Refine your search
Collections
Co-Authors
Journals
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Keerthana, B.
- Analysis of Surface Roughness, Material Removal Rate and Temperature in Milling of En 31 for Die Making under Minimum Quantity Lubirication Condition
Abstract Views :264 |
PDF Views:2
Authors
Affiliations
1 S V University, Tirupati, IN
1 S V University, Tirupati, IN
Source
Manufacturing Technology Today, Vol 16, No 2 (2017), Pagination: 11-17Abstract
Conventional milling process remains its importance as it is flexible in cutting dovetails, keyways and slots etc., although Non-traditional machining process are playing major role in machining industry. In other hand Minimum Quantity Lubrication (MQL) refers a little (50-5600ml/hr) lubricant dispensing system which is the substitute for the flood type lubricating system which dispenses 20 L/h of lubricant. In this work, milling experiments are conducted on En-31 material for making die using solid carbide tool and HSS tools under MQL condition. The effect of process parameters such as speed (S), depth of cut (DOC), coolant type (CT), tool material (TM), and amount of coolant dispensed (ACD) on cutting temperature, material removal rate and surface roughness is investigated. A special setup is prepared for regulating the minimum quantity of lubricant. The responses such as Temperature, Material Removal Rate and Surface Roughness are measured and regression models are developed for these responses. Regression models are solved using Particle Swarm Optimization (PSO) technique.Keywords
Milling Operation, Particle Swarm Optimization, En 31, MQL Setup.
References
- Anand Babu, K; Vijaya Kumar, G; Venkataramaiah, P: Prediction of Surface Roughness in Drilling of Al 7075/10% - SiCp Composite under MQL Condition using Fuzzy Logic, ‘Indian Journal of Science and Technology’, vol. 8, no. 12, 2015, 54074.
- Tunc, Lutfi Taner; Gu, Yuchen; Burke, Mary Grace: Effects Of Minimal Quantity Lubrication (MQL) On Surface Integrity In Robotic Milling Of Austenitic Stainless Steel, ‘Peer-review under responsibility of the scientific committee of the 3rd CIRP Conference on Surface Integrity (CIRP CSI)’, doi: 10.1016/j.procir.2016.
- Kedare, SB; Borse, DR; Shahane, PT: Effect of Minimum Quantity Lubrication (MQL) on Surface Roughness of Mild Steel of 15HRC on Universal Milling Machine, ‘3rd International Conference on Materials Processing and Characterization', ICMPC, 2014.
- Goyal, Anurag; Jasvir, S; Tiwana, Amrit Pal: A Review study on Minimum Quantity Lubrication in Machining, ‘International Journal of Recent Development in Engineering and Technology’, ISSN: 2347 - 6435, vol. 2, no. 5, May 2014.
- Hossain, Md. Shahriar Jahan; Ahmad, Nafis: A neuro-fuzzy approach to select cutting parameters for commercial die manufacturing,’ 10th International Conference on Mechanical Engineering, ICME 2013, 'Procedia Engineering', vol. 90, 2014, 753 - 759.
- Loginsa, Andris; Torimsa, Toms: The Influence of High-Speed Milling Strategies on 3D Surface Roughness Parameters, 25th DAAAM International Symposium on Intelligent Manufacturing and Automation, 2014, 'Procedia Engineering', vol. 100, 2015, 1253 - 1261.
- Jurkovic, J; Korosec, M; Kopac, J: New approach in tool wear measuring technique using CCD vision system,’ International Journal of Machine Tools and Manufacture’, vol. 45, no. 9, 2005, 1023-1030.
- Rajesh Choudhary; Kumar, H; Garg, RK: Analysis and Evaluation of Heat Affected Zones in Electric Discharge Machining of EN 31 Die Steel,’ Indian Journal of Engineering & Material Sciences’, vol. 17, 2010, 91-98.
- Dave, HK; Patel, LS; Raval, HK: Effect of machining conditions on MRR and surface roughness during CNC Turning of different Materials Using TiN Coated Cutting Tools – A Taguchi approach, ‘International Journal of Industrial Engineering Computations', vol. 3, no. 5, April 2012, 925-930.
- Arunkumar, N; Rawoof, Shareef Abdur H; Vivek, R: Investigation on the Effect of Process Parameters For Machining of EN31 (Air Hardened Steel) By EDM, ‘International Journal of Engineering Research and Applications (IJERA)’, ISSN: 2248-9622, www.ijera.com, vol. 2, no. 4, July-August 2012, 1111-1121.
- Tsai, CY; Yeh, SW: A Multiple objective particle swarm optimization approach for Inventory Classification,’ 'International Journal of Production Economics', vol. 114, no. 2, Feb 2008, 656-666.
- Sreenivasa Rao, M; Venkaiah, N: Parametric Optimization In Machining of Nimonic-263 Alloy Using RSM And Particle Swarm Optimization,’ 2nd International Conference on Nanomaterials and Technologies (CNT 2014)’, Procedia Materials Science 10, 2015, 70-79.
- Cus, F; Zuperl, U; Gecevska, V: High speed end-milling optimization using Particle Swarm Intelligence, ‘Journal of Achievements in Materials and Manufacturing Engineering’, www.journalamme.org, vol. 22, no. 2, June, 2007.
- Rajyalakshmi, G; Koushik, Thatipally; Kalyan, Tammu: Parametric Optimization In Wedm Using Particle Swarm Optimization Technique, ‘Proceedings of 56th IRF International Conference’, 29th May, 2016, Pune, India, ISBN: 978-93-86083-26-5.
- James, Kennedy; Russell, Eberhart: Particle Swarm Optimisation, ' Proceedings of IEEE International Conference on Neural Networks', vol. 4, 1942–1948. doi:10.1109/ICNN.1995.488968