Effect of corrosion and surface finishing on fatigue behavior of friction stir welded EN AW-5754 aluminum alloy using various tool configurations. - In: Materials, ISSN 1996-1944, Bd. 13 (2020), 14, 3121, insges. 20 S.
https://doi.org/10.3390/ma13143121
Herstellung individueller Strukturen aus silikatischen Werkstoffen mittels Wire-Laser Additive Manufacturing. - In: Glasbau ..., (2020), S. 287-298
Wire arc additive manufacturing (WAAM) of aluminium alloy AlMg5Mn with energy-reduced gas metal arc welding (GMAW). - In: Materials, ISSN 1996-1944, Bd. 13 (2020), 12, 2671, insges. 22 S.
https://doi.org/10.3390/ma13122671
M07-256 : Additive manufacturing of large, temperature-controlled injection molding tools using arc welding and diffusion bonding. - In: SPE ANTEC 2018, (2020), S. 275-281
Reduction of energy input in wire arc additive manufacturing (WAAM) with gas metal arc welding (GMAW). - In: Materials, ISSN 1996-1944, Bd. 13 (2020), 11, 2491, insges. 18 S.
https://doi.org/10.3390/ma13112491
Influence of hydrogen, oxygen, nitrogen, and water vapor on the formation of pores at welding of copper using laser light at 515 nm wavelength. - In: Journal of laser applications, ISSN 1938-1387, Bd. 32 (2020), 2, 022020, S. 022020-1-022020-7
https://doi.org/10.2351/7.0000063
Challenges of temperature measurement during the friction stir welding process. - In: Measuring Equipment and Metrology, ISSN 2617-846X, Bd. 81 (2020), 1, insges. 8 S.
The exact determination of the process zone temperature can be considered as an increasingly important role in the control and monitoring of the friction stir welding process (FSW). At present, temperature measurement is carried out with the aid of a temperature sensor integrated into the tool (usually thermocouples). Since these cannot be attached directly to the joining area, heat dissipation within the tool and to the environment cause measurement deviations as well as a time delay in the temperature measurement. The article describes a process and the challenges that arise in this process, how a direct temperature measurement during the process can be achieved by exploiting the thermoelectric effect between tool and workpiece, without changing the tool by introducing additional temperature sensors.
https://doi.org/10.23939/istcmtm2020.01.034
An experimental study on lap joining of multiple sheets of aluminium alloy (AA 5754) using friction stir spot welding. - In: The international journal of advanced manufacturing technology, ISSN 1433-3015, Bd. 107 (2020), 7/8, S. 3093-3107
Friction stir spot welding (FSSW) process is widely used in the automotive industry for a range of applications such as battery components, standard wire connectors and terminals. This manuscript addresses two grand challenges in the arena of FSSW, hitherto, unaddressed in the extant literature: (i) lap joining of thin sheets (0.3 mm thickness) of AA 5754 alloy and (ii) lap joining of more than two sheets using FSSW. To accomplish this task, a novel pinless convex-shaped tool was designed to alter the stress state while gradually advancing the tool which led to achieving stress state necessary for obtaining defect-free lap joints. The weld joints were inspected by optical microscopy, SEM imaging and analysed by nanoindentation tests and Vickers microindentation tests for assessment of the quality of the weld interface (WI). Process parameters of FSSW such as torque on the tool and axially applied load were used to analytically obtain the average local measure of peak normal and axial stresses as well as the coefficient of friction in the contact zone. In samples welded at low rotational speeds, the strain-hardening mechanism was seen dominating in contrast to samples welded at higher rotational speeds, which showed thermal softening. As a direct consequence of this, the samples welded at low rotational speeds showed much higher hardness at the weld surface than the samples welded at higher speeds. A strong transition of strain hardening to thermal softening was noticeable beyond an applied strain rate of 400 s^-1.
https://doi.org/10.1007/s00170-020-05214-z
Development of a highly productive GMAW hot wire process using a two-dimensional arc deflection. - In: Welding in the world, ISSN 1878-6669, Bd. 64 (2020), 5, S. 873-883
Gas metal arc welding (GMAW) processes are used in a wide range of applications due to their high productivity and flexibility. Nevertheless, the supplied melting wire electrode leads to a coupling of material and heat input. Therefore, an increase of the melting rate correlates with an increase of the heat input by the arc at the same time. A possibility to separate material and heat input is to use an additional wire, which reduces penetration and worsens the wetting behaviour. Consequently, bead irregularities such as bonding defects or insufficient root weldings can occur. In the context of this article, a controlling system for a two-dimensional magnetic arc deflection is presented, which allows to influence arc position as well as material transfer. The analysed GMAW hot wire process is characterised by high melting rates while also realising a sufficient penetration depth and wetting behaviour.
https://doi.org/10.1007/s40194-020-00880-9
In-situ monitoring of hybrid friction diffusion bonded EN AW 1050/EN CW 004A lap joints using artificial neural nets. - In: Proceedings of the Institution of Mechanical Engineers, ISSN 2041-3076, Bd. 234 (2020), 5, S. 766-785
In this work, a dissimilar copper/aluminum lap joint was generated by force-controlled hybrid friction diffusion bonding setup (HFDB). During the welding process, the appearing torque, the welding force as well as the plunge depth are recorded over time. Due to the force-controlled process, tool wear and the use of different materials, the resulting data series varies significantly, which makes quality assurance according to classical methods very difficult. Therefore, a Convolutional Neural Network was developed which allows the evaluation of the recorded process data. In this study, data from sound welds as well as data from samples with weld defects were considered. In addition to the different welding qualities, deviations from the ideal conditions due to tool wear and the use of different alloys were also considered. The validity of the developed approach is determined by cross validation during the training process and different amounts of training data. With an accuracy of 88.5%, the approach of using Convolutional Neural Network has proven to be a suitable tool for monitoring the processes.
https://doi.org/10.1177/1464420720912773