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The purpose of this paper is to incorporate prior knowledge in the artificial neural network (ANN) model for the prediction of continuous cooling transformation (CCT) diagram of steel, so that the model predictions become valid from materials engineering point of view.

Genetic algorithm (GA) is used in different ways for incorporating system knowledge during training the ANN. In case of training, the ANN in multi-objective optimization mode, with prediction error minimization as one objective and the system knowledge incorporation as the other, the generated Pareto solutions are different ANN models with better performance in at least one objective. To choose a single model for the prediction of steel transformation, different multi-criteria decision-making (MCDM) concepts are employed. To avoid the problem of choosing a single model from the non-dominated Pareto solutions, the training scheme also converted into a single objective optimization problem.

The prediction results of the models trained in multi and single objective optimization schemes are compared. It is seen that though conversion of the problem to a single objective optimization problem reduces the complexity, the models trained using multi-objective optimization are found to be better for predicting metallurgically justifiable result.

ANN is being used extensively in the complex materials systems like steel. Several works have been done to develop ANN models for the prediction of CCT diagram. But the present work proposes some methods to overcome the inherent problem of data-driven model, and make the prediction viable from the system knowledge.

The purpose of this paper is to design and develop precipitation hardened Al-Mg alloy imparting enhanced strength with acceptable ductility through minor addition of Sc and Cr by using multi-objective genetic algorithm-based searching. In earlier attempts of strengthening aluminum alloys, owing to the formation of Al₃Sc and Al₇Cr phase, addition of Sc and Cr have yielded attractive precipitation hardening, respectively. Both the Al-Sc and Al-Cr system are quench sensitive due to presence of a sloping solvus in their phase diagrams. It is also known that both the Al₃Sc and Al₇Cr phases nucleate directly from the supersaturated solid solution without formation of GP-zones or transient phases prior to the formation of the Al₃Sc and Al₇Cr. Sc also found to have beneficial effect on the corrosion property of such alloys. In view of the above, it is of interest to explore the possibility of enhancing the age hardening effect in Al-Mg alloy by addition of Sc and Cr.

The paper uses an approach where experimental information of two different alloy systems (namely, Al-Mg-Sc and Al-Cr) has been combined to generate a single database involving the potential features of both the systems with the aim to formulate the suitable artificial neural network (ANN) models for strength and ductility. The models are used as the objective functions for the optimization process. The patterns of the optimized Pareto front are analyzed to recognize the optimal property of the alloy system. The hitherto unexplored Al-Mg-Sc-Cr alloy, designed from the Pareto solutions and suitably modified on the basis of prior knowledge of the system, is then synthesized and characterized.

The paper has demonstrated the ANN- and genetic algorithm (GA)-based design of a hitherto unexplored alloy by utilizing the existing information concerning the component alloy systems. The paper also established that analyses of the Pareto solutions generated through multi-objective optimization using GA provide an insight of the variation of the parameters at different combination of strength and ductility. It also revealed that the Al-Mg-Sc-Cr alloy has exhibited a two-stage age hardening effect. The first and second stages are due to the precipitation of Al₃Sc and Al₇Cr phases, respectively.

In the present study the two alloy systems are used in tandem to develop models to describe the properties involving the distinct mechanistic features of phase evolution inherent in both the systems. Though the ANN models having the capability to capture huge non-linearity of a system have been employed to predict the convoluted effects of those characteristics when an alloy containing Mg, Sc and Cr are added simultaneously, but the ANN models predictions can be checked experimentally by the future researchers.

The paper demonstrates the role of scandium and chromium addition on the ageing characteristics of the alloy by analyzing the age hardening behavior of the designed alloy in cast and cold rolled condition clearly.

The approach stated in this paper is a novel one, in the sense that experimental data of two different alloy systems have been clubbed to generate a single database with the aim to formulate the suitable ANN models for strength and ductility.

The purpose of this paper is to develop an automated phase segmentation model from complex microstructure. The mechanical and physical properties of metals and alloys are influenced by their microstructure, and therefore the investigation of microstructure is essential. Coexistence of random or sometimes patterned distribution of different microstructural features such as phase, grains and defects makes microstructure highly complex, and accordingly identification or recognition of individual phase, grains and defects within a microstructure is difficult.

In this perspective, computer vision and image processing techniques are effective to help in understanding and proper interpretation of microscopic image. Microstructure-based image processing mainly focuses on image segmentation, boundary detection and grain size approximation. In this paper, a new approach is presented for automated phase segmentation from 2D microstructure images. The benefit of the proposed work is to identify dominated phase from complex microstructure images. The proposed model is trained and tested with 373 different ultra-high carbon steel (UHCS) microscopic images.

In this paper, Sobel and Watershed transformation algorithms are used for identification of dominating phases, and deep learning model has been used for identification of phase class from microstructural images.

For the first time, the authors have implemented edge detection followed by watershed segmentation and deep learning (convolutional neural network) to identify phases of UHCS microstructure.