Computational design and development of novel Al-Mg-Sc-Cr alloy

Purpose – 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. Design/methodology/approach – 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. Findings – 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. Research limitations/implications – 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. Practical implications – 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. Originality/value – 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.

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MCDM towards knowledge incorporation in ANN models for phase transformation in continuous cooling of steel

Multidiscipline Modeling in Materials and Structures ◽

10.1108/mmms-01-2018-0002 ◽

2019 ◽

Vol 15 (1) ◽

pp. 170-186 ◽

Cited By ~ 2

Author(s):

Subhamita Chakraborty ◽

Prasun Das ◽

Naveen Kumar Kaveti ◽

Partha Protim Chattopadhyay ◽

Shubhabrata Datta

Keyword(s):

Optimization Problem ◽

Multi Objective Optimization ◽

Pareto Solutions ◽

Single Model ◽

Content Type ◽

Multi Objective ◽

Ann Models ◽

Knowledge Incorporation ◽

Single Objective ◽

System Knowledge

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

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Optimal Structural Design of a Heat Sink With Laminar Single-Phase Flow Using Computational Fluid Dynamics-Based Multi-Objective Genetic Algorithm

Journal of Heat Transfer ◽

10.1115/1.4037643 ◽

2017 ◽

Vol 140 (2) ◽

Cited By ~ 7

Author(s):

Ya Ge ◽

Feng Shan ◽

Zhichun Liu ◽

Wei Liu

Keyword(s):

Genetic Algorithm ◽

Heat Sink ◽

Volumetric Flow Rate ◽

Pareto Solutions ◽

Nondominated Solutions ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Conflicting Objectives ◽

Order Preference ◽

General Method

This paper proposes a general method combining evolutionary algorithm and decision-making technique to optimize the structure of a minichannel heat sink (MCHS). Two conflicting objectives, the thermal resistance θ and the pumping power P, are simultaneously considered to assess the performance of the MCHS. In order to achieve the ultimate optimal design, multi-objective genetic algorithm is employed to obtain the nondominated solutions (Pareto solutions), while technique for order preference by similarity to an ideal solution (TOPSIS) is employed to determine which is the best compromise solution. Meanwhile, both the material cost and volumetric flow rate are fixed where this nonlinear problem is solved by applying the penalty function. The results show that θ of Pareto solutions varies from 0.03707 K W−1 to 0.10742 K W−1, while P varies from 0.00307 W to 0.05388 W, respectively. After the TOPSIS selection, it is found that P is significantly reduced without increasing too much θ. As a result, θ and P of the optimal MCHS determined by TOPSIS are 35.82% and 52.55% lower than initial one, respectively.

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Selection methods and diversity preservation in many-objective evolutionary algorithms

Data Technologies and Applications ◽

10.1108/dta-01-2018-0009 ◽

2018 ◽

Vol 52 (4) ◽

pp. 502-519 ◽

Cited By ~ 1

Author(s):

Luis Martí ◽

Eduardo Segredo ◽

Nayat Sánchez-Pi ◽

Emma Hart

Keyword(s):

Genetic Algorithm ◽

Evolutionary Algorithms ◽

Euclidean Distance ◽

Selection Methods ◽

Selection Scheme ◽

Content Type ◽

Multi Objective ◽

Pareto Optimal Set ◽

Diversity Preservation ◽

Promising Line

Purpose One of the main components of multi-objective, and therefore, many-objective evolutionary algorithms, is the selection mechanism. It is responsible for performing two main tasks simultaneously. First, it has to promote convergence by selecting solutions which are as close as possible to the Pareto optimal set. And second, it has to promote diversity in the solution set provided. In the current work, an exhaustive study that involves the comparison of several selection mechanisms with different features is performed. Particularly, Pareto-based and indicator-based selection schemes, which belong to well-known multi-objective optimisers, are considered. The paper aims to discuss these issues. Design/methodology/approach Each of those mechanisms is incorporated into a common multi-objective evolutionary algorithm framework. The main goal of the study is to measure the diversity preserved by each of those selection methods when addressing many-objective optimisation problems. The Walking Fish Group test suite, a set of optimisation problems with a scalable number of objective functions, is taken into account to perform the experimental evaluation. Findings The computational results highlight that the the reference-point-based selection scheme of the Non-dominated Sorting Genetic Algorithm III and a modified version of the Non-dominated Sorting Genetic Algorithm II, where the crowding distance is replaced by the Euclidean distance, are able to provide the best performance, not only in terms of diversity preservation, but also in terms of convergence. Originality/value The performance provided by the use of the Euclidean distance as part of the selection scheme indicates this is a promising line of research and, to the best of the knowledge, it has not been investigated yet.

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Multi-objective optimization of hydro-viscous flexible drive for dynamic characteristics using genetic algorithm

Industrial Lubrication and Tribology ◽

10.1108/ilt-12-2020-0472 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Jianzhong Cui ◽

Hu Li ◽

Dong Zhang ◽

Yawen Xu ◽

Fangwei Xie

Keyword(s):

Genetic Algorithm ◽

Dynamic Characteristics ◽

Transmission Efficiency ◽

Outer Radius ◽

Transmission Model ◽

Design Parameters ◽

Multi Objective Optimization ◽

Content Type ◽

Multi Objective ◽

Dynamic Transmission Model

Purpose The purpose of this study is to investigate the flexible dynamic characteristics about hydro-viscous drive providing meaningful insights into the credible speed-regulating behavior during the soft-start. Design/methodology/approach A comprehensive dynamic transmission model is proposed to investigate the effects of key parameters on the dynamic characteristics. To achieve a trade-off between the transmission efficiency and time proportion of hydrodynamic and mixed lubrication, a multi-objective optimization of friction pair system by genetic algorithm is presented to obtain the optimal combination of design parameters. Findings Decreasing the engagement pressure or the ratio of inner and outer radius, increasing the lubricating oil viscosity or the outer radius will result in the increase of time proportion of hydrodynamic and mixed lubrication, as well as the transmission efficiency and its maximum value. After optimization, main dynamic parameters including the oil film thickness, angular velocity of the driven disk, viscous torque and total torque show remarkable flexible transmission characteristics. Originality/value Both the dynamic transmission model and multi-objective optimization model are established to analyze the effects of main design parameters on the dynamic characteristics of hydro-viscous flexible drive.

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Multi-objective optimization and modeling of age hardening process using ANN, ANFIS and genetic algorithm: Results from aluminum alloy A356/cow horn particulate composite

Journal of Materials Research and Technology ◽

10.1016/j.jmrt.2019.01.031 ◽

2019 ◽

Vol 8 (3) ◽

pp. 3054-3075 ◽

Cited By ~ 8

Author(s):

Chidozie Chukwuemeka Nwobi-Okoye ◽

Basil Quent Ochieze ◽

Stanley Okiy

Keyword(s):

Genetic Algorithm ◽

Aluminum Alloy ◽

Particulate Composite ◽

Age Hardening ◽

Multi Objective Optimization ◽

Multi Objective ◽

Hardening Process

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Multi-Objective Genetic Algorithm Based Optimization of Age Hardening for AA6063 Alloy

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/912/5/052019 ◽

2020 ◽

Vol 912 ◽

pp. 052019

Author(s):

Aluru Praveen Sekhar ◽

Supriya Nandy ◽

Swati Dey ◽

Subhabrata Datta ◽

Debdulal Das

Keyword(s):

Genetic Algorithm ◽

Age Hardening ◽

Multi Objective ◽

Multi Objective Genetic Algorithm ◽

Aa6063 Alloy

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Design optimization for cogging torque mitigation in brushless DC motor using multi-objective particle swarm optimization algorithm

COMPEL The International Journal for Computation and Mathematics in Electrical and Electronic Engineering ◽

10.1108/compel-07-2014-0162 ◽

2015 ◽

Vol 34 (4) ◽

pp. 1302-1318 ◽

Cited By ~ 3

Author(s):

Umadevi Nagalingam ◽

Balaji Mahadevan ◽

Kamaraj Vijayarajan ◽

Ananda Padmanaban Loganathan

Keyword(s):

Genetic Algorithm ◽

Design Optimization ◽

Pareto Optimal ◽

Bldc Motor ◽

Swarm Optimization ◽

Cogging Torque ◽

Content Type ◽

Multi Objective ◽

Brushless Dc ◽

Design Variables

Purpose – The purpose of this paper is to propose a multi-objective particle swarm optimization (MOPSO) algorithm based design optimization of Brushless DC (BLDC) motor with a view to mitigate cogging torque and enhance the efficiency. Design/methodology/approach – The suitability of MOPSO algorithm is tested on a 120 W BLDC motor considering magnet axial length, stator slot opening and air gap length as the design variables. It avails the use of MagNet 7.5.1, a Finite Element Analysis tool, to account for the geometry and the non-linearity of material for assuaging an improved design framework and operates through the boundaries of generalized regression neural network (GRNN) to advocate the optimum design. The results of MOPSO are compared with Multi-Objective Genetic Algorithm and Non-dominated Sorting Genetic Algorithm-II based formulations for claiming its place in real world applications. Findings – A MOPSO design optimization procedure has been enlivened to escalate the performance of the BLDC motor. The optimality in design has been out reached through minimizing the cogging torque, maximizing the average torque and reducing the total losses to claim an increase in the efficiency. The results have been fortified in well-distributed Pareto-optimal planes to arrive at trade-off solutions between different objectives. Research limitations/implications – The rhetoric theory of multi objective formulations has been reinforced to provide a decisive solution with regard to the choice of the design obtained from Pareto-optimal planes. Practical implications – The incorporation of a larger number of design variables together with an orientation to thermal and vibration analysis will still go a long way in bringing on board new dimensions to the fold of optimality in the design of BLDC motors. Originality/value – The proposal offers a new perspective to the design of BLDC motor in the sense it be-hives the facility of a swarm based approach to optimize the parameters in order that it serves to improve its performance. The results of a 120 W motor in terms of lowering the losses, minimizing the cogging torque and maximizing the average torque emphasize the benefits of the GRNN based multi-objective formulation and establish its viability for use in practical applications.

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214 Discussion of Search Strategy for Multi-objective Genetic Algorithm with Consideration of Accuracy and Broadness of Pareto Solutions

The Proceedings of OPTIS ◽

10.1299/jsmeoptis.2008.8.219 ◽

2008 ◽

Vol 2008.8 (0) ◽

pp. 219-224

Author(s):

Masashi NISHIOKA ◽

Tomoyuki HIROYASU ◽

Mitsunori MIKI ◽

Hisatake YOKOUCHI

Keyword(s):

Genetic Algorithm ◽

Search Strategy ◽

Pareto Solutions ◽

Multi Objective ◽

Multi Objective Genetic Algorithm

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Multi-objective production-distribution planning based on vendor-managed inventory strategy in a supply chain

Industrial Management & Data Systems ◽

10.1108/imds-03-2015-0073 ◽

2015 ◽

Vol 115 (6) ◽

pp. 1086-1112 ◽

Cited By ~ 11

Author(s):

Amir Hossein Niknamfar

Keyword(s):

Genetic Algorithm ◽

Supply Chain ◽

Manufacturing Systems ◽

Demand Elasticity ◽

Vendor Managed Inventory ◽

Substantial Impact ◽

Content Type ◽

Total Cost ◽

Multi Objective ◽

Production Distribution

Purpose – The production-distribution (P-D) problems are two critical problems in many industries, in particular, in manufacturing systems and the supply chain management. In previous researches on P-D planning, the demands of the retailers and their inventory levels have less been controlled. This may lead into huge challenges for a P-D plan such as the bullwhip effects. Therefore, to remove this challenge, the purpose of this paper is to integrate a P-D planning and the vendor-managed inventory (VMI) as a strong strategy to manage the bullwhip effects in supply chains. The proposed P-D-VMI aims to minimize the total cost of the manufacturer, the total cost of the retailers, and the total distribution time simultaneously. Design/methodology/approach – This paper presents a multi-objective non-linear model for a P-D planning in a three-level supply chain including several external suppliers at the first level, a single manufacturer at the second level, and multi-retailer at the third level. A non-dominated sorting genetic algorithm and a non-dominated ranking genetic algorithm are designed and tuned to solve the proposed problem. Then, their performances are statistically analyzed and ranked by the TOPSIS method. Findings – The applicability of the proposed model and solution methodologies are demonstrated under several problems. A sensitivity analysis indicates the market scale and demand elasticity have a substantial impact on the total cost of the manufacturer in the proposed P-D-VMI. Originality/value – Although the P-D planning is a popular approach, there has been little discussion about the P-D planning based on VMI so far. The novelty comes from developing a practical and new approach that integrates the P-D planning and VMI.

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