Can instrumented hardness measurements (AI-augmented indentation testing) deliver reliable strength - plasticity data in 3D-printed high performance Aluminum materials ?

Classical hardness testing like Vickers or Brinell is a very valuable tool for fast material (strength) evaluation. The simplicity of the measuring procedure features high robustness and widespread trust in the generated results. However due to its propensities it cannot answer the quality assurance engineering request on material ductility. For (safe) strength driven products beside sufficient high strength the capability of visible deformation before final failure is of big concern. Additively manufactured (AM) parts which always include the part material creation represent an inherent challenge regarding the final safeguarding of its operational product properties. Simply said: Stress (quality) engineers have (understandable) doubts or lack trust about the directly generated material properties due to the complexity of the L-PBF- process. In particular Aluminum alloys are known to be notoriously difficult when it comes to laser based melting (welding) and solidification events. A further development of incumbent hardness measurements (based on Rockwell & Universal hardness) which combine the “old” technology with local material displacement measurements feeding into a material modelling algorithm to simulate & predict delivers pseudo-stress-strain curves highlighting local material ultimate strength (UTS), yield strength (YS) and elongation. Permanent training by artificial intelligence (AI) might enable high accuracy and sufficient reliability if compared against standard material testing approaches. The presentation will showcase the results of an assessment using this new testing tool compared to classical tensile testing done with 450 - 500 MPa strong 3D-printed (AlMgSc) ScalmalloyⓇ and (AlCrSc) ScancromalⓇ.