Experimental and computational investigation of drilling parameters and cooling conditions on super-duplex stainless steel 2507

This manuscript studies various process parameters like cutting velocity, feed rate, and cooling condition (dry, cryogenic, and emulsion) during drilling experiments performed on super-duplex stainless steel (SDSS) 2507 with coated and uncoated drill bits. The thrust force ranged from a minimum of 878.8 N (uncoated tool and emulsion cooling) to a maximum of 2500.1 N (uncoated tool and dry cooling), while torque values ranged from 19.87 to 2249.6 Nm under varying conditions. It was observed that thrust force and torque increased with higher feed rates and reduced with higher cutting velocities. Emulsion cooling consistently produced the lowest reactions, with an average reduction of 43% in thrust force compared to dry cooling, while dry drilling resulted in the highest forces and torques due to increased friction and heat. The use of a coated tool reduced the thrust force by up to 12% on average compared to uncoated tools under the same conditions. All three parameters studied (cutting velocity, feed rate and cooling condition) had statistically significant effects on thrust force (p ≤ 0.05), while torque was mainly influenced by feed rate and cooling condition, with a lesser effect from cutting velocity (p ≈ 0.06). The manuscript also presents the use of various computer-based modelling methods, including linear regression (R 2 = 0.9357 for thrust force), exponent-law relations (R 2 = 0.9854 for thrust force, 0.9375 for torque), artificial neural networks (mean error of 0.11% for thrust force, 0.3% for torque), and fuzzy-logic inference systems (R 2 = 0.9927 for thrust force, 0.9935 for torque), to model the outputs’ behaviour in relation to the input with remarkable precision.

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Publication Details

Journal
Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering
Published
2025-04-13
DOI
https://doi.org/10.1177/09544089251331267
Primary Topic
Advanced machining processes and optimization
Type
article
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article

Experimental and computational investigation of drilling parameters and cooling conditions on super-duplex stainless steel 2507

Achyuth Ramachandran, Anirudh Selvam, Amos Gamaleal David, Vimal Samsingh Ramalingam et al.
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Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering
Advanced machining processes and optimization
article

Experimental and computational investigation of drilling parameters and cooling conditions on super-duplex stainless steel 2507

Achyuth Ramachandran, Anirudh Selvam, Amos Gamaleal David, Vimal Samsingh Ramalingam, T. Kanagaraju
article en

Abstract

This manuscript studies various process parameters like cutting velocity, feed rate, and cooling condition (dry, cryogenic, and emulsion) during drilling experiments performed on super-duplex stainless steel (SDSS) 2507 with coated and uncoated drill bits. The thrust force ranged from a minimum of 878.8 N (uncoated tool and emulsion cooling) to a maximum of 2500.1 N (uncoated tool and dry cooling), while torque values ranged from 19.87 to 2249.6 Nm under varying conditions. It was observed that thrust force and torque increased with higher feed rates and reduced with higher cutting velocities. Emulsion cooling consistently produced the lowest reactions, with an average reduction of 43% in thrust force compared to dry cooling, while dry drilling resulted in the highest forces and torques due to increased friction and heat. The use of a coated tool reduced the thrust force by up to 12% on average compared to uncoated tools under the same conditions. All three parameters studied (cutting velocity, feed rate and cooling condition) had statistically significant effects on thrust force (p ≤ 0.05), while torque was mainly influenced by feed rate and cooling condition, with a lesser effect from cutting velocity (p ≈ 0.06). The manuscript also presents the use of various computer-based modelling methods, including linear regression (R 2 = 0.9357 for thrust force), exponent-law relations (R 2 = 0.9854 for thrust force, 0.9375 for torque), artificial neural networks (mean error of 0.11% for thrust force, 0.3% for torque), and fuzzy-logic inference systems (R 2 = 0.9927 for thrust force, 0.9935 for torque), to model the outputs’ behaviour in relation to the input with remarkable precision.

Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering
University of Stuttgart (DE), Stuttgart Technical University of Applied Sciences (DE), The University of Texas at Austin (US), Sri Sivasubramaniya Nadar College of Engineering (IN)
Openalex Percentile: Top 93%
Advanced machining processes and optimization
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