This article presents a novel approach for assessing the effects of residual stresses in laser-directed energy deposition (L-DED). The approach focuses on exploiting the potential of rapidly growing tools such as machine learning and polynomial chaos expansion for handling full-field data for measurements and predictions. In particular, the thermal expansion coefficient of thin-wall L-DED steel specimens is measured and then used to predict the displacement fields around the drilling hole in incremental hole-drilling tests. The incremental hole-drilling test is performed on cubic L-DED steel specimens and the displacement fields are visualized using a 3D micro-digital image correlation setup. A good agreement is achieved between predictions and experimental measurements.

In this paper, the potential of directional ultrasonic wave superposition by moving sound generators for laser beam welding of high-strength steel alloys 1.5528 (22MnB5) is studied. Steel sheets of identical thickness and in form of tailored blanks were joined in butt joint configuration. The influences of the various excitation parameters of the moving sound generators on the ultrasonic coupling and their influence on the distribution of the AlSi coating components within the melting zone and the weld seam characteristics are investigated. Etched cross-sections, scanning electron microscopy, energy dispersive X-ray spectroscopy, and electron backscattering measurements were used as the investigation methods to determine the AlSi distribution in the weld as well as its microstructure. The results presented a series of experiments which show that a suitable superposition of ultrasonic waves by the moving sound generators lead to a more homogeneous distribution of AlSi particles in the melt as well as to a finer microstructure within the weld, which improves the mechanical–technological properties.