Combined machine learning and CALPHAD approach for discovering processing-structure relationships in soft magnetic alloys

Jha, Rajesh; Chakraborti, Nirupam; Diercks, David R.; Stebner, Aaron P.; Ciobanu, Cristian V.

doi:10.1016/j.commatsci.2018.04.008

Cited by 30 publications

(12 citation statements)

References 46 publications

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“…Metastable phases cannot be shown on the phase diagram, nor is there any estimate of the amount (mole) of these phases. Metastable phases can be preferentially stabilized by suppressing/removing a set of stable phases while performing phase stability calculations [29,33,34,40]. Once a set of thermodynamically stable phases are suppressed/removed, we are left with the remaining phases of that system.…”

Section: Identification Of Stable and Metastable Phasesmentioning

confidence: 99%

“…A brief understanding of the formation of metastable phases for a given system can be helpful while designing experiments including heat treatment protocols. We have published articles on stabilizing metastable phases in aluminum alloys [29] and soft magnetic FINEMET-type alloys [33,34]. In soft magnetic alloys, we even performed heat treatment simulations for studying the nucleation and growth of metastable phases and some of the results were experimentally verified using advanced diagnostic tools such as atom probe tomography [34].…”

Section: Identification Of Stable and Metastable Phasesmentioning

confidence: 99%

“…DLANN models were used as a predictive tool and can be used on a computer and even on an Android device. As mentioned before, metastable phases are absent in the presence of stable phases while performing phase stability calculations under the framework of the CALPHAD approach [29,33,34,39]. We used the alloy composition and temperatures included in the dataset obtained from initial calculations containing only stable phases and then predicted metastable phases for these alloy compositions and temperatures through DLANN models.…”

Section: Dlann Modelmentioning

confidence: 99%

“…Our research team has significant experience in designing alloys by application of artificial intelligence (AI) algorithms on data generated from experiments and data generated under the framework of the CALPHAD approach. We have successfully designed titanium alloys [28], aluminum alloys [29,30], hard magnets (AlNiCo) [31,32], soft magnets (FINEMET type) [33,34], and Ni-based superalloys [35]. Thus, we propose this computational design approach, which can be easily adopted in other alloy systems and can help in developing ω-phase free Ti-based biomaterials with improved mechanical properties.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Discovery of New Ti-Based Alloys Aimed at Avoiding/Minimizing Formation of α” and ω-Phase Using CALPHAD and Artificial Intelligence

Jha

Dulikravich

2020

Metals

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In this work, we studied a Ti-Nb-Zr-Sn system for exploring novel composition and temperatures that will be helpful in maximizing the stability of β phase while minimizing the formation of α” and ω-phase. The Ti-Nb-Zr-Sn system is free of toxic elements. This system was studied under the framework of CALculation of PHAse Diagram (CALPHAD) approach for determining the stability of various phases. These data were analyzed through artificial intelligence (AI) algorithms. Deep learning artificial neural network (DLANN) models were developed for various phases as a function of alloy composition and temperature. Software was written in Python programming language and DLANN models were developed utilizing TensorFlow/Keras libraries. DLANN models were used to predict various phases for new compositions and temperatures and provided a more complete dataset. This dataset was further analyzed through the concept of self-organizing maps (SOM) for determining correlations between phase stability of various phases, chemical composition, and temperature. Through this study, we determined candidate alloy compositions and temperatures that will be helpful in avoiding/minimizing formation of α” and ω-phase in a Ti-Zr-Nb-Sn system. This approach can be utilized in other systems such as ω-free shape memory alloys. DLANN models can even be used on a common Android mobile phone.

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Section: Identification Of Stable and Metastable Phasesmentioning

confidence: 99%

Section: Identification Of Stable and Metastable Phasesmentioning

confidence: 99%

Section: Dlann Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Discovery of New Ti-Based Alloys Aimed at Avoiding/Minimizing Formation of α” and ω-Phase Using CALPHAD and Artificial Intelligence

Jha

Dulikravich

2020

Metals

Self Cite

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“…Stage 2: In this stage, we will use the information from the Stage 1 and make an attempt to design and perform heat treatment simulations under the framework of CALPHAD approach. Our group has expertise in performing heat treatment simulations in Thermo-Calc Software [23][24][25]. One of the challenges in performing heat treatment simulations in any CALPHAD-based tools is the estimation of interfacial energy between the matrix phase and the precipitate phase.…”

mentioning

confidence: 99%

Design of High Temperature Ti–Al–Cr–V Alloys for Maximum Thermodynamic Stability Using Self-Organizing Maps

Jha

Dulikravich

2019

Metals

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Data generated for the Ti–Al–Cr–V system of metallic alloys from our previous publication, where the composition of 102 alloys were computationally Pareto optimized with the objective of simultaneously maximizing the Young’s modulus and minimizing density for a range of temperatures, was the starting point of the current research, where compositions at different temperatures of these alloys were analyzed for phase stability in order to generate new data for compositions and volume fractions of stable phases at various temperatures. This resulted in a large dataset where a lot of data were still missing as all the phases are not stable at a given temperature for all the compositions. The concept of Self-Organizing Maps (SOM) was then applied to determine correlations between alloy compositions, stabilities of desired phases at various temperatures, associated Young’s moduli and densities, and the effect of the composition of phases on these properties. This work should help alloy designers to determine the required chemical composition of a new alloy with reference to the temperature of application of that alloy and see the effect of temperature and composition on stable phases and associated properties of alloys.

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In-situ porosity prediction in metal powder bed fusion additive manufacturing using spectral emissions: a prior-guided machine learning approach

Atwya

Panoutsos

2023

J Intell Manuf

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Numerous efforts in the additive manufacturing literature have been made toward in-situ defect prediction for process control and optimization. However, the current work in the literature is limited by the need for multi-sensory data in appropriate resolution and scale to capture defects reliably and the need for systematic experimental and data-driven modeling validation to prove utility. For the first time in literature, we propose a data-driven neural network framework capable of in-situ micro-porosity localization for laser powder bed fusion via exclusively within hatch strip of sensory data, as opposed to a three-dimensional neighborhood of sensory data. We further propose using prior-guided neural networks to utilize the often-abundant nominal data in the form of a prior loss, enabling the machine learning structure to comply more with process physics. The proposed methods are validated via rigorous experimental data sets of high-strength aluminum A205 parts, repeated k-fold cross-validation, and prior-guided validation. Using exclusively within hatch stripe data, we detect and localize porosity with a spherical equivalent diameter (SED) smaller than $$50.00\,\upmu $$ 50.00 μ m with a classification accuracy of $$73.13\pm 1.57\%$$ 73.13 ± 1.57 % This is the first work in the literature demonstrating in-situ localization of porosities as small as $$38.12\,\upmu m$$ 38.12 μ m SED and is more than a five-fold improvement on the smallest SED porosity localization via spectral emissions sensory data in the literature. In-situ localizing micro-porosity using exclusively within hatch-stripe data is a significant step towards within-layer defect mitigation, advanced process feedback control, and compliance with the reliability certification requirements of industries such as the aerospace industry.

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Combined machine learning and CALPHAD approach for discovering processing-structure relationships in soft magnetic alloys

Cited by 30 publications

References 46 publications

Discovery of New Ti-Based Alloys Aimed at Avoiding/Minimizing Formation of α” and ω-Phase Using CALPHAD and Artificial Intelligence

Discovery of New Ti-Based Alloys Aimed at Avoiding/Minimizing Formation of α” and ω-Phase Using CALPHAD and Artificial Intelligence

Design of High Temperature Ti–Al–Cr–V Alloys for Maximum Thermodynamic Stability Using Self-Organizing Maps

In-situ porosity prediction in metal powder bed fusion additive manufacturing using spectral emissions: a prior-guided machine learning approach

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