Quantification of phase transformation in stainless steel 301LN sheets

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2008.Includes bibliographical references (p. 101-102).This thesis investigates the large deformation behavior of stainless steel 301LN cold-rolled sheets which is largely governed by the initial anisotropy combined with the phase transformation during deformation. Stainless steel offers high strength with relatively high ductility as compared with other structural steels. The effect of initial anisotropy on the strength in different material directions is studied in order to predict forming and crash response of vehicle components. It is observed that loading in the material rolling direction results in increased strength in the cross direction, however loading in the material cross-rolling direction results in decreased strength in the rolling direction. The mechanism responsible for the above cross-hardening is complex and requires investigation of the microstructural evolution of the sheets. The austenitic stainless steel studied is comprised of only austenite when in bulk form. However, the process of cold-rolling the bulk material into sheets results in strain-induced martensitic phase transformation. Additional straining of the material leads to even more transformation of austenite to martensite. Because martensite is a harder phase than austenite, micromechanical arguments suggest that the amount of martensite has an effect on the plasticity and eventual fracture of this material. In this thesis, the martensitic evolution as a function of material direction and strain level is measured using three different techniques: X-ray diffraction, microscopy, and magnetic induction. The first two methods require interrupted tests, while using a Feritscope allows for in-situ measurement of the martensite content. However, the Feritscope must be calibrated by another measurement method.(cont.) Observations of the measurements from each of the three methods confirm that the output of the Feritscope, Ferrite Number, is proportional to the martensite content. Therefore in-situ tests employing the Feritscope will allow for monitoring of the martensite content with evolution of stress and strain. From experiments described here, a directional dependence on martensite content is observed. The results from this study can be used to formulate an anisotropic martensite transformation kinetics law to describe the evolution of martensite content as a function of material anisotropy, stress state, and strain state.by Allison M. Beese.S.M

Experimental investigation and constitutive modeling of the large deformation behavior of anisotropic steel sheets undergoing strain-induced phase transformation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 139-146).The strain-induced phase transformation from austenite to martensite is responsible for the high strength and ductility of TRansformation-Induced Plasticity (TRIP)- assisted steels. The large deformation behavior of conventional steels is governed by crystallographic slip. In the case of TRIP steels, the phase transformation provides an additional microstructural deformation mechanism, which has a particularly strong effect on the strain hardening response at the macroscopic level. This thesis work develops a new plasticity model for TRIP steels that accounts for the effect of phase transformation. In particular, the large deformation behavior of 1.5mm thick stainless steel 301LN sheets at room temperature is studied in detail. Several techniques for quantifying the martensite volume fraction are evaluated including micrography, X-ray diffraction, neutron diffraction, magnetic saturation, and magnetic permeability measurements. The latter is then used to measure the evolution of the martensite content throughout mechanical experiments. The experimental program for different stress states includes experiments for uniaxial tension, uniaxial compression, equi-biaxial tension, pure shear, and transverse plane strain tension. The resulting experimental data demonstrate the influence of both the stress triaxiality and Lode angle parameter on the austenite-to-martensite transformation kinetics. A stress-state dependent transformation kinetics evolution equation is proposed which describes the martensite content as a function of plastic strain, the stress triaxiality, and the Lode angle parameter. Furthermore, a phenomenological plasticity model is developed comprising an anisotropic yield function, an isotropic hardening law, and a nonlinear kinematic hardening law with initial back stress. The isotropic hardening law expresses the increase in deformation resistances as a function of the plastic strain and the martensite content and is directly coupled with the stress-state dependent transformation kinetics equation. As a result, the model is able to describe the experimentally observed effect of stress state on the macroscopic hardening response. The constitutive model is implemented into a finite element program and used to simulate all experiments performed. The model predictions agree well with the experimental results for a wide range of stress states and for both specimens with homogeneous and heterogeneous stress and strain fields.by Allison M. Beese.Ph.D

An image-based transfer learning approach for using in situ processing data to predict laser powder bed fusion additively manufactured Ti-6Al-4V mechanical properties

The mitigation of material defects from additive manufacturing (AM) processes is critical to reliability in their fabricated parts and is enabled by modeling the complex relations between available build monitoring signals and final mechanical performance. To this end, the present study investigates a machine learning approach for predicting mechanical properties for Ti-6Al-4V fabricated through laser powder bed fusion (PBF-LB) AM using in situ photodiode processing signals. Samples were fabricated under different processing parameters, varying laser powers and scan speeds for the purpose of probing a wide range of microstructure and property variations. Photodiode data were collected during fabrication, later to be arranged in image format and extracted to information-dense vectors by the transferal of deep convolutional neural network (DCNN) structures and weights pre-trained on a large computer vision benchmark image database. The extracted features were then used to train and test a newly designed regression model for mechanical properties. Average cross-validation accuracies were found to be 98.7% (r2 value of 0.89) for the prediction of ultimate tensile strength, which ranged from 900 to 1150 MPa in the samples studied, and 93.1% (r2 value of 0.96) for the prediction of elongation to fracture, which ranged from 0 to 17%. Thus, with high accuracy and hardware accelerated inference speeds, we demonstrate that a transfer learning framework can be used to predict strength and ductility of metal AM components based on processing signals in PBF-LB, illustrating a potential route toward real-time closed-loop control and process optimization of PBF-LB in industrial applications

First-principles Investigation of Thermodynamic Properties of CrNbO4 and CrTaO4

In the present study, the DFT+U method was employed to predict the thermodynamic properties of Cr2O3, Nb2O5, and Ta2O5. Results were benchmarked with experimental data showing high accuracy, except for the negative thermal expansion (NTE) of Nb2O5, which is attributed to its polymorphic complexity. Additionally, we extended our analysis to rutile-type oxides CrNbO4 and CrTaO4, examining their entropy and heat capacity at finite temperatures. CrNbO4 displayed slightly higher entropy and heat capacity at high temperatures. The mean linear thermal expansion coefficients for CrNbO4 and CrTaO4 from 500 K to 2000 K were predicted to be 6.00*10-6/K and 13.49*10-6/K, respectively, corroborating with DFT predictions and experimental evidence. Our research highlights the precision of the DFT+U and phonon methods in predicting the thermodynamic properties of oxide materials, offering insights into the design of corrosion-resistant materials

MaterialsMap: A CALPHAD-Based Tool to Design Composition Pathways through feasibility map for Desired Dissimilar Materials, demonstrated with RSW Joining of Ag-Al-Cu

Assembly of dissimilar metals can be achieved by different methods, for example, casting, welding, and additive manufacturing (AM). However, undesired phases formed in liquid-phase assembling processes due to solute segregation during solidification diminish mechanical and other properties of the processed parts. In the present work, an open-source software named MaterialsMap, has been developed based on the CALculation of Phase Diagrams (CALPHAD) approach. The primary objective of MaterialsMap is to facilitate the design of an optimal composition pathway for assembling dissimilar alloys with liquid-phases based on the formation of desired and undesired phases along the pathway. In MaterialsMap, equilibrium thermodynamic calculations are used to predict equilibrium phases formed at slow cooling rate, while Scheil-Gulliver simulations are employed to predict non-equilibrium phases formed during rapid cooling. By combining these two simulations, MaterialsMap offers a thorough guide for understanding phase formation in various manufacturing processes, assisting users in making informed decisions during material selection and production. As a demonstration of this approach, a compositional pathway was designed from pure Al to pure Cu through Ag using MaterialsMap. The design was experimentally verified using resistance spot welding (RSW)

Assessing MWCNT-graphene surface energy through in situ SEM peeling

Carbon nanotubes (CNTs) are envisioned as ideal filaments for next-generation nanocomposites due to their high strength-to-weight ratios. However, while individual nanotubes are strong, interfaces between tubes cannot bear significant load due to the weak van der Waals forces that govern their behavior. Premature interfacial failure could thus counteract the inherent strength of carbon nanotubes and, in turn, prevent CNT-based composites from achieving optimal mechanical performance. To increase the load bearing capacity of these interfaces, interlayer crosslinking schemes have been proposed using chemical functionalization. For instance, introduction of hydrogen bonds or additional van der Waals bonds between tubes could improve load transfer between CNTs. While introducing chemical groups on CNT surfaces may enhance intermolecular interactions at these interfaces, a means of quantitatively evaluating changes in interlayer adhesion as a result of to these treatments needs to be defined. In addition, as sizes of CNTs will inherently vary within a composite, it is important that such energy measurements be normalized irrespective of tube dimensions. Here we report an experimental peeling technique that can be used to measure the adhesion energy between multiwalled carbon nanotubes (MWCNTs) and graphene. Peeling tests conducted in situ a scanning electron microscope allow direct visualization of the nanoscale peeling process which, in turn, enables adhesion energy to be estimated through classical fracture analysis. The applicability of this analysis is validated by finite element simulations with boundary conditions derived from experiments. The effective contact width between tubes and graphene is estimated via atomistic simulations, providing a means to normalize interaction energy per unit area. The surface energies of bare MWCNT-graphene interfaces found in this study compare favorably with theoretical and experimental values reported for graphite. This method can serve as a foundation for evaluating the enhancements afforded by chemical functionalization, which is a critical step toward the development of strong, lightweight composites that effectively utilize the full mechanical potential of CNTs

Additively manufactured Ni-20Cr to V functionally graded material: computational predictions and experimental verification of phase formations

A database for the Cr-Ni-V system was constructed by modeling the binary Cr-V and ternary Cr-Ni-V systems using the CALPHAD approach aided by density functional theory (DFT)-based first-principles calculations and ab initio molecular dynamics (AIMD) simulations. To validate this new database, a functionally graded material (FGM) using Ni-20Cr and elemental V was fabricated using directed energy deposition additive manufacturing (DED AM) and experimentally characterized. The deposited Ni-20Cr was pure fcc phase, while increasing the amount of V across the gradient resulted in the formation of sigma phase, followed by the bcc phase. The experimentally measured phase data was compared with computational predictions made using a Cr-Ni-V thermodynamic database from the literature as well as the database developed in the present work. The newly developed database was shown to better predict the experimentally observed phases due to its accurate modeling of binary systems within the database and the ternary liquid phase, which is critical for accurate Scheil calculations

DFTTK: Density Functional Theory Tool Kit for High-throughput Calculations of Thermodynamic Properties at Finite Temperatures

In this work, we present a software package in Python for high-throughput first-principles calculations of thermodynamic properties at finite temperatures, which we refer to as DFTTK (Density Functional Theory Tool Kit). DFTTK is based on the atomate package and integrates our experiences in the last decades on the development of theoretical methods and computational software. It includes task submissions on all major operating systems and task execution on high-performance computing environments. The distribution of the DFTTK package comes with examples of calculations of phonon density of states, heat capacity, entropy, enthalpy, and free energy under the quasi-harmonic phonon scheme for the stoichiometric phases of Al, Ni, Al3Ni, AlNi, AlNi3, Al3Ni4, and Al3Ni5, and the fcc solution phases treated using the special quasirandom structures at the compositions of Al3Ni, AlNi, and AlNi3.Comment: 49 pages, 18 figure

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio