This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Multi Response Optimization on Machining Titanium Alloy Using Taguchi-DEAR Analysis in Abrasive Water Jet Cutting
Technical Paper
2019-28-0070
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
Abrasive water jet cutting has been proven to be an effective technology for processing various engineering materials. This paper investigated the effects of process parameters on depth of cut in abrasive water jet cutting of titanium alloy. Four different process parameters were undertaken for this study; water pressure, nozzle traverse speed, abrasive mass flow rate and standoff distance. The influence of these process parameters on depth of cut, surface roughness and MRR has been investigated and analyzed. An empirical model for the prediction of depth of cut in abrasive water jet cutting of cast iron has been developed using regression analysis. The approach is based on Taguchi-DEAR method to optimize the AWJM process parameter for effective machining. It has been found that the stand-off-distance has highest impact on performance measures among all process parameters.
Authors
Topic
Citation
Thangaraj, M., Loganathan, G., Atif, A., and Palanisamy, S., "Multi Response Optimization on Machining Titanium Alloy Using Taguchi-DEAR Analysis in Abrasive Water Jet Cutting," SAE Technical Paper 2019-28-0070, 2019, https://doi.org/10.4271/2019-28-0070.Data Sets - Support Documents
Title | Description | Download |
---|---|---|
Unnamed Dataset 1 | ||
Unnamed Dataset 2 | ||
Unnamed Dataset 3 | ||
Unnamed Dataset 4 | ||
Unnamed Dataset 5 |
Also In
References
- Li , H. and Wang , J. An Experimental Study of Abrasive Water Jet Machining of Ti-6Al-4V International Journal of Advanced Manufacturing Technology 81 1 361 369 2015 10.1007/s00170-015-7245-5
- Dhanawade , A. and Kumar , S. Multi-Performance Optimization of Abrasive Water Jet Machining of Carbon Epoxy Composite Material Indian Journal of Engineering and Materials Science 25 5 406 416 2018 http://nopr.niscair.res.in/handle/123456789/45869
- Patel , D. and Tandon , P. Experimental Investigations of Thermally Enhanced Abrasive Water Jet Machining of Hard-to-Machine Metals CIRP Journal of Manufacturing Science and Technology 10 1 92 101 2015 10.1016/j.cirpj.2015.04.002
- Kumar , N. and Shukla , M. Finite Element Analysis of Multi-Particle Impact on Erosion in Abrasive Water Jet Machining of Titanium Alloy Journal of Computational and Applied Mathematics 236 18 4600 4610 2012 10.1016/j.cam.2012.04.022
- Muthuramalingam and Mohan , B. Application of Taguchi-Grey Multi Responses Optimization on Process Parameters in Electro Erosion Measurement 58 1 495 502 2013 10.1016/j.measurement.2014.09.029
- Geethapriyan , T. , Kalaichelvan , K. , and Muthuramalingam , T. Multi Performance Optimization of Electrochemical Micro-Machining Process Surface Related Parameters on Machining Inconel 718 Using Taguchi-Grey Relational Analysis La Metallurgia Italiana 2016 4 13 19 2016
- Muthuramalingam , T. and Mohan , B. Taguchi-Grey Relational Based Multi Response Optimization of Electrical Process Parameters in Electrical Discharge Machining Indian Journal of Engineering and Materials Science 20 6 471 475 2013 http://nopr.niscair.res.in/handle/123456789/25590
- Simsek , B. , Ic , Y.T. , and Simsek , E.H. A RSM-Based Multi-Response Optimization Application for Determining Optimal Mix Proportions of Standard Ready-Mixed Concrete Arabian Journal for Science and Engineering 41 4 1435 1450 2016 10.1007/s13369-015-1987-0
- Afram , A. , Sharifi , F.J. , Fung , A.S. , and Raahemifar , K. Artificial Neural Network (ANN) Based Model Predictive Control (MPC) and Optimization of HVAC Systems: A State of the Art Review and Case Study of a Residential HVAC System Energy and Buildings 141 96 113 2017 10.1016/j.enbuild.2017.02.012
- Vasanth , S. , Muthuramalingam , T. , Vinothkumar , P. , Geethapriyan , T. et al. Performance Analysis of Process Parameters on Machining Titanium (Ti-6Al-4V) Alloy Using Abrasive Water Jet Machining Process Procedia CIRP 46 1 139 142 2016 10.1016/j.procir.2016.04.072
- Manoj , M. , Jinu , G.R. , and Muthuramalingam , T. Multi Response Optimization of AWJM Process Parameters on Machining TiB2 Particles Reinforced Al7075 Composite Using Taguchi-DEAR Methodology Silicon 10 5 2287 2293 2018 10.1007/s12633-018-9763-x