ICME at GE: Accelerating the insertion of new materials and processes

Backman, Daniel; Wei, Daniel; Whitis, Deborah D.; Buczek, Matthew B.; Finnigan, Peter M.; Gao, D.T.

doi:10.1007/s11837-006-0225-3

Cited by 44 publications

(19 citation statements)

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“…The tool can therefore be allowed to explore the entire range of compositions, and as soon as it is extrapolating far beyond any available experimental data points the uncertainty will grow, naturally bounding the range of compositions from which new alloys may be reliably predicted. The use of likelihood also allows the tool to explore and select the ideal compromise between material properties, which is inaccessible to methods that do not account for likelihood such as a principal component analysis [6] and robust design [7]. Similarly, the design tool may interpolate between experimental data, exploring more compositions than would be accessible by an experimentally driven search.…”

Section: Formalismmentioning

confidence: 99%

Design of a nickel-base superalloy using a neural network

et al. 2017

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A new computational tool has been developed to model, discover, and optimize new alloys that simultaneously satisfy up to eleven physical criteria. An artificial neural network is trained from pre-existing materials data that enables the prediction of individual material properties both as a function of composition and heat treatment routine, which allows it to optimize the material properties to search for the material with properties most likely to exceed a target criteria. We design a new polycrystalline nickel-base superalloy with the optimal combination of cost, density, γ phase content and solvus, phase stability, fatigue life, yield stress, ultimate tensile strength, stress rupture, oxidation resistance, and tensile elongation. Experimental data demonstrates that the proposed alloy fulfills the computational predictions, possessing multiple physical properties, particularly oxidation resistance and yield stress, that exceed existing commercially available alloys.

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Section: Formalismmentioning

confidence: 99%

Design of a nickel-base superalloy using a neural network

et al. 2017

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“…This allows us to predict and control local microstructure and properties within components thereby meeting complex design specifications demanded by the aerospace industry. Such a strategy has facilitated rapid advancement in technology related to manufacturing process as well as alloy design (Committee on Integrated Computational Materials Engineering, 2008;Backman et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

A model for Ti–6Al–4V microstructure evolution for arbitrary temperature changes

Murgau

Pederson

Lindgren

2012

Modelling Simul. Mater. Sci. Eng.

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This thesis is devoted to microstructure modelling of Ti-6Al-4V. The microstructure and the mechanical properties of titanium alloys are highly dependent on the temperature history experienced by the material. The developed microstructure model accounts for thermal driving forces and is applicable for general temperature histories. It has been applied to study wire feed additive manufacturing processes that induce repetitive heating and cooling cycles. The microstructure model adopts internal state variables to represent the microstructure through microstructure constituents' fractions in finite element simulation. This makes it possible to apply the model efficiently for large computational models of general thermomechanical processes. The model is calibrated and validated versus literature data. It is applied to Gas Tungsten Arc Welding -also known as Tungsten Inert Gas welding-wire feed additive manufacturing process. Four quantities are calculated in the model: the volume fraction of phase, consisting of Widmanstätten , grain boundary , and martensite . The phase transformations during cooling are modelled based on diffusional theory described by a Johnson-Mehl-AvramiKolmogorov formulation, except for diffusionless martensite formation where the Koistinen-Marburger equation is used. A parabolic growth rate equation is used for the to transformation upon heating. An added variable, structure size indicator of Widmanstätten , has also been implemented and calibrated. It is written in a simple Arrhenius format. The microstructure model is applied to in finite element simulation of wire feed additive manufacturing. Finally, coupling with a physically based constitutive model enables a comprehensive and predictive model of the properties that evolve during processing.

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“…In the past decade, integrated computational materials engineering (ICME) has emerged and under this framework has developed through‐process modeling concept, which promotes integration of individual process modeling, making it possible to take the effect of a previous process into account in the modeling of a current process. For example, CA models have been developed for the heating process of heat treatment .…”

Section: Introductionmentioning

confidence: 99%

Modeling of Austenite Decomposition during Continuous Cooling Process in Heat Treatment of Hypoeutectoid Steel with Cellular Automaton Method

Zhou

2017

steel research int.

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The austenite decomposition during continuous cooling process in heat treatment of hypoeutectoid steel is studied with experimental method and numerical simulation. To investigate the phase transformation, quenching experiments are conducted and the microstructure evolution is investigated through metallographic examination. Dilatometric experiments are conducted as well and the experimental results are employed to analyze the phase transformation process during austenite decomposition. Subsequent studies of austenite decomposition in the steel are conducted using a numerical model based on cellular automaton (CA) method. In the simulation, the transformation of austenite into ferrite and pearlite is simulated, and the growth kinetics and average grain size of ferrite as well as the average interlamellar spacing of pearlite were obtained. The simulation results of the effects of cooling rate on the average grain size of ferrite and interlamellar spacing of pearlite agree well with the experimental results. Finally, integrated with a previously developed model for the austenization in heating process of heat treatment, the present model shows a capacity to investigate the effect of the prior austenite grain size on the final microstructure formed at the end of austenite decomposition.

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ICME at GE: Accelerating the insertion of new materials and processes

Cited by 44 publications

References 0 publications

Design of a nickel-base superalloy using a neural network

Design of a nickel-base superalloy using a neural network

A model for Ti–6Al–4V microstructure evolution for arbitrary temperature changes

Modeling of Austenite Decomposition during Continuous Cooling Process in Heat Treatment of Hypoeutectoid Steel with Cellular Automaton Method

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