Laser pressure welding of Al alloy and low C steel

Abstract

This study was performed to obtain fundamental knowledge concerning the development of laser pressure welding technology for the joining of dissimilar metals. Laser pressure welding of Al alloy A6061 and low C steel SPCC sheets was carried out to investigate the effects of the roller pressure, laser beam scanning speed and irradiation position on the tensile shear and peel strength of welded joints. The interfaces of the joints were observed and analysed by SEM and EDX, and the formation phases on the peeled surfaces were identified with XRD. It was revealed that prevention and suppression of oxidation during welding was extremely important to the production of a sound joint with good mechanical properties. The highest tensile strength and the highest peel strength of joints were obtained at a laser power of 1·8 kW, laser scanning speed of 30 Hz, laser irradiation position at the centreline, roller pressure of more than 245 MPa and welding speed of 0·5 m min^?1 in an Ar atmosphere. The fracture occurred not in the welded zone but in the A6061 base alloy specimen.     

Neural network model of heat and fluid flow in gas metal arc fillet welding based on genetic algorithm and conjugate gradient optimisation

Abstract

Although numerical calculations of heat transfer and fluid flow can provide detailed insights into welding processes and welded materials, these calculations are complex and unsuitable in situations where rapid calculations are needed. A recourse is to train and validate a neural network, using results from a well tested heat and fluid flow model to significantly expedite calculations and ensure that the computed results conform to the basic laws of conservation of mass, momentum and energy. Seven feedforward neural networks were developed for gas metal arc (GMA) fillet welding, one each for predicting penetration, leg length, throat, weld pool length, cooling time between 800°C and 500°C, maximum velocity and peak temperature in the weld pool. Each model considered 22 inputs that included all the welding variables, such as current, voltage, welding speed, wire radius, wire feed rate, arc efficiency, arc radius, power distribution, and material properties such as thermal conductivity, specific heat and temperature coefficient of surface tension. The weights in the neural network models were calculated using the conjugate gradient (CG) method and by a hybrid optimisation scheme involving the CG method and a genetic algorithm (GA). The neural network produced by the hybrid optimisation model produced better results than the networks based on the CG method with various sets of randomised initial weights. The CG method alone was unable to find the best optimal weights for achieving low errors. The hybrid optimisation scheme helped in finding optimal weights through a global search, as evidenced by good agreement between all the outputs from the neural networks and the corresponding results from the heat and fluid flow model.     

Preliminary studies on friction welding of sintered P/M steel preforms to wrought mild steel

Abstract

Continuous drive friction welding studies on sintered powder metallurgical (P/M) steel preforms–wrought mild steel combination are reported in the present study. The work is a preliminary study to optimise the friction welding parameters and data generated by the present work is expected to contribute to friction welding of dissimilar and similar sintered P/M preforms to wrought metals or sintered P/M preforms – a planned future research work. Sound welds were obtained with all welding parameter combinations studied. The mechanical properties of welds were comparable to those of sintered P/M steel. Sintered P/M preforms deformed to a greater extent than wrought mild steel due to their low flow stress and thermal conductivity. The sintered density and other properties of the P/M preforms were found to dictate the deformation at the interface and consequently the weld strength. The results indicate that the current approach can be extended to other combinations of sintered P/M preforms.     

Artificial neural networks and acoustic emission applied to stability analysis in gas metal arc welding

Abstract

The present paper describes the application of neural networks to obtain a model for estimating the stability of gas metal arc welding (GMAW) process. A neural network has been developed to obtain and model the relationships between the acoustic emission (AE) signal parameters and the stability of GMAW process. Statistical and temporal parameters of AE signals have been used as input of the neural networks; a multilayer feedforward neural network has been used, trained with back propagation method, and using Levenberg Marquardt's algorithm for different network architectures. Different welding conditions have been studied to analyse the incidence of the parameters of the process in acoustic signals. The AE signals have been processed by using the wavelet transform, and have been characterised statistically. Experimental results are provided to illustrate the proposed approach. Finally a statistical analysis for the validation of the experimental results obtained is presented. As a main result of the study, the effectiveness of the application of the artificial neural networks for modelling stability analysis in welding processes has been demonstrated. The regression analysis demonstrates the validity of neural networks to predict the stability of welding process using the statistical characterisation of the signal parameters of AE that have been calculated.     

Method for welding highly crack susceptible magnesium alloy ZK60

Abstract

The highly crack susceptible magnesium alloy ZK60 plates of 2 mm thickness were successfully welded by laser beam welding (LBW) with filler strip, which has the advantages of low heat input and capability of adjusting the compositions of weld metal to a less susceptible level. The effects of the compositions of filler strips on the microstructures and mechanical properties of the joints were investigated. Compared with autogenous LBW, LBW with filler strip can produce a narrower joint and avoid the cracks and pits, which severely worsen mechanical properties of the joints. When the filler strip of ZK40 alloy is employed, the grains in fusion zone can be refined, and a high quality joint, with the ultimate tensile strength of 322 MPa up to 90·7% of the base metal, is obtained.     

Thermophysical properties of liquid Co–Cu–Ni alloys

Abstract

Within the Coolcop project, funded by ESA, the thermophysical properties of selected liquid Cu–Co–Ni alloys were investigated. Using the new high-temperature oscillating cup viscometer at DLR, the viscosity of this alloy along two perpendicular cuts in the ternary phase diagram was measured as a function of temperature. Along the same cuts also surface tension and density were determined by using electromagnetic levitation and image processing.     

New approach to determination of maximum temperatures in welding using travelling heat sources

Abstract

The present paper considers methods of calculation of maximum temperatures in welding using a constant welding speed and a point heat energy source. A comparison of theoretically obtained times for the occurrence of maximum temperatures with those obtained experimentally shows slight deviations. The latter are dependent only on the accuracy of temperature measurements and the workpiece size (i.e. plate size in the present work). In the present case, the limitations refer only to the workpiece size. The advantage of this new approach is in the determination of a plane curve which is an envelope of all the points in the workpiece that are simultaneously at the maximum temperature in arc welding.     

Prediction of weld quality in pulsed current GMAW process using artificial neural network

Abstract

Weld joint dimensions and weld metal mechanical properties are important quality characteristics of any welded joint. The success of building these characteristics in any welding situation depends on proper selection-cum-optimisation of welding process parameters. Such optimisation is critical in the pulsed current gas metal arc welding process (GMAW-P), as the heat input here is closely dictated by a host of additional pulse parameters in comparison to the conventional gas metal arc welding process. Neural network based models are excellent alternatives in such situations where a large number of input conditions govern certain outputs in a manner that is often difficult to adjudge a priori. Six individual prediction models developed using neural network methodology are presented here to estimate ultimate tensile strength, elongation, impact toughness, weld bead width, weld reinforcement height and penetration of the final weld joint as a function of four pulse parameters, e.g. peak current, base current, pulse on time and pulse frequency. The experimental data employed here are for GMAW-P welding of extruded sections of high strength Al–Zn–Mg alloy (7005). In each case, a committee of different possible network architectures is used, including the final optimum network, to assess the uncertainty in estimation. The neural network models developed here could estimate all the outputs except penetration fairly accurately.     

Investigation of seal effects according to axial compression variation of metal seals for transport and storage casks

Abstract

Casks for the transport and storage of heat generating radioactive waste in Germany are normally provided with screwed lid systems, which are in most cases equipped with double jacket metal seals with an inner spring wire to provide long term resistance to the seal compression force. Preservation of the high sealing quality of those seals under operational and accidental stress conditions is essentially important to the safety of those casks. Relative displacements of the lid system surfaces caused by specific impact scenarios cannot be excluded and have to be evaluated with respect to a possible increase in the leakage rate.

To get representative data for such metal sealed lid systems, BAM has developed a special conceptualised flange system placed in an appropriate testing machine for relevant mechanical loading of the metal seals under static and cyclic conditions. Furthermore, the flange system enables continuous measurement of the standard helium leakage rate during each test.

The primary aim of the investigation is to identify the correlation between variation of installation conditions (axial displacements) caused by external loads and the standard helium leakage rate. An essential parameter in this case is the useable resilience r_u of a metal seal under relevant stress conditions. The useable resilience r_u is the vertical difference in the cross-section between the seal's assembling status and the point where the leakage rate, by means of external load relieving, exceeds the quality criterion of 10^–8 Pa m³ s^–1. Load relieving can instantly occur due to modification of the seal groove dimension caused by accident impacts and deformation of the lid system. Furthermore, component specific basis data for the development of finite element calculation models should be collected. In the tests, seals are subjected to static and cyclic loads. All tests are performed at ambient temperature.

This paper presents the test configuration, different test series and results of the current experiments. Typical load–displacement–leakage rate correlations are presented and discussed.     

Use of Alcan's Al-B4C metal matrix composites as neutron absorber material in TN International's transportation and storage casks

Abstract

Major issues in the area of transportation and/or storage of radioactive materials are reliability and safety of engineering components. Among the functions to be undertaken, transportation and storage systems shall allow the criticality control of the transported matter, the control of its temperature, as well as the capacity to withstand the mechanical stresses due to normal, incidental and accidental conditions of use. In most cases, criticality control requires the use of an internal arrangement made of a neutron absorber material, which must also have high thermal conductivity properties to ensure the temperature control. When, as in many AREVA-TN International applications, the design takes credit of the neutron absorber material as a structural component, it must show high mechanical performance. Alcan's Al-B₄C metal matrix composites (Al-B₄C MMCs) meet all the above mentioned requirements, due to their special capability of capturing neutrons, their light weight, and their superior thermal conductivity and mechanical properties. The significant advantage of Alcan's technology is its flexibility with regards to a wide range of boron carbide contents and matrix alloys (from AA1XXX to AA6XXX). This enables the adjustment of the properties to the exact needs of the design. TN International presently uses extruded and/or rolled Al-B₄C MMC parts in several of its internal arrangements. The present paper gives an overview of the manufacture processes of Alcan's Al-B₄C MMCs, from the mixing of B₄C into liquid aluminium to the extrusion and rolling operations. It describes the methods and results for the qualification tests in terms of the neutron absorption, thermal, physical and mechanical properties of the material. Finally, details are given on the use of Alcan's MMCs as a neutron absorber with enough credit for structural material in TN International's TN24 designs.