
Since 1937, the MMPDS database (Metallic Materials Properties Development and Standardization) has been the reference for designing safety-critical aerospace components. At the MMPDS workshop on October 16, 2025, in Wuppertal, the central question was how to further narrow scatter bands in material properties—especially for additively manufactured components. Doug Hall, MMPDS Program Manager at Battelle, outlined the role of statistically substantiated datasets for qualifying AM parts and pointed to a milestone: Ti-6Al-4V per AMS 7004/7005 is set to be included in the upcoming MMPDS Volume 2, based on decisions from the 46th MMPDS Coordination Meeting in September 2025. The basis is A-, B-, C-, D-, and S-values, which represent minimum strengths and, respectively, scatter parameters with defined statistical confidence.
A second focus was the integration of MMPDS with ICME tools. JMatPro provides physics-based predictions of phases, mechanical and thermophysical properties, thereby reducing testing effort in early development phases. Matplus EDA adds exploratory data analysis and model-based correlations to identify uncertainties in large data spaces. Both approaches are coupled in BPMN2 workflows with DoE plans and optimizers such as MADS or NSGA-III to systematically resolve trade-offs between strength, cost, and process windows.
There are tangible effects for the industry. Additive manufacturing benefits from clearly defined, statistically robust material cards, which accelerate qualifications and make re-tests more targeted. Narrower scatter bands enable lighter structural components without compromising demonstration of compliance. In digitally linked process chains, predictions from ICME and evidence from MMPDS can be merged into consistent “digital twins”—from alloy development all the way to series production.
“The combination of MMPDS, ICME, and JMatPro is a game changer for the aerospace industry,” emphasizes Uwe Diekmann, exclusive JMatPro distributor in the DACH region. “By integrating simulation, DoE, and optimization, material data can not only be captured more precisely but also translated more quickly into certifiable solutions.”
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