2. Materials and Methods
2.1. Identification of Stable and Metastable Phases
2.2. Deep Learning Artificial Neural Network (DLANN) Model
2.3. Self Organizing Maps (SOM)
2.4. Computational Infrastructure
2.4.1. CALPHAD-Based Work
2.4.2. Artificial Intelligence-Based Work
3.1. Stability of Stable and Metastable Phases
3.2. DLANN Model
3.3. Self-Organizing Maps (SOM)
- Develop predictive models for Young’s modulus of new proposed alloys through the CALPHAD approach and AI algorithms.
- Study kinetics of precipitation of various stable and metastable phases within the framework of the CALPHAD approach and work with solidification simulation to have a better understanding of precipitation of various stable and metastable phases for different cooling rates. Thereafter, study precipitation kinetics of nucleation and growth of various phases.
- This study will be helpful for understanding micro-segregation, especially for cast prosthetics [13,44,45,46]. Studies have shown that during solidification, it is difficult to avoid composition variation in the inter-dendritic region due to solute entrapment, which thus makes the casting composition non-homogeneous . Micro-segregation can be controlled by properly choosing the cooling rate [13,44]. Thus, solidification simulation will be helpful in understanding the temperature regimes where a certain desired or undesired phase is stable . This way, one should be able to design a cooling rate that is fast enough to avoid ageing in the temperature regimes where undesired phases are unstable.
- Heat-treatment simulations are equally important . Some of these alloys are subjected to ageing at a defined temperature for a prolonged time (several hours). Through heat treatment simulations, one can obtain an estimate of the grain size and volume fractions of a desired phase and observe its growth over time. Grain size and volume fraction affect the Young’s modulus of an alloy, so this study is important.
- Simulate microstructure evolution, micro-segregation, composition variation in the inter-dendritic regions [47,48,49] under the framework of the CALPHAD and phase field approach [47,48,49].
- The phase field approach is a popular approach for simulating microstructure evolution. A user can get insights required for the understanding of the solidification process and can study the growth of dendrites and composition variation in inter-dendritic regions, which is important for addressing micro-segregation [47,48,49]. The CALPHAD approach will be used for providing vital information on thermodynamics and kinetics to the phase-field models especially regarding the sequence of precipitation of a phase as well as stability of various phases . The CALPHAD approach also provides the grain size, and this information can be used to calibrate the phase field model .
- Design new manufacturing protocols with special emphasis on additive manufacturing [50,51,52,53,54,55,56].
- Several modes of designing new parts through additive manufacturing exist, such as selective laser beam, electron beam, etc. [50,51,52,56]. All of these methods have advantages and limitations [50,51,52,56]. Optimization of operation parameters plays a vital role in achieving targeted properties of a prosthetic/implant manufactured by additive manufacturing [50,53].
- CALPHAD, and the phase field approach have been used for studying microstructure evolution for additively manufactured parts [47,48,49]. AI algorithms have been used to study data and develop inexpensive predictive models within the framework of additive manufacturing . We plan to work on these topics.
- Finally, the most important characteristic of an implant is its biocompatibility, and osteointegration [55,58,59,60,61,62]. Several coatings have been developed and there is always room for improvement [55,58,59,60,61,62]. We plan to use AI-based tools to understand these coatings and possibly design new coatings with enhanced biocompatibility and osteointegration.
- Data for various stable and metastable phases were generated for about 3000 composition and temperature values of a Ti-Nb-Zr-Sn system through the commercial software Thermo-Calc and TCTI database for titanium alloys.
- Phase stability data were used for developing deep learning artificial neural network (DLANN) models for various phases as a function of alloy composition and temperature. DLANN models were used to predict the concentrations of phases for new compositions and temperatures. DLANN models can be used on a personal computer and even on an Android phone.
- The SOM algorithm was used to determine correlations among alloying elements, temperature, and various stable and metastable phases.
- Finally, we predicted compositions of five select alloys that are expected to meet our expectations regarding the phase stability of β phase.
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|Error Metrics (Validation Set)|
|Mean Absolute Error (MAE)||Mean Squared Error (MSE)|
|Quantization Error||Topological Error|
|Alloy No.||Ti (Mole %)||Nb (Mole %)||Zr (Mole %)||Sn (Mole %)||Temp. (K)|
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