Self-organizing maps for pattern recognition in design of alloys

Abstract

A combined experimental-computational methodology for accelerated design of AlNiCo-type permanent magnetic alloys is presented with the objective of simultaneously extremizing several magnetic properties. Chemical concentrations of eight alloying elements were initially generated using a quasirandom number generator so as to achieve a uniform distribution in the design variable space. It was followed by manufacture and experimental evaluation of these alloys using an identical thermo-magnetic protocol. These experimental data were used to develop meta-models capable of directly relating the chemical composition with desired macroscopic properties of the alloys. These properties were simultaneously optimized to predict chemical compositions that result in improvement of properties. These data were further utilized to discover various correlations within the experimental dataset by using several concepts of artificial intelligence. In this work, an unsupervised neural network known as selforganizing maps was used to discover various patterns reported in the literature. These maps were also used to screen the composition of the next set of alloys to be manufactured and tested in the next iterative cycle. Several of these Pareto-optimized predictions out-performed the initial batch of alloys. This approach helps significantly reducing the time and the number of alloys needed in the alloy development process.

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

“…All of these columns are interrelated. Thus, we chose Self-organizing Maps (SOM) algorithm [20] in our work as it is known for capturing the topology of the multidimensional data sets.…”

“…The SOM algorithm is a classification technique that is based upon an unsupervised artificial neural network, popularly known as self-organizing feature maps (SOM) [17][18][19][20][21][22], which was popularized by Teuvo Kohonen in the 1980s. SOM implements a term competitive learning along with a neighborhood function to preserve the topological properties of the dataset [21].…”

“…This makes SOM a perfect tool to visualize high-dimensional datasets in lower dimensions-usually two to three-while preserving the topology for determining various correlations within the dataset [18]. SOMs can be considered as a nonlinear generalization of principal component analysis (PCA), an unsupervised machine learning method [20,22]. Recent studies demonstrated the advantage of using SOM over PCA [10,14].…”

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

“…Data obtained from CALPHAD-based calculations and predicted through DLANN models were further studied by the concept of self-organizing maps (SOM) [25,28,31,42]. SOM maps are known for preserving topology of the data, which is helpful in determining various correlations in the dataset among concentrations of alloying element, temperature, and stability of various stable and metastable phases.…”

“…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.…”