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Keywords = coolin rate

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25 pages, 8440 KiB  
Article
Multi-Objective Optimization of Machinability and Energy Consumption of Cast Iron Depending on Cooling Rate
by Burak Öztürk and Fuat Kara
Machines 2025, 13(2), 84; https://doi.org/10.3390/machines13020084 - 23 Jan 2025
Cited by 4 | Viewed by 937
Abstract
Cooling rates in cast iron significantly impact its microstructure, leading to bainitic transformation instead of ferritic structures, resulting in microstructures with higher pearlite content and even cementite formation. Consequently, this transformation causes hardness values to vary between 160 and 320 HB, directly affecting [...] Read more.
Cooling rates in cast iron significantly impact its microstructure, leading to bainitic transformation instead of ferritic structures, resulting in microstructures with higher pearlite content and even cementite formation. Consequently, this transformation causes hardness values to vary between 160 and 320 HB, directly affecting the material’s machinability. Energy efficiency has become a critical focus in sustainable production techniques and cost-effective machining processes. This variation directly influences machinability, with higher hardness generally improving surface quality. Energy efficiency in machining is crucial for sustainable production, and Specific Cutting Energy Consumption (SCEC) has become a key metric in evaluating machinability. Using genetic algorithms (GA) and Response Surface Methodology (RSM), this study optimized machining parameters for energy consumption and surface finish. GA results indicated that a cutting speed of 200 m/min and a feed rate of 0.15 mm/rev minimized surface roughness to 1.359 Ra while reducing Specific Energy Consumption (SEC) from 3.25 to 2.83 Wh/mL. The lowest surface roughness (1.0 µm) was observed at a hardness of 320 HB, with the same cutting parameters. RSM analysis identified optimal parameters as a cutting speed of 150–200 m/min, a feed rate of 0.2 mm/rev, and a hardness of 220–245 HB, balancing energy efficiency and surface quality. ANOVA showed that cutting speed and feed rate contributed to 30% of the surface roughness variability and 45% of the energy consumption variability. Full article
(This article belongs to the Section Advanced Manufacturing)
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