Optimizing wire-cut EDM parameters through evolutionary algorithm: a study for improving cost efficiency in turbo-machinery manufacturing

Sen, Binayak; Dasgupta, Archisman; Bhowmik, Abhijit

doi:10.1007/s12008-024-02001-y

Int J Interact Des Manuf

2024

DOI: 10.1007/s12008-024-02001-y

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Optimizing wire-cut EDM parameters through evolutionary algorithm: a study for improving cost efficiency in turbo-machinery manufacturing

Binayak Sen,

Archisman Dasgupta,

Abhijit Bhowmik

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2024

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Cited by 2 publications

References 33 publications

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Prediction of specific cutting energy consumption in eco-benign lubricating environment for biomedical industry applications: Exploring efficacy of GEP, ANN, and RSM models

Sen,

Bhowmik,

Prakash

et al. 2024

AIP Advances

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This study emphasizes the criticality of measuring specific cutting energy in machining Hastelloy C276 for biomedical industry applications, offering valuable insights into machinability and facilitating the optimization of tool selection, cutting parameters, and process efficiency. The research employs artificial intelligence-assisted meta-models for cost-effective and accurate predictions of specific cutting energy consumption. Comparative analyses conducted on Hastelloy C276, utilizing a TiAlN-coated solid carbide insert across various media (dry, MQL, LN2, and MQL+LN2), reveal the superiority of hybrid LN2+MQL in reducing specific cutting energy consumption. Subsequently, the analysis of variance underscores the cutting speed as the most influential parameter as compared to other inputs. Finally, a statistical evaluation compares the Gene Expression Programming (GEP) model against the Artificial Neural Network (ANN), and Response Surface Methodology model, demonstrating the superior predictive performance of the GEP meta-model. The GEP model demonstrates validation results with an error range of 0.25%–1.52%, outperforming the ANN and RSM models, which exhibit an error range of 0.49%–8.33% and 2.68%–10.18%, respectively. This study suggests the potential integration of contemporary intelligent methodologies for sustainable superalloy machining in biomedical industry applications, providing a foundation for enhanced productivity and reduced environmental impact of surgical instrument and biomedical device machining.

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Prediction of specific cutting energy consumption in eco-benign lubricating environment for biomedical industry applications: Exploring efficacy of GEP, ANN, and RSM models

Sen,

Bhowmik,

Prakash

et al. 2024

AIP Advances

View full text Add to dashboard Cite

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Application of Heuristic Methods for Polymer Synthesis Process Optimization

Miftakhov,

Mustafina,

Akimov

2024

Journal of Engineering

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A numerical algorithm has been developed for solving the problem of finding optimal conditions for the synthesis of polymers using heuristic optimization methods, and its effectiveness has been evaluated in the investigation of the copolymerization process of butadiene with styrene, proceeding via a free‐radical mechanism. To address the task of finding optimal synthesis conditions, a genetic algorithm has been proposed, and the main stages of its implementation using software development tools have been defined. For the mathematical description of the process, a kinetic approach based on the use of statistical moments of the molecular mass distribution has been employed, allowing for the derivation of a classical model description of the process in the form of a system of differential equations. Application of the genetic algorithm to this process has enabled the identification of the optimal regulator feeding regime, facilitating the attainment of the desired molecular product characteristics. The proposed algorithm is versatile, allowing for the search of one or multiple system parameters in both continuous and discrete variable sets, characteristic of optimizing technological parameters in industrial production.

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Optimizing wire-cut EDM parameters through evolutionary algorithm: a study for improving cost efficiency in turbo-machinery manufacturing

Cited by 2 publications

References 33 publications

Prediction of specific cutting energy consumption in eco-benign lubricating environment for biomedical industry applications: Exploring efficacy of GEP, ANN, and RSM models

Prediction of specific cutting energy consumption in eco-benign lubricating environment for biomedical industry applications: Exploring efficacy of GEP, ANN, and RSM models

Application of Heuristic Methods for Polymer Synthesis Process Optimization

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