5th Hybrid Materials and Structures 2022 - International Conference on Hybrid Materials
Vortrag
21.07.2022 (CEST)
Friction stir spot welding of aluminum-copper lap joints: Challenges and future perspectives
MG

Michael Grätzel (M.Sc.)

Technische Universität Ilmenau

Kranz, M. (Speaker)¹; Bergmann, J.P.¹; Grätzel, M.¹; Pöthig, P.¹
¹Technical University of Ilmenau
Vorschau
25 Min. Untertitel (CC)

Friction stir spot welding (FSSW) is a formidable technique for joining dissimilar materials such as aluminum and copper and is of great interest in engineering and design applications. Due to the fact, that there is only partly melting of the materials during the joining process , phenomena as they occur in fusion welding such as solidification and liquation cracking, porosity, and loss of volatile solutes can be avoided. These advantages of solid-state joining have been recognized by the industry due to the increasing demands for welding techniques in terms of battery applications and the electrification of the automotive industry. Because of this, the FSSW is a promising technology in welding of aluminum or aluminum to copper and is utilized by a variety of industries, e.g. automotive industry.
Nevertheless, there are still multiple challenges during FSSW of thin sheet applications, as the process control and the influence of tool wear. In particular, the tool wear is an influencing factor and can significantly reduce the quality of the weld.
Thus, the influence of FSSW tool wear was investigated regarding the weld seam quality and process properties. The investigations were carried out with a force-controlled friction welding setup on 1.5 mm EN AW 1050 sheet and 1 mm CW004A sheet in lap joint formation. The resulting weld quality was characterized by different non-destructive and destructive methods e.g. tensile testing, optical inspections and in-situ temperature measurements. Furthermore, welding data was recorded during the process and subsequently evaluated with regard to tool wear. The data have been analyzed with methods of artificial intelligence regarding its suitability for predictive detection of process irregularities due to tool wear.

Abstract

Abstract

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