Effect of Dry and Wet Machining Environments on Surface Quality of Al6061 Using Particle Swarm Optimization (PSO)

Aluminum, one of the most abundant metals found on our planet, plays a crucial role in manufacturing as it is lightweight and resistant to corrosion and has excellent machinability. Of its numerous alloys, Al6061 is one of the most popular alloys used for CNC machining due to its superior mechanical and processing properties. This paper aims to investigate the impact of machining under dry and wet machining conditions. Correspondingly, the impact of dry machining on the material removal rate (MRR) and surface roughness (Ra) of Al6061 was evaluated. Machining was performed on a CNC Lathe. Two rods of Al6061 were used, and a dynamometer was attached to them to measure the radial, thrust, and tangential forces. In wet machining, the coolant used was a mixture of cutting oil and water. Different RPMs, feed rates, and depths of cut were entered into the machine as parameters. And the optimum parameters where found. This research utilizes particle swarm optimization approaches in order to evaluate optimal parameters, in contrast to traditional measurement methods such as contact profilometry or cutting force measurement. The results indicate that surface roughness rises with the depth of cut and feed rate. Ra rises by about 200% when dry machining is conducted at 0.05 mm/rev with increased depths of cut from 0.5 mm to 2.5 mm. In wet machining, the rise is much smaller, approximately 67% at 0.05 mm/rev and 30% at 0.25 mm/rev. Wet machining always produces more finished surfaces, decreasing Ra by 22–25% over dry machining. Wet machining is therefore better suited for achieving high-quality surface finish in Al6061 machining.

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Published
2025-11-10
DOI
https://doi.org/10.3390/engproc2025114021
Primary Topic
Advanced machining processes and optimization
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article
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article

Effect of Dry and Wet Machining Environments on Surface Quality of Al6061 Using Particle Swarm Optimization (PSO)

Mahendra Gadge, Mahendra U. Gaikwad, Himadri Majumder, Avinash A. Somatkar et al.
Advanced machining processes and optimization
article

Effect of Dry and Wet Machining Environments on Surface Quality of Al6061 Using Particle Swarm Optimization (PSO)

Mahendra Gadge, Mahendra U. Gaikwad, Himadri Majumder, Avinash A. Somatkar, Abhishek Shinde, Atharv V. Lohakare
article en

Abstract

Aluminum, one of the most abundant metals found on our planet, plays a crucial role in manufacturing as it is lightweight and resistant to corrosion and has excellent machinability. Of its numerous alloys, Al6061 is one of the most popular alloys used for CNC machining due to its superior mechanical and processing properties. This paper aims to investigate the impact of machining under dry and wet machining conditions. Correspondingly, the impact of dry machining on the material removal rate (MRR) and surface roughness (Ra) of Al6061 was evaluated. Machining was performed on a CNC Lathe. Two rods of Al6061 were used, and a dynamometer was attached to them to measure the radial, thrust, and tangential forces. In wet machining, the coolant used was a mixture of cutting oil and water. Different RPMs, feed rates, and depths of cut were entered into the machine as parameters. And the optimum parameters where found. This research utilizes particle swarm optimization approaches in order to evaluate optimal parameters, in contrast to traditional measurement methods such as contact profilometry or cutting force measurement. The results indicate that surface roughness rises with the depth of cut and feed rate. Ra rises by about 200% when dry machining is conducted at 0.05 mm/rev with increased depths of cut from 0.5 mm to 2.5 mm. In wet machining, the rise is much smaller, approximately 67% at 0.05 mm/rev and 30% at 0.25 mm/rev. Wet machining always produces more finished surfaces, decreasing Ra by 22–25% over dry machining. Wet machining is therefore better suited for achieving high-quality surface finish in Al6061 machining.

International Institute of Information Technology (IN), Tecnológico Nacional de México (MX), Raisoni Group of Institutions (IN)
Openalex Percentile: Top 62%
Advanced machining processes and optimization
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