vikas saraf
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© ATI 2012. All Rights Reserved.
Vikas Saraf
Artificial Neural Networks to Predict Tensile Propertiesin Titanium Forgings
An ICME Approach: Integrated Computational Materials Engineering
October 7-10, 2012 • Atlanta, Georgia
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Predicting Tensile Properties in Titanium Forgings
• ICME in action
• Ti6-4 processing
• Models developed
• Integration and results
• Summary
ICME: Integrated Computational Materials Engineering
Overview
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Optimal Solution
Billet Requirement
Forging/HT/Machining requirement
Engine designrequirement
Machined
In-engine
OEM shape design
ICME in Supply chain
EA GP7000
© ATI 2012. All Rights Reserved.© ATI 2012. All Rights Reserved.Titanium in Boeing 777: 10% of the structural weight (Source: ITA).
Advanced Material Usage- Titanium in Boeing 777
Courtesy: The Boeing Company
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ß-transus
α-ß
Primary processing
Secondary processing
Evolving microstructure
Ti6‐4 Processing
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Ti6-4 HT1 Temp= ß+
Alpha Prime phase basketweave structure within prior beta grains.
This structure results from fast cooling from Heat Treating in the Beta phase field.
Alpha-Beta structure refined through conversion and forging process.
Heavy Cross-Section thicknesses Cooling rate is too slow…
Reduced Cross-Section thicknesses Cooling rates better for high strength.
Ti6‐4 Processing
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• Phase Equilibrium/ Beta approach curve
• Phase Field/ Alpha and beta volume fraction
• Crystal Plasticity– Yield surface– Strain partitioning– Texture
• Primary alpha growth/ volume fraction
• Prior beta grain size
• Secondary alpha growth (lath width)
• Variant selection (αs transformation)
• Texture formulation (Kearns number)
• RT Tensile properties
Predictability
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Neural Network model, Ti6-4
Fv- volume Fraction
Predictability
Artificial Neural Network Training using PatternMaster® Program
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Validation
UTS; ±5%
Neural Network model, Ti6-4
Predictability
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Production resultsTi 6-4 pancake forged in production environmentα-ß forged; α-ß heat treated; aged
Measurement locations
Process Simulation• Billet conversion• Secondary near-net forging• Heat Treatment
Data Generation• Beta approach curve• Flow stress generation• Strain partitioning• Initial Texture
Prediction• α, ß growth• α Fv• α-lath• ND α-texture
Post-Processing• RT Tensile properties
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Volume fraction of primary alpha Grain size (radius) of primary alpha
Thickness of secondary alpha lath
Production results
LocationBCMCRCBTMT
0.070.070.000.220.00
0.070.020.210.010.09
0.262.500.370.980.69
αp-size (µm) αp-Fv αs-width (µm)Measured-Predicted Measured-Predicted Measured-Predicted
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Production resultsUTS
1 3
2 4 5
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Production resultsYield Strength
1 3
2 4 5
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Production resultsElongationReduction in Area
With texture
Without texture
1 3
2 4 5
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Neural Network model, Ti6-4
(-) Nominal (+) (-) Nominal (+)
Upper bound
Lower bound
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Neural Network model, Ti6-4
0 2x 0 2x
Upper bound
Lower bound
© ATI 2012. All Rights Reserved.© ATI 2012. All Rights Reserved. Measured Avg / Range
Radial Axial Hoop
148.5/3
150/0
156/0
147.5/1
146.5/1
157/0
135.5/1
136/0142/0
134/2
133.5/1
144/0
UTS
YS
α-ß forged; α-ß heat treated; agedTensile strength prediction; Shaft forging Shen, Saraf, Furrer; TMS 2006
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Radial Hoop
144ksi143ksi
143ksi
147ksi147ksi149ksi
139ksi
Tensile strength prediction; Complex forging
α-ß forged; Mill annealed
Shen, Saraf, Furrer; TMS 2006
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Infrastructure to predict complex microstructural evolution and, subsequently, its effect on tensile properties, has been developed.
Simple geometrical shapes produced in an industrial environment confirm the predictability and application of the developed infrastructure.
Complex, near-net-shape aerospace components have shown good correlation between prediction and measurements.
Summary
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Acknowledgement
Development and integration of models was funded by the USAF under the auspices of Materials Affordability Initiatives.
Michael Glavicic, Tom Broderick, Vasisht Venkatesh, Todd Morton,Yoji Kosaka, Ron Wallis, Vikas Saraf
(AIPT Team members from Rolls-Royce, GE, Pratt & Whitney, Boeing, Timet, Wyman Gordon and ATI Ladish respectively)
And
Fan Zhang, Wei-Tsu Wu, Ravi Shankar, Ayman Salem,Yunzhi Wang, Donald Boyce
(Sub-contractors from CompuTherm, Scientific Forming Technologies Corp., Materials Resources, The Ohio State University and Cornell University, respectively)
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