A service oriented architecture for decision making in engineering design

Decision making in engineering design can be effectively addressed by using genetic algorithms to solve multi-objective problems. These multi-objective genetic algorithms (MOGAs) are well suited to implementation in a Service Oriented Architecture. Often the evaluation process of the MOGA is compute-intensive due to the use of a complex computer model to represent the real-world system. The emerging paradigm of Grid Computing offers a potential solution to the compute-intensive nature of this objective function evaluation, by allowing access to large amounts of compute resources in a distributed manner. This paper presents a grid-enabled framework for multi-objective optimisation using genetic algorithms (MOGA-G) to aid decision making in engineering design

High-power 1H composite pulse decoupling provides artifact free exchange-mediated saturation transfer (EST) experiments.

Exchange-mediated saturation transfer (EST) provides critical information regarding dynamics of molecules. In typical applications EST is studied by either scanning a wide range of (15)N chemical shift offsets where the applied (15)N irradiation field strength is on the order of hundreds of Hertz or, scanning a narrow range of (15)N chemical shift offsets where the applied (15)N irradiation field-strength is on the order of tens of Hertz during the EST period. The (1)H decoupling during the EST delay is critical as incomplete decoupling causes broadening of the EST profile, which could possibly result in inaccuracies of the extracted kinetic parameters and transverse relaxation rates. Currently two different (1)H decoupling schemes have been employed, intermittently applied 180° pulses and composite-pulse-decoupling (CPD), for situations where a wide range, or narrow range of (15)N chemical shift offsets are scanned, respectively. We show that high-power CPD provides artifact free EST experiments, which can be universally implemented regardless of the offset range or irradiation field-strengths

Modelling of direct metal laser sintering of EOS DM20 bronze using neural networks and genetic algorithms

An attempt was made to predict the density and microhardness of a component produced by Laser Sintering of EOS DM20 Bronze material for a given set of process parameters. Neural networks were used for process-based-modelling, and results compared with a Taguchi analysis. Samples were produced using a powder-bed type ALM (Additive Layer Manufacturing)-system, with laser power, scan speed and hatch distance as the input parameters, with values equally spaced according to a factorial design of experiments. Optical Microscopy was used to measure cross-sectional porosity of samples; Micro-indentation to measure the corresponding Vickers’ hardness. Two different designs of neural networks were used - Counter Propagation (CPNN) and Feed-Forward Back-Propagation (BPNN) and their prediction capabilities were compared. For BPNN network, a Genetic Algorithm (GA) was later applied to enhance the prediction accuracy by altering its topology. Using neural network toolbox in MATLAB, BPNN was trained using 12 training algorithms. The most effective MATLAB training algorithm and the effect of GA-based optimization on the prediction capability of neural networks were both identified

Fuzzy-genetic algorithms and mobile robot navigation among static obstacles

The paper describes a fuzzy genetic algorithm in which a fuzzy logic controller (FLC) is used with genetic algorithms (GAs) to find obstacle-free paths in a number of find-path problems of a mobile robot. In this algorithm, an obstacle-free direction for the movement of a robot locally is created using an FLC and the extent of travel along obstacle-free direction is determined by a GA. Here, the fuzzy logic approach is used to create initial population and GA crossover and mutation operators. This algorithm is found to perform better than the popular steepest descent approach. The proposed algorithm also finds solutions close to the best known tangent graph with A algorithm from the accuracy point of view. However, the proposed algorithm finds a near-optimal solution faster than the tangent graph and A algorithm. Moreover, the proposed approach shows how genetic operators can be modified with problem-specific information to create a search algorithm which is efficient for the particular application

High-power relaxation dispersion NMR data set at different ligand concentrations: a litmus test for classification of recognition mechanism

Publication associated with this dataset: Chakrabarti, K.S., Olsson, S., Pratihar, S. et al. A litmus test for classifying recognition mechanisms of transiently binding proteins. Nat Commun 13, 3792 (2022). https://doi.org/10.1038/s41467-022-31374-

Modelling of weld-bead geometry and hardness profile in laser welding of plain carbon steel using neural networks and genetic algorithms

An attempt was made to predict weld-bead geometry and its cross-sectional micro-hardness profile produced by laser welding of plain carbon steel (DC05) for a given set of process parameters. Welding was done using ytterbium fibre laser by considering laser power, weld speed and distance of the focal point from the sample surface as the input parameters. Microscopy was used to measure the weld dimensions. Micro-indentation was made to measure the corresponding Vickers’ hardness along the horizontal cross section. Two different models were developed. The first model had mean hardness and weld-bead geometry represented by four geometrical dimensions of the weld (that is, top width, depth, mid-width and heat-affected-zone width at mid-depth) as the modelling outputs. The second model had the hardness profile plot interpolation parameters as the modelling outputs. Two different designs of neural networks were used for process-based modelling, namely counter-propagation neural network (CPNN) and feed-forward back-propagation neural network (BPNN), and their prediction capabilities were compared. For the feed-forward neural network, a genetic algorithm was later applied to enhance the prediction accuracy by altering its topology. Back-propagation was implemented using 12 different training algorithms. Mean generalisation error was used to compare the modelling accuracy of the neural networks

Influence of yarn structure, sizing ingredients and type of sizing on properties and performance of sized yarns : Part III<b> </b>- A study of attrition during weaving for air-jet, ring and rotor yarns on a modern high speed weaving machine

149-155How best the yarns of a given spinning system can be sized with optimum cost to overcome attritive forces acting on warp yarns during weaving has been studied. Polyester/viscose (70:30) yarns (30s) obtained from the three spinning systems, viz. air-jet, ring and rotor, were sized with cold brand PVA and tested for abrasion and tensile properties. Using these yarns in warp, a plain weave fabric was woven on a Dornier Rapier loom. The yarns were then unravelled from the fabric and tested for the abrasion and tensile properties. It is observed that among the ring, rotor and air-jet yarns, the excellent abrasion resistance is obtained with air-jet yarn at higher size concentration and slow rate of drying. This is because at higher concentration there is good size film formation on the yarn surface and at slow rate of drying more outward size migration takes place, resulting in better size encapsulation. An air-jet yarn whose strength properties are derived from the tightness and compression of the surface wrappings is, therefore, protected from the abrasion forces, caused during weaving, by this size film for a longer duration

Influence of yarn structure, sizing ingredients and type of sizing on properties and performance of sized yarns: Part II-A comparative study of sized yarn performance<span style="font-size:14.0pt;font-family: HiddenHorzOCR;mso-hansi-font-family:"Times New Roman";mso-bidi-font-family: HiddenHorzOCR;color:#292929"> for ring- and rotor-spun cotton yarns </span>

142-148A comparative study on influence of structural differences of ring- and rotor-spun cotton yarns on the process of sizing and the performance of sized yarns against abrasion has been carried out using the 6s and 16s ring and rotor yarns sized with cold and hot brand PVA at different concentrations (9%, 7% and 5%) and drying rates (70°C and 140°C). It is observed that, in general, the abrasion resistance of rotor yarns is better than that of ring yarns. The rotor yarns give better abrasion resistance at 140°C because the higher temperature reduces size migration from inside to surface of the yarn, thereby ensuring more uniform deposition of size along the cross-section of the yarn. A better size distribution reinforces the open structure of rotor yarns and improves cohesion

Influence of yarn structure, sizing ingredients and type of sizing on properties and performance of sized yarns : Part I— Evaluation of sizing process using Zweigle G551 weavability tester

59-64The average and minimum number of abrasion strokes to failure, rate of deterioration in tensile properties when subjected to simultaneous flexion and abrasion, and the effect of cyclic extension on sized yarn strength at different size concentrations have been studied. It is observed that the number of abrasion strokes till break is higher in case of grey ring yarn than that in case of the yams sized with thin boiling starch (TBS) because the fluff generated on the surface of the grey yarn acts as a protective shield. The gain in strength after sizing is greater for rotor yarns. However, the loss in strength after a fixed number of strokes is less for rotor yarns than that for ring yarns. Though the strength of the yarns sized with TBS and PVA (both hot and cold brand) are not significantly different, the loss in strength after a fixed number of abrasion strokes is least for the cold brand PVA followed by the hot brand PVA and TBS