University of Cambridge Department of Materials Science and Metallurgy

1. Modelling The Microstructure and Mechanical Properties of Austempered Ductile Cast Iron University of Cambridge Department of Materials Science and Metallurgy By Miguel Angel Yescas-Gonzalez

2. CHEMICAL COMPOSITION OF CAST IRON:Fe C Si Mn P S Mgval. 3.5 2.5 0.25 0.038 0.015 0.05 Only in Ductile iron Grey cast iron No addition of Mg or Ce Tensile strength: 150-400 MPa Elongation: 0 % Ductile cast iron Addition of cerium or magnesium to induce nodularisation of graphite Tensile strength: 350-800 MPa Elongation: 3-20 %

3. Microstructure of Ductile irons

4. Austempered ductile cast iron (ADI) A further improvement of ductile cast iron is obtained with an isothermal heat treatment named austempering 1. Austenitising between 850 and 950 C typically for 60 min. 2. Quenching into a salt or oil bath at a temperature in the range 450 - 250 C usually between 0.5 and 3 hours 3. Cooling to a room temperature

5. Mechanical properties STRENGTH : equal to or greater than steel ELONGATION : maintain as cast elongation while double the strength of quenched and tempered ductile iron TOUGHNESS : better than ductile iron and equal to or better than cast or forged steel FATIGUE STRENGTH : equal to or better than forged steel. DAMPING : 5 times greater than steel.

6. R. Elliott, 1988

7. Economical advantages and applications • ADI has excellent castability, it is possible to obtain near-net shape castings even of high complex parts. • ADI is cheaper than steel forgings • ADI has a weight saving of 10% Gears Automotive industry

8. Processing window Sage I: Austenite decomposition to bainitic ferriteandcarbon enriched austenite g g a + r The bainitic transformation in ductile iron can be described as two stage reaction: Sage II: Further austenite decomposition to ferrite and carbide g + a Carbide r

9. Closed processing window

10. Microstructure of ADI • Bainite • Retained austenite • Martensite • Carbide • Pearlite

11. Element Cell boundary Close to graphite Mn 0.81 0.57 Si 2.31 2.49 Mo 0.16 0.12 Element Cell boundary Close to graphite Mn 1.73 0.40 Si 1.75 2.45 Mo 0.60 0.07 Fe-3.5C-2.5Si-0.55Mn-0.15Mo

12. o homogenised at 1000 C for 3 days Austempered at 350 C for 64 min

15. Variables for modelling include: C, Mn, Si, Mo, Ni, Cu, Austenitising temperature and time Austempering temperature and time V = a + b (%C) + c (%Mn) g V = a + b (%C) + c(%Mn) + d (%C x %Mn) g V = sin (%C) + tanh (%Mn) g

16. T C Mn A INPUT C x W c Mn x W Mn HIDDEN sum OUTPUT V g

17. Modelling with neural networks Hyperbolic tangents a) three different hyperbolic tangents functions b) combination of two hyperbolic tangents

18. NEURAL NETWORKS Microstructural model for volume fraction of retained austenite (V) DATABASE (Experimental data) Modelling with neural networks g Input variables Output or target = tanh (Sw x + q ) h i j i ij j

20. Error bars

28. Physical Model for Retained Austenite

33. Babu etal. 1993

38. 40 mm