Neural Networks Analysis of Steel Plate Processing Augmented by Multi‐objective Genetic Algorithms

An earlier neural network analysis of processing of steel plates through hot rolling was subjected to a further refined analysis through some flexible neural networks that evolved using a multi‐objective predator‐prey genetic algorithm. The original data set expressing the Yield Strength and Ultimate Tensile Strength of the rolled slabs in terms of a total of 108 process variables were subjected to a systematic pruning through this evolutionary approach, till the nitrogen content of the steel emerged as the most significant input variable. A theoretical explanation is provided for this slightly unexpected result.     

A study of the cu clusters using gray-coded genetic algorithms and differential evolution

Energy minimization studies were carried out for a number of Cu clusters using binary and Gray-coded genetic algorithms along with real coded differential evolution, and their optimized ground state geometries are presented. The potential energy function is constructed using a two-body interaction methodology, involving both attractive and repulsive pair-potential terms. The results obtained through the evolutionary algorithms are compared against those obtained earlier using a Monte Carlo technique.     

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.     

A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem

A new data-driven reference vector-guided evolutionary algorithm has been successfully implemented to construct surrogate models for various objectives pertinent to an industrial blast furnace. A total of eight objectives have been modeled using the operational data of the furnace using 12 process variables identified through a principal component analysis and optimized simultaneously. The capability of this algorithm to handle a large number of objectives, which has been lacking earlier, results in a more efficient setting of the operational parameters of the furnace, leading to a precisely optimized hot metal production process.     

Optimization of annealing cycle parameters of dual phase and interstitial free steels by multiobjective genetic algorithms

Data-driven models have been constructed for Dual Phase (DP) and Interstitials Free (IF) steels using an evolutionary approach. DP steel data are utilized from an existing database, while for the IF steels, data generated at an integrated steel plant have been used. The objective function for Ultimate Tensile Strength (UTS) and % elongation, created as data-driven models, is simultaneously optimized for an optimum strength-ductility balance and the results indicate the possibilities of developing steels with better mechanical properties than what are known to have been existing so far.     

Making use of prognostics health management information for aerospace spare components logistics network optimisation

Although research has evolved significantly over the last decade, there are still a large number of Grand Challenges confronting modelling, model deployment, and model-based decision making of large-scale complex Discrete Event Logistics Systems (DELS) to be tackled, as identified and reviewed during a Dagstuhl workshop in March 2010. This paper illustrates how several of these challenges are already being addressed, based on a series of case studies from the Aerospace Spare Components Logistics domain, where consolidated operational Prognostics and Health Management (PHM) information can be used for tactical planning and optimisation of spare components logistics networks. In this setting, the growing potential of PHM technology to facilitate the maintenance and support of commercial and military aircraft emphasises the need for tools to determine the impacts and benefits of a PHM system. To achieve this, the prognostics parameters and related logistics policies were identified, modelled, and subsequently incorporated into a simulation-based decision support framework.     

Surface integrity study of WEDM with various wire electrodes: experiments and analysis

Abstract

Wire electrical discharge machining (WEDM) is always significant for its high-precision machining. However, due to the generation of high discharge energy during machining, machined surfaces are often got distorted. These might be upgraded by choosing the correct tool with proper machining condition. The effects of the electrode materials and process parameters on different responses of WEDM like average surface roughness, recast layer thickness, and surface morphology are systematically examined here to enhance the knowledge of WEDM and its correlation with electrode property. The experiments have been carried out on one of the expensive steel namely Maraging steel 300 due to its applicability in tooling and aerospace industries. Plain brass wire, zinc-coated brass wire (ZCB), and silver-coated brass (SCB) wires are used as a tool electrode for analysis. Comparative experimental studies prove that among BW, ZCB, and SCB, the overall performance of SCB is commendable owing to the high-quality surface considering control parameters in low discharge energy level. However, the second-best performance is shown by ZCB.     

An analytical and experimental approach for pressure distribution analysis of a particular lobe and plain bearing performance keeping in view of all impeding varying parameters associating with fixed lubrication SAE20W40

This paper presents both analytical and experimental pressure analysis approach of a typical Lobe and plain bearing for determining effective performance of the bearing. This is found to be dependent on several variables viz. angular velocity (1200-1900 rpm), load (300-750 N) and pressure angle (0°-180°). This study in particular has been carried out for better rectifications and comparative prediction of lobe and plain bearing in terms of pressure distribution behavior under lubrication oil grade of SAE20W40. Influencing parameters were varied in this set up only to get optimum parametric combination considering all relevant practical issues. The experimentation was done based on significant directives of relevant literatures in these sectors. Attempt was made to compare the analytical findings with experimental results and found matched appreciably. After that attention was diverted to find the nature of pressure and load carrying capacity at various fluctuating speed and load with a fixed lubrication of SAE20W40 for appropriate decision making towards its characteristic performance. The analytical data generated by MATLAB are compared with experimental data which is generated by JBTR.

     

Comparative studies of the effect of moderate and high altitude stress on human subjects: studies on erythrocyte ghost ATPases

Studies have been carried out on erythrocyte membrane‐bound ATPases of ten female subjects at a moderate altitude (2200 m) and at a high altitude (5100 m). Mg²⁺‐ATPase activity of the subjects was found to be significantly stimulated under conditions at a high altitude in comparison to that at a moderate altitude. The changes in total lipids and cholesterol contents of erythrocyte ghosts and plasma Na⁺/K⁺ levels at high altitude in comparison to the moderate altitude indicates an alteration in the membrane permeability under high altitude stress. On acclimatization of the subjects from a high to a moderate altitude, the studied parameters reverted to the values as observed at a moderate altitude.