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Understanding Transformations in Copper Bearing Low Carbon Steels Teruhisa Okumura Department of Materials Science and Metallurgy University of Cambridge 23 June 2003
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Introduction ● Copper-bearing low-carbon steel ● Aim and method ● Transformations in steel ● Experimental procedure ● Results and discussion ● Conclusion ● Future work
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Copper-Bearing Low-Carbon Steel Low Carbon- High Toughness - Good Weldability Copper - High Strength (Precipitation Hardening) Without heat treatments!
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Aim and Method Understand transformations and precipitation Predict mechanical properties Reduce development cost and term
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Transformations in Steel Fe + + wt% C Fe-C Alloy Fe 3 C Temperature + Time P ww Displacive ' ' Bs Ws Ms bb Reconstructive Cooling curve CCT diagram Ae 3 Ae 1
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Rolling Conditions Average cooling rate 0.4 K s -1 (1073 - 773 K) Reheating 1573 K 1 hr
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Chemical Compositions Chemical compositions were varied to determine the optimum value. wt% C Si MnCu Ni 0.02-0.06 0.10-0.15 1.00-1.50 0.1-1.2 0.1-1.2 Mo Cr V B 0.0-0.3 0.0-0.3 0.00-0.08 0-11 ppm
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Experimental Results
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Lab. Experiment Micrography UTS 730 MPa vEo 9 J 670 MPa 10 J 671 MPa 108 J 509 MPa 271 J 40 m Toughness w fraction UTS
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Mill Experiment Micrography Chemical segregation of substitutional alloying elements (Mn) Solute enriched region Widmanstätten ferrite Solute depleted region allotriomorphic ferrite UTS 604 MPa vEo 256 J
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Cu precipitates Solute enriched region (BF) Solute depleted region (BF) Solute enriched region (DF)
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Interphase Precipitation Quasi-Ledge MechanismLedge Mechanism
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Copper Precipitation Fe + Cu + wt% Cu Fe-Cu Alloy Cu Temperature + Cu Time -Cu w w Cooling curve
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Conclusion In Copper-bearing low-carbon steels, ● Correlation between UTS and toughness. ● Chemical segregation in the real products which may increase fraction. ● Interphase Cu-precipitates which may reduce growth rates.
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Future Work Established transformation model Extended transformation model Copper precipitation Microstructure Solute enriched region Solute depleted region Mechanical properties Physical and neural network modelling
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