OPTIMIZATION OF MACHINING PERFORMANCE IN DRY END MILLING OF INCONEL 601.

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Bibliographic Details
Title:	OPTIMIZATION OF MACHINING PERFORMANCE IN DRY END MILLING OF INCONEL 601.
Authors:	Vukelic, D.¹ ukelic@uns.ac.rs, Milosevic, A.¹, Kanovic, Z.¹, Sokac, M.¹, Santosi, Z.¹, Simunovic, G.²
Source:	International Journal of Simulation Modelling (IJSIMM). Jun2026, Vol. 25 Issue 2, p294-305. 12p.
Subjects:	Surface roughness, Multi-objective optimization, Milling cutters, Nickel-chromium alloys, High-speed machining
Abstract:	This study investigates the influence of helix angle, cutting speed, feed per tooth, radial depth of cut and axial depth of cut on surface roughness and material removal rate during dry end milling of Inconel 601. An I-optimal design of experiments was employed to develop a quadratic regression-based simulation model for surface roughness, while material removal rate was determined analytically. Multi-objective optimization was performed to maximize material removal rate while satisfying target surface roughness levels corresponding to previously defined machining quality grades. The results indicate that higher helix angles, moderate cutting speeds, lower feed per tooth and lower depths of cut lead to improved surface roughness. Conversely, a reduction in the stringency of surface roughness requirements enables a significant increase in productivity by permitting the use of more aggressive milling parameters, including higher cutting speeds, feed per tooth and depths of cut. Confirmation experiments demonstrated good agreement between predicted and measured surface roughness values, with low prediction errors, confirming high predictive accuracy and model stability. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	This study investigates the influence of helix angle, cutting speed, feed per tooth, radial depth of cut and axial depth of cut on surface roughness and material removal rate during dry end milling of Inconel 601. An I-optimal design of experiments was employed to develop a quadratic regression-based simulation model for surface roughness, while material removal rate was determined analytically. Multi-objective optimization was performed to maximize material removal rate while satisfying target surface roughness levels corresponding to previously defined machining quality grades. The results indicate that higher helix angles, moderate cutting speeds, lower feed per tooth and lower depths of cut lead to improved surface roughness. Conversely, a reduction in the stringency of surface roughness requirements enables a significant increase in productivity by permitting the use of more aggressive milling parameters, including higher cutting speeds, feed per tooth and depths of cut. Confirmation experiments demonstrated good agreement between predicted and measured surface roughness values, with low prediction errors, confirming high predictive accuracy and model stability. [ABSTRACT FROM AUTHOR]
ISSN:	17264529
DOI:	10.2507/IJSIMM25-2-769