposed heuristics. The statistical analysis reveals that the performance of the neighbourhood-based heuristics is very much dependent on the initial constructive heuristics used.     

Self-organizing maps for pattern recognition in design of alloys

A combined experimental–computational methodology for accelerated design of AlNiCo-type permanent magnetic alloys is presented with the objective of simultaneously extremizing several magnetic properties. Chemical concentrations of eight alloying elements were initially generated using a quasi-random number generator so as to achieve a uniform distribution in the design variable space. It was followed by manufacture and experimental evaluation of these alloys using an identical thermo-magnetic protocol. These experimental data were used to develop meta-models capable of directly relating the chemical composition with desired macroscopic properties of the alloys. These properties were simultaneously optimized to predict chemical compositions that result in improvement of properties. These data were further utilized to discover various correlations within the experimental dataset by using several concepts of artificial intelligence. In this work, an unsupervised neural network known as self-organizing maps was used to discover various patterns reported in the literature. These maps were also used to screen the composition of the next set of alloys to be manufactured and tested in the next iterative cycle. Several of these Pareto-optimized predictions out-performed the initial batch of alloys. This approach helps significantly reducing the time and the number of alloys needed in the alloy development process.     

Endometriosis and the neoplastic process

Endometriosis is a frequent disorder that commonly presents with infertility and pelvic pain. Although the precise aetiology of endometriosis is unclear, it is generally considered to involve multiple genetic, environmental, immunological, angiogenic and endocrine processes. Genetic factors have been implicated in endometriosis but the susceptibility genes remain largely unknown. Although endometriosis is a benign disorder, recent studies of endometriosis suggest endometriosis could be viewed as a neoplastic process. Evidence to support this hypothesis includes the increased susceptibility to develop ovarian clear-cell and endometrioid cancers in the presence of endometriosis, and molecular similarities between endometriosis and cancer. In this article we discuss (i) the evidence suggesting that endometriosis might be viewed as a neoplastic process, and (ii) the implications of this hypothesis for elucidating the pathogenesis of endometriosis and developing novel methods of diagnostic classification and individualised treatments.     

A simple decision tree-based disturbance monitoring system for VSC-based HVDC transmission link integrating a DFIG wind farm

Fault detection and classification is a key challenge for the protection of High Voltage DC (HVDC) transmission lines. In this paper, the Teager–Kaiser Energy Operator (TKEO) algorithm associated with a decision tree-based fault classi f ier is proposed to detect and classify various DC faults. The Change Identification Filter is applied to the average and differential current components, to detect the first instant of fault occurrence (above threshold) and register a Change Identified Point (CIP). Further, if a CIP is registered for a positive or negative line, only three samples of currents (i.e., CIP and each side of CIP) are sent to the proposed TKEO algorithm, which produces their respective 8 indices through which the, fault can be detected along with its classification. The new approach enables quicker detection allowing utility grids to be restored as soon as possible. This novel approach also reduces computing complexity and the time required to identify faults with classification. The importance and accuracy of the proposed scheme are also thor oughly tested and compared with other methods for various faults on HVDC transmission lines.     

Partial replacement of carbon black with graphene in natural rubber/butadiene rubber based tire compound: Investigation of critical properties

Journal of Polymer Research - In this study, we prepared graphene oxide-based styrene/acrylic elastomeric disks (GSAE) from sugarcane bagasse/ferrocene (GO) and styrene/acrylic emulsion (SAE) to...     

Cut off frequency and transit time analysis of lightly doped drain (LDD) MOSFETs

Analytical model for the transconductance, cut off frequency, transit time and fringing capacitance of LDD MOSFETs is presented with a simple approach. The analysis is carried out considering the LDD device as a conventional MOSFET with a series resistance [Z.-H. Liu et al., Threshold voltage model for submicrometer MOSFETs. IEEE Trans Electron Devices 1993; ED-40: 86–94] and a simple closed form expressions for cut off frequency and transit time is obtained. The total gate capacitance, i.e. the geometric and fringing capacitance, is calculated for both LDD and non-LDD devices and lower fringing capacitance is reported in LDD devices. Lower cut-off frequencies and higher transit time are reported in LDD devices for the same channel length.     

Generating human-like soccer primitives from human data

Recently, interest in analysis and generation of human and human-like motion has increased in various areas. In robotics, in order to operate a humanoid robot, it is necessary to generate motions that have strictly dynamic consistency. Furthermore, human-like motion for robots will bring advantages such as energy optimization.This paper presents a mechanism to generate two human-like motions, walking and kicking, for a biped robot using a simple model based on observation and analysis of human motion. Our ultimate goal is to establish a design principle of a controller in order to achieve natural human-like motions. The approach presented here rests on the principle that in most biological motor learning scenarios some form of optimization with respect to a physical criterion is taking place. In a similar way, the equations of motion for the humanoid robot systems are formulated in such a way that the resulting optimization problems can be solved reliably and efficiently.The simulation results show that faster and more accurate searching can be achieved to generate an efficient human-like gait. Comparison is made with methods that do not include observation of human gait. The gait has been successfully used to control Robo-Erectus, a soccer-playing humanoid robot, which is one of the foremost leading soccer-playing humanoid robots in the RoboCup Humanoid League.