Mechanical characteristics of resistance multispot welded joints

Abstract

In the present study, three-dimensional thermal elastoplastic analysis has been carried out in order to clarify the mechanical phenomena of the thermal elastoplastic behaviour of multispot welded joints. As the shape of multispot welded joints is not axisymmetrical, unlike single spot welded joints, the solution domain for simulation should be three-dimensional. Therefore, the present study first developed three-dimensional heat conduction and thermal elastoplastic programs using the isoparametric finite element method. Second, from the results analysed by the developed programs, thermal and mechanical characteristics and their production mechanisms on single and multispot welded joints were clarified. Moreover, effects of pitch length on the temperature distribution, welding residual stresses and plastic strain of multispot welded joints were evaluated, indicating that a pitch of 30 mm was more advantageous compared to a pitch of 15 mm.     

Development of efficient numerical heat transfer model coupled with genetic algorithm based optimisation for prediction of process variables in GTA spot welding

Abstract

Although numerical heat transfer models based on conduction mode of heat transfer have become a strong basis for the quantitative analysis of fusion welding, they still find limited use in actual design for three primary reasons. First, these traditional models consider a volumetric heat source term, which ironically requires a-priori knowledge of the final weld pool dimensions. Second, the numerical models need confident values of a few parameters, e.g. arc efficiency and arc radius, which are usually uncertain and requires many trial and error simulations to realise their suitable values. Third, these models are rarely attempted for the prediction of possible weld conditions for a requisite or target weld dimensions, which is of paramount interest in design for welding. The present work attempts to circumvent these issues by linking a genetic algorithm (GA) based global optimisation scheme with a finite element based three-dimensional numerical heat transfer model. The numerical model includes a volumetric heat source that adapts itself to the computed weld pool geometry at any instant. The GA module identifies the optimum values of a set of uncertain parameters needed for the reliable modelling calculations and next, identifies the suitable values of the process variables, e.g. weld current, for a target weld dimension. In each case, the GA module guides the numerical model to compute weld dimensions for a given set of inputs, traces the sensitivity of the error in prediction on the inputs being optimised, updates them accordingly and reuses the numerical model to finally obtain their optimised values. The complete integrated model is validated with a number of experimental results in gas tungsten arc spot welding processes.     

Using Data Flow Information to Obtain Efficient Check Sets for Algorithm-Based Fault Tolerance

Algorithm-Based Fault Tolerance (ABFT) is a well known technique for achieving fault and error detection in multiprocessor systems. We examine several issues concerning ABFT systems when the data flow information for the underlying multiprocessor computation is available. Our results show that this finergrained information can be exploited to obtain test schemes involving fewer checks, in some cases, dramatically fewer checks. We address both the analysis and design of ABFT systems when the data flow information is available. The analysis problem for a given ABFT system is to determine the fault detectability and the fault locatability (maximum number of detectable and locatable faulty processors) of the system. We show that the analysis problem can be solved efficiently when the number of faults is fixed. We also address the computational difficulty of this problem when the number of faults is not fixed. The design problem is concerned with the construction of a minimal collection of checks which can detect or locate a specified number of faults for a given multiprocessor computation. We examine some special classes of data flow graphs and establish upper and lower bounds on the number of checks needed to detect or locate a given number of faults. We also address the computational difficulty of this design problem for several cases.     

Mise en évidence par émission ionique secondaire du rôle de l'oxygène dans quelques mécanismes intergranulaires

Abstract

With the ionic microanalyser it is possible to obtain, with a resolution better than a micron, an image of the distribution of elements present in a metallic matrix.

The contrast on the image results primarily from the local difference in concentration; however, other effects related to the variation in the efficiency of the pulverisation with orientation as well as to the modification of the electronic environment can be observed. Local variations in contrast related to the nature of the chemical bonds can also be observed. In this latter case, oxygen plays an important role, so that an element in a homogeneous solid solution, even in slight concentrations, can appear reinforced at the sites at which it is bond with oxygen.

The study of the contrast obtained provided new experimental data on the mechanisms of the grain boundary diffusion of oxygen during the low temperature oxidation of zironium and during the internal oxidation of Cu-Be.

Résumé

A l'aide du microanalyseur ionique il est possible d'obtenir - avec une résolution meilleure que le micron - une image de la répartition des éléments présents dans une matrice métallique.

Le contraste observé sur l'image provient en premier lieu de la différence locale de concentration, toutefois d'autres effets liés à la variation du rendement de pulvérisation avec l'orientation ainsi qu'à la modification de l'environnement électronique peuvent être observés. De plus, on peut remarquer localement des variations de contraste induites par la nature des liaisons chimiques. Dans ce cas précis, l'oxygènejoue un rôle fondamental: Ainsi un élément en solution solide homogène - même en faible quantité - peut paraître renforcé aux endroits où il se trouve lié à l'oxygène.

L'étude par densitométrie des images obtenues nous a pennis d'apporter des données expérimentales nouvelles sur les mécanismes de diffusion intergranulaire de l'oxygène, soit lors de l'oxydation à basse température du zirconium, ou lors de l'oxydation interne du Cu-Be.     

Model based optimisation of friction stir welding processes

Abstract

This paper proposes a model based approach for the optimisation of friction stir welding processes. The proposed approach starts with building a model of the process. For this study, the thermal model developed by Chao and his associate for friction stir welding of AA 2195-T8 is replicated using Fluent. Once developed, the thermal model is then used to simulate the process. Two surrogate models, one linear and one non-linear, are constructed to relate three process parameters with maximum temperature at a selected location, using the simulation data generated by the thermal model. A constrained optimisation model is next formulated, which is eventually solved by five population based metaheuristrics to find the optimal solutions for the studied friction stir welding process. The optimal solutions are primarily constrained by the lower bound of the temperature. The lower this lower bound temperature is the higher travel speed can go. The linear surrogate model results in a slightly better optimal solution than the non-linear model when the temperature constraint is loose and the converse is true when the temperature constraint is tight. Comparing all five metaheuristics, differential evolution has the best performance, followed by particle swarm optimisation, ant colony optimisation, genetic algorithm, and lastly harmony search.     

Improvement and optimisation of hot dip galvanising line using neural networks and genetic algorithms

Abstract

In the present article, an application is present based on the combination of genetic algorithms and neural networks, used to improve the annealing process of a hot dip galvanising line with steel coils. The main objective is to determine the best settings for a furnace in order to reduce the margin of error between the actual strip temperature and expected temperature, not only for each coil that forms the strip, but also in the zones of the strip where transitions are formed by coils with different dimensions or steel types. Basically, the methodology consists in training a multilayer perceptron (MLP), which then determines the settings of the furnace and the speed of the strip according to the type of coil that forms the same strip. Another MLP is used to predict the dynamic behaviour of the strip related to its fluctuations in speed, as well as the temperature of the furnace. In this way, using simulations and genetic algorithms, the optimum settings of the furnace are determined, as well as the speed of the strip in those zones where there are changes in the coils, namely, in dimensions and types of steel.     

Estimation of rate parameters for reduction of iron ore–graphite composite pellets in packed bed reactor using genetic algorithm

Abstract

A semiempirical kinetic model has been developed to determine the course of reduction of iron ore–graphite composite pellets over time in a laboratory scale side heated packed bed reactor attached with a tailor made bottom hanging thermogravimetric set-up. The rate parameters in the model, especially the three sets of apparent activation energy values and frequency factors associated with the reduction of iron oxides in three elementary steps, namely hematite to magnetite, magnetite to wustite and wustite to iron, have been estimated based on experimental data by employing an optimisation tool, the genetic algorithm (GA). The difference between the predicted and experimental degree of reduction is minimised to obtain the rate parameters. The experimental degree of reduction is calculated based on mass loss data during reduction and the exit gas analysis. Estimated values of apparent rate parameters were found to be of the same order of magnitude to their intrinsic counterparts reported in literature. Finally, by using the predicted rate parameters the temporal evolution of various oxide phases as well as pure iron has been evaluated.     

AERODYNAMIC OPTIMIZATION FOR TURBINE BLADE BASED ON HIERARCHICAL FAIR COMPETITION GENETIC ALGORITHMS WITH DYNAMIC NICHE

A global optimization approach to turbine blade design based on hierarchical fair competition genetic algorithms with dynamic niche (HFCDN-GAs) coupled with Reynolds-averaged Navier-Stokes (RANS) equation is presented. In order to meet the search theory of GAs and the aerodynamic performances of turbine, Bezier curve is adopted to parameterize the turbine blade profile, and a fitness function pertaining to optimization is designed. The design variables are the control points' ordinates of characteristic polygon of Bezier curve representing the turbine blade profile. The object function is the maximum lift-drag ratio of the turbine blade. The constraint conditions take into account the leading and trailing edge metal angle, and the strength and aerodynamic performances of turbine blade. And the treatment method of the constraint conditions is the flexible penalty function. The convergence history of test function indicates that HFCDN-GAs can locate the global optimum within a few search steps and have high robustness. The lift-drag ratio of the optimized blade is 8.3% higher than that of the original one. The results show that the proposed global optimization approach is effective for turbine blade.     

Genetic Programming-Based Automatic Gait Generation in Joint Space for a Quadruped Robot

This paper introduces a new approach to developing a fast gait for a quadruped robot using genetic programming (GP). Planning gaits for legged robots is a challenging task that requires optimizing parameters in a highly irregular and multi-dimensional space. Several recent approaches have focused on using genetic algorithms (GAs) to generate gaits automatically and have shown significant improvement over previous gait optimization results. Most current GA-based approaches optimize only a small, pre-selected set of parameters, but it is difficult to decide which parameters should be included in the optimization to get the best results. Moreover, the number of pre-selected parameters is at least 10, so it can be relatively difficult to optimize them, given their high degree of interdependence. To overcome these problems of the typical GA-based approach, we have proposed a seemingly more efficient approach that optimizes joint trajectories instead of locus-related parameters in Cartesian space, using GP. Our GP-based method has obtained much-improved results over the GA-based approaches tested in experiments on the Sony AIBO ERS-7 in the Webots environment. The elite archive mechanism is introduced to combat the premature convergence problems in GP and has shown better results than a traditional multi-population approach.     

Sample Processing with Hydrophobic Microfluidics

Hydrophobic microfluidics is a method for controlling fluid flow in microfluidic systems using short restrictions in channel diameter that act as passive valves. Systems designed using hydrophobic microfluidics have the advantage of easily interfacing with external hardware and integrating with external analysis equipment. This allows it to take advantage of both the micro and macro realms, whichever is most suited for the application, as well as allowing for an inexpensive integration of microfluidics into a company's sample analysis protocols. This method of fluid control is excellent for highly parallel sample analysis, such as DNA processing.