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The microstructure–properties relations and strengthening mechanisms of additively manufactured 316L stainless steel are comprehensively investigated in this work. The orientation dependency and the strain hardening are studied by tensile testing of as‐built specimens fabricated by laser‐based powder bed fusion (LPBF) in different directions. The results are compared with those obtained for wrought material. The microstructure of the wrought and the LPBF materials are also comprehensively investigated. Equiaxed grains with random orientation and relatively uniform size (≈30 μm) are observed in the wrought material, where the LPBF samples show columnar grains inside as well as fine equiaxed grains in the bottom of the molten pool. A bimodal grain size distribution, higher values of geometrically necessary dislocations density (≈25–32%), and lower fractions of high‐angle grain boundaries (≈24–28%) are observed in LPBF 316L. A significant yield strength and considerable ultimate strength improvement without remarkable elongation decrease are obtained for the LPBF tensile specimens, resulting in a high strength‐elongation balance (up to 26 122 MPa%). Two‐stage strain hardening is depicted in both wrought and LPBF samples. This is successfully predicted with two‐stage Hollomon analysis. However, the LPBF samples illustrate lower strain hardening exponents in comparison with the wrought ones. [ABSTRACT FROM AUTHOR]

The article provides information on the digitization application in the welding repair of turbine blades. It also discusses the development of automatically recondition thin-walled components using robot technology. Topics discussed includes digitizing the welding plane, welding repair and Laser beam deposition welding.

This article describes an algorithm for the online extrapolation of hand-motion during remote welding. The aim is to overcome the spatial limitations of the human welder's arms in order to cover a larger workspace with a continuous weld seam and to substantially relieve the welder from strain and fatigue. Depending on the sampled hand-motion data, an extrapolation of the given motion patterns is achieved by decomposing the input signals in a linear direction and a periodic motion component. An approach to efficiently determine the periodicity using a sampled autocorrelation function and the subsequent application of parameter identification using a spline function are presented in this paper. The proposed approach is able to resemble all practically relevant motion patterns and has been validated successfully on a remote welding system with limited input space and audio-visual feedback by an experienced welder. [ABSTRACT FROM AUTHOR]