University of CambridgeDepartment of Materials Science and Metallurgy

Modelling The Microstructure and Mechanical Properties of Austempered Ductile Cast Iron

By

Miguel Angel Yescas-Gonzalez

CHEMICAL COMPOSITION OF CAST IRON:Fe C Si Mn P S Mg

val. 3.5 2.5 0.25 0.038 0.015 0.05

Grey cast iron

Ductile cast ironAddition of cerium or magnesium to inducenodularisation of graphite

Only in Ductile iron

No addition of Mg or Ce

Tensile strength: 150-400 MPa

Tensile strength: 350-800 MPa

Elongation: 3-20 %

Elongation: 0 %

Microstructure of Ductile irons

1. Austenitising between 850 and 950 Ctypically for 60 min.

2. Quenching into a salt or oil bath at a temperature in the range 450 - 250 Cusually between 0.5 and 3 hours

3. Cooling to a room temperature

Austempered ductile cast iron (ADI)

A further improvement of ductile cast iron is obtained with anisothermal heat treatment named austempering

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.

R. Elliott, 1988

ADI has excellent castability, it is possible to obtain near-net shape castings even of ADI has excellent castability, it is possible to obtain near-net shape castings even of high complex parts. high complex parts.

ADI is cheaper than steel forgingsADI is cheaper than steel forgings

ADI has a weight saving of 10% ADI has a weight saving of 10%

Economical advantages and applications

Gears Automotive industry

Processing windowThe bainitic transformation in ductile iron can be described as two stage reaction:

Sage I: Austenite decomposition to bainitic ferrite and carbon enriched austenite

g +Sage II: Further austenite decomposition to ferrite and carbide

g + Carbide

gra

r a

Closed processing window

Microstructure of ADI

Bainite

Retained austenite

Martensite

Carbide

Pearlite

Element Cell boundary Close to graphite Mn 1.73 0.40 Si 1.75 2.45 Mo 0.60 0.07

Element Cell boundary Close to graphite Mn 0.81 0.57 Si 2.31 2.49 Mo 0.16 0.12

Fe-3.5C-2.5Si-0.55Mn-0.15Mo

homogenised at 1000 C for 3 days

Austempered at 350 C for 64 min

o

Variables for modelling include:

C, Mn, Si, Mo, Ni, Cu,

Austenitising temperature and time

Austempering temperature and time

Vg= a + b (%C) + c (%Mn)

Vg= a + b (%C) + c(%Mn) + d (%C x %Mn)

Vg = sin (%C) + tanh (%Mn)

V

TA Mn

INPUT

HIDDEN

OUTPUT

C

g

sum

C x WMn x W

cMn

Modelling with neural networks

a) three different hyperbolic tangents functionsb) combination of two hyperbolic tangents

Hyperbolic tangents

Modelling with neural networks

Microstructural modelfor volume fraction of

retained austenite(V )

NEURAL NETWORKS

DATABASE(Experimental data)

g Input variables

Output or target

h = tanh (S w x + )qijj ji i

Error bars

Physical Model for Retained Austenite

Babu etal. 1993

40 mm