Project description

Austempered Ductile Iron ADI is an ausferritic ductile iron with spheroidal graphite. It combines high strength and wear resistance with good ductility. Manufacturing is demanding because many parameters across the chain from casting through heat treatment to machining must be coordinated precisely. Important quality metrics are dimensional accuracy, hardness, martensite content and tool life. Changes in composition or temperature control can modify the microstructure, cause hardness variation and, through uneven cooling, create residual stresses and distortion.

The project seeks to model the ADI process chain with data driven methods and to guide it via inverse optimization. We study how adjustable variables such as alloy concentrations during casting, temperatures together with transfer times and process times during heat treatment, and machine settings during milling as well as sources of uncertainty such as casting temperature relate to the quality defining targets.

In the first funding phase we will build an experimental and well instrumented process chain that captures data continuously. These data form the basis for multivariate invertible models of the individual steps. A central element is the integration of uncertainties into the modeling environment to raise process robustness for milled ADI parts. The step models will then be linked into a single model of the entire chain that provides a holistic prediction of part quality. Inverse design is at the core. Starting from required properties we infer the controllable parameters while accounting for process uncertainties. This prepares the second phase, which will carry out a comprehensive multi objective optimization with the goal of producing ADI parts more efficiently and with lower resource use.

Process chain