Combination of Thermal and Mechanical Strategies to Compensate for Distortion Effects during Profile Grinding

This paper describes the investigations and the results of an analysis of distortion compensation processes for profile grinding. Steel workpieces often change their residual stress state due to machining in a seemingly uncontrolled matter. Furthermore, in research as well as in the industry, the accurate representation of shape deviations during the cutting of slim profiled workpieces and their deformation handling is a major challenge. In this paper, a valid predictive model, developed for the compensation of distortions resulting from the effect of a laser-based treatment and a deep rolling, was calibrated by experimental data. The numerical design of these strategies provided a model for predicting compensation parameters to minimize profile grinding distortions

A Novel Approach to the Holistic 3D Characterization of Weld Seams—Paving the Way for Deep Learning-Based Process Monitoring

In an industrial environment, the quality assurance of weld seams requires extensive efforts. The most commonly used methods for that are expensive and time-consuming destructive tests, since quality assurance procedures are difficult to integrate into production processes. Beyond that, available test methods allow only the assessment of a very limited set of characteristics. They are either suitable for determining selected geometric features or for locating and evaluating internal seam defects. The presented work describes an evaluation methodology based on microfocus X-ray computed tomography scans (µCT scans) which enable the 3D characterization of weld seams, including internal defects such as cracks and pores. A 3D representation of the weld contour, i.e., the complete geometry of the joint area in the component with all quality-relevant geometric criteria, is an unprecedented novelty. Both the dimensions of the weld seam and internal defects can be revealed, quantified with a resolution down to a few micrometers and precisely assigned to the welded component. On the basis of the methodology developed within the framework of this study, the results of the scans performed on the alloy AA 2219 can be transferred to other aluminum alloys. In this way, the data evaluation framework can be used to obtain extensive reference data for the calibration and validation of inline process monitoring systems employing Deep Learning-based data processing in the scope of subsequent work

Formation of joining mechanisms in friction stir welded dissimilar Al-Ti lap joints

Friction Stir Welding (FSW) is a suitable technology for joining dissimilar materials. As the process temperature during FSW typically does not exceed the solidus temperature, like in fusion welding, high quality joints can be produced with a minimum of intermetallic phases. A comprehensive description of the effective joining mechanisms of friction stir welded dissimilar material joints is still subject of research. In this study the results of an analysis of the effect of the pin length, which is supposed to have a significant influence on the characteristics of the joining mechanisms, are presented. Especially the influence on the bonding conditions and the mechanical properties of the joints has been investigated. For this purpose combinations of aluminum and titanium have been welded with varying pin length at different rotational speeds. The experiments show that at a sufficient distance between the interface zone and the pin tip the bonding is realized by a substance-to-substance bond and microscopic form-fit. As this distance decreases, a visible macroscopic form-fit is generated. However, this macroscopic form-fit causes no significant elevation of the joint strength. First scanning transmission electron microscopy (STEM) images reveal an interfacial layer, which indicates a diffusion of the two materials.</jats:p