Artificial Immune System Based Local Search for Solving Multi-Objective Design Problems
Issue:
Volume 3, Issue 3, June 2017
Pages:
29-35
Received:
24 October 2017
Accepted:
20 November 2017
Published:
14 December 2017
Abstract: In this paper, an artificial intelligent approach based on the clonal selection principle of Artificial Immune System (AIS) and local search (LS) is propose to solve Multiobjective engineering design problems. This paper presents an optimal design of a linear synchronous motor (LSM) considering two objective functions namely, maximum force and minimum saturation and then design of air-cored solenoid with maximum inductance and minimum volume as the objective functions. The proposed approach uses Local search, dominance principle and feasibility to identify solutions that deserve to be cloned.
Abstract: In this paper, an artificial intelligent approach based on the clonal selection principle of Artificial Immune System (AIS) and local search (LS) is propose to solve Multiobjective engineering design problems. This paper presents an optimal design of a linear synchronous motor (LSM) considering two objective functions namely, maximum force and mini...
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Application of Artificial Neural Network for Flow Stress Modelling of Steel
Sushant Rath,
Pinaki Talukdar,
Arujun Prasad Singh
Issue:
Volume 3, Issue 3, June 2017
Pages:
36-39
Received:
27 October 2017
Accepted:
20 November 2017
Published:
14 December 2017
Abstract: The hot rolling mills of steel plants are in the process of transformation from manual operation to artificial intelligence (AI) based automatic operations. Most of the mill input parameters required by the automation system are recorded from different sensors installed in the mill except the flow stress of rolled material. Generally a semi-empirical equation is used that correlate flow stress with strain, strain rate and temperature during rolling. The coefficients and exponents of the empirical equations are calculated from experimental data with parameter estimation techniques. This paper discusses the application of artificial neural network (ANN) for calculation of flow stress of material from experimental data. Experiments were conducted in a dynamic thermo-mechanical simulator to measure flow stress of steel at different strain, strain rate and temperature. The experimental data was used to calculate coefficients of empirical equations using multivariable optimization techniques. The data was also used to formulate an ANN model using feed forward network. The ANN model was trained with backpropagation algorithm. The ANN method is found to be more accurate than the semi-empirical equations for correlating the flow stress with strain, strain rate and temperature.
Abstract: The hot rolling mills of steel plants are in the process of transformation from manual operation to artificial intelligence (AI) based automatic operations. Most of the mill input parameters required by the automation system are recorded from different sensors installed in the mill except the flow stress of rolled material. Generally a semi-empiric...
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