From Metallurgy to Optimized Casting Properties
The quality and profitability of iron castings are driven by the metallurgy of the melt, the design of the pattern, the productivity of the process, reproducible manufacturing conditions and the resulting properties of the cast iron material after casting and heat treatment.
MAGMASOFT® is capable of autonomously establishing robust process conditions and optimized solutions for the pouring, solidification and heat treatment of all cast iron alloys. The integrated tools to statistically set up virtual Designs of Experiments and automatically assess results support foundrymen in determining optimal design and process conditions, for example regarding:
- optimization of gating design for improved cast iron cleanliness and surface quality
- positioning and size of feeders/risers eliminating shrinkage and porosity while maintaining high yield levels
- optimum cast iron chemistry, metal treatment and inoculation for prevention of critical phases during solidification and further metal cooling
- set-up of robust process conditions for reaching the required mechanical properties
- casting design features such as minimized risk of cold cracks and dimensional tolerances
- resource conserving heat treatment processes tuned to achieve the desired microstructure and the required properties
Mold filling of gray iron brake discs
Thermal modulus in a planetary carrier
Solidification path for a ductile iron axle housing
Calculated cooling curves for different inoculation conditions
Improved process stability assessing nucleation fading behavior on cooling curves and nodule count
Local perlite fractions in a ductile iron casting
Influence of different Si contents on the amount of white iron in a chill wedge
Porosity in a ductile iron casting
Assessment of influence of carbon content, metallurgy and shake out conditions on casting hardness
Tensile strength distribution in an exhaust manifold
Mold filling of a rotor hub
Residual stresses in a rotor hub after heat treatment
Residual stresses in a cast iron boiler segment
Critical areas for cold cracking
Hardness distribution in a gray iron crankcase
Assessment of different inoculation methods on resulting casting nodule count
Local graphite nodularity for a CGI crankcase
Variation of pouring temperature to establish robust processes, here showing cold lap tendency
Identifying the best compromise between cold lap tendency and air entrapment
Systematic defect reduction during series production, here for a gray iron exhaust manifold
Autonomous optimization of venting conditions to avoid entrapped gases
Scatter diagram from virtual experiments for systematic defect reduction during series production
Virtual Design of Experiments to assess different process and design variables on casting quality autonomously
Quantitative impact of different chill layouts on casting porosity
Quantitative assessment of main effects on casting porosity
Reproducible process conditions to realize required properties, here hardness
MAGMASOFT®s unique integrated methodology of virtual experimentation and autonomous optimization lets the software automatically run through various iron casting design and parameter combinations. MAGMASOFT® helps identify a set of process conditions best meeting the specified requirements. This is done quickly and comprehensively, matching different objectives considering all driving forces decisive for iron casting quality.
This makes MAGMASOFT® key for casting designers, pattern makers and foundrymen to lay out optimal cast component designs, implement reliable production processes, minimize quality risks, set up reliable process windows and realize the full potential of cast iron materials.
Currents due to thermal convection influence the feeding behavior
MAGMAiron 5.5 - Solid Tracer
Accurate porosity prediction with MAGMAiron
SMAFEE considers flow through partially solidified feeding channels
Effect on melt quality on amount of porosity
Different inoculation levels change the extent of predicted porosity
Currents due to thermal convection influence the feeding behavior
Porosity prediction with and without consideration of convection
