A standard deviation based firefly algorithm for multi-objective optimization of WEDM process during machining of Indian RAFM steel

• Published in: Neural computing & applications Vol. 29; no. 3; pp. 665 - 677
• Main Authors: Majumder, Arindam, Das, Argha, Das, Pankaj Kr
• Format: Journal Article
• Language: English
• Published: London Springer London 01.02.2018; Springer Nature B.V
• Subjects: Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Cutting speed; Data Mining and Knowledge Discovery; Evolutionary algorithms; Ferritic stainless steels; Heuristic methods; Image Processing and Computer Vision; Machining; Martensitic stainless steels; Mathematical models; Multiple objective analysis; Optimization algorithms; Original Article; Particle swarm optimization; Probability and Statistics in Computer Science; Process parameters; Product quality; Productivity; Regression analysis; Standard deviation; Surface roughness; Reduced activation ferritic martensitic steel; Multi-objective optimization; Standard deviation method; Wire cut EDM; Firefly algorithm
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-016-2471-9

Non-conventional machining processes always suffer due to their low productivity and high cost. However, a suitable machining process should improve its productivity without compromising product quality. This implies the necessity to use efficient multi-objective optimization algorithm in non-conventional machining processes. In this present paper, an effective standard deviation based multi-objective fire-fly algorithm is proposed to predict various process parameters for maximum productivity (without affecting product quality) during WEDM of Indian RAFM steel. The process parameters of WEDM considered for this study are: pulse current ( I ), pulse-on time ( T on ), pulse-off time ( T off ) and wire tension (WT).While, cutting speed (CS) and surface roughness (SR) were considered as machining performance parameters. Mathematical models relating the process and response parameters had been developed by linear regression analysis and standard deviation method was used to convert this multi objective into single objective by unifying the responses. The model was then implemented in firefly algorithm in order to optimize the process parameters. The computational results depict that the proposed method is well capable of giving optimal results in WEDM process and is fairly superior to the two most popular evolutionary algorithms (particle swarm optimization algorithm and differential evolution algorithm) available in the literature.