Integration of Hybrid Machine Learning and Multi-Objective Optimization for Enhanced Turning Parameters of EN-GJL-250 Cast Iron
This study aims to optimize the turning parameters for EN-GJL-250 grey cast iron using hybrid machine learning techniques integrated with multi-objective optimization algorithms. The experimental design focused on evaluating the impact of cutting tool type, testing three tools: uncoated and coated s...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Crystals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4352/15/3/264 |
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| Summary: | This study aims to optimize the turning parameters for EN-GJL-250 grey cast iron using hybrid machine learning techniques integrated with multi-objective optimization algorithms. The experimental design focused on evaluating the impact of cutting tool type, testing three tools: uncoated and coated silicon nitride (Si<sub>3</sub>N<sub>4</sub>) ceramic inserts and coated cubic boron nitride (CBN). Key cutting parameters such as depth of cut (<i>ap</i>), feed rate (<i>f</i>), and cutting speed (<i>Vc</i>) were varied to examine their effects on surface roughness (<i>Ra</i>), cutting force (<i>Fr</i>), and power consumption (<i>Pc</i>). The results showed that the coated Si<sub>3</sub>N<sub>4</sub> tool achieved the best surface finish, with minimal cutting force and power consumption, while the uncoated Si<sub>3</sub>N<sub>4</sub> and CBN tools performed slightly worse. Advanced optimization models including improved grey wolf optimizer–deep neural networks (DNN-IGWOs), genetic algorithm–deep neural networks (DNN-GAs), and deep neural network–extended Kalman filters (DNN-EKF) were compared with traditional methods like Support Vector Machines (SVMs), Decision Trees (DTs), and Levenberg–Marquardt (LM). The DNN-EKF model demonstrated exceptional predictive accuracy with an R<sup>2</sup> value of 0.99. The desirability function (DF) method identified the optimal machining parameters for the coated Si<sub>3</sub>N<sub>4</sub> tool: <i>ap</i> = 0.25 mm, <i>f</i> = 0.08 mm/rev, and <i>Vc</i> = 437.76 m/min. At these settings, <i>Fr</i> ranged between 46.424 and 47.405 N, <i>Ra</i> remained around 0.520 µm, and <i>Pc</i> varied between 386.518 W and 392.412 W. The multi-objective grey wolf optimization (MOGWO) further refined these parameters to minimize <i>Fr</i>, <i>Ra</i>, and <i>Pc</i>. This study demonstrates the potential of integrating machine learning and optimization techniques to significantly enhance manufacturing efficiency. |
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| ISSN: | 2073-4352 |