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Pradeep Kumar, M.
- Finite Element Analysis of Orthogonal Cutting of AISI 1045 Steel under Dry and Cryogenic Condition
Abstract Views :169 |
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Affiliations
1 Dept. of Mechanical Engg., CEG, Anna University, Chennai, IN
1 Dept. of Mechanical Engg., CEG, Anna University, Chennai, IN
Source
Manufacturing Technology Today, Vol 8, No 2 (2009), Pagination: 26-30Abstract
The major needs in machining are high material removal rate, good surface finish and low tool wear. These objectives can be achieved by reducing the cutting temperature. Cryogenic cooling is an environmental friendly clean technology for desirable control of cutting temperature. In this study, an attempt is made to develop a finite element model for orthogonal cutting operation of AISI 1045 steel at different speed-feed combinations under dry and cryogenic cutting conditions. The FEA simulated cutting temperature, cutting force, chip thickness and shear angle of dry and cryogenic cutting were compared. The present work shows the substantial benefit of cryogenic cooling on cutting temperature up to 30% over dry machining. Application of this cryogenic cooling was found to be more effective in cutting speed rather than a feed rate.- Comparison of Different Flow Stress Models of the Orthogonal Metal Cutting Process Using Finite Element Analysis
Abstract Views :151 |
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Authors
Affiliations
1 Dept. of Mech. Engg., CEG, IN
2 Dept. of Production Engg, MIT, Anna University, Chennai, IN
1 Dept. of Mech. Engg., CEG, IN
2 Dept. of Production Engg, MIT, Anna University, Chennai, IN
Source
Manufacturing Technology Today, Vol 7, No 5 (2008), Pagination: 21-26Abstract
This paper presents a comparative study of the effect of two different flow stress models on the orthogonal metal cutting process using the FEM. The flow stress models used were the Oxley's model and the modified Johnson and Cook model. The orthogonal cutting experiments were conducted with AISI 1045 steel work material and tungsten carbide cutting tool. The FEA results with modified Johnson's model for the cutting force, feed force, chip thickness, shear angle and shear strain compared well with the experimental values with only a marginal deviation of 10-20% for feed rates of 0.16mm/rev and above while the deviation for Oxley's model was higher across most feed rates. The Oxley model gave better results for the FE predictions of contact temperature. This study suggested that the modified Johnson and Cook model was an efficient alternative tool for FE simulation of the orthogonal metal cutting process.- Orthogonal Cutting Investigations on Ti64 Alloy Machining by Using Artificial Neural Networks
Abstract Views :174 |
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Authors
Affiliations
1 Dept. of Mechanical & Prod. Engg., Sathyabama University, Chennai, IN
2 Dept. of Mechanical Engg., Anna University, Chennai, IN
1 Dept. of Mechanical & Prod. Engg., Sathyabama University, Chennai, IN
2 Dept. of Mechanical Engg., Anna University, Chennai, IN
Source
Manufacturing Technology Today, Vol 7, No 1 (2008), Pagination: 22-27Abstract
Titanium and its alloys are attractive materials due to their unique high strength to weight ratio and their exceptional corrosion resistance. This paper combines the predictive machining approach with neural network modeling of cutting parameters. Experimental work has been performed in orthogonal cutting of Ti-6Al-4V using Plain Carbide tool. At the selected cutting conditions the forces have been measured. The experimental data were utilized to train the developed simulation environment based on back propagation neural network modeling. The cutting speed, feed, depth of cut have been considered as the input parameters and cutting force, feed force as output parameters to develop the model. The trained neural network was used in predicting the cutting parameters. Predictive ANN models were found to be capable of better predictions of forces about 93 to 97% accuracy within the range that they had been trained.- Finite Element Analysis of Orthogonal Machining with Grooved Tool
Abstract Views :165 |
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Authors
Affiliations
1 Department of Mechanical Engineering, Anna University, Chennai, IN
2 Department of Production Technology, MIT, Anna University, Chennai, IN
1 Department of Mechanical Engineering, Anna University, Chennai, IN
2 Department of Production Technology, MIT, Anna University, Chennai, IN