E3S Web Conf.
Volume 309, 20213rd International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2021)
|Number of page(s)||7|
|Published online||07 October 2021|
- I.D. Marinescu, M.P. Hitchiner, E. Uhlmann, W.B. Rowe, I. Inasaki, Handbook of machining with grinding wheels, CRC Press, (2006). [Google Scholar]
- S. Malkin, C. Guo, Grinding technology: Theory and Applications of Machining with Abrasives (2nd Edition), New York: Industrial Press, (2008). [Google Scholar]
- D. D. Trung, Influence of Cutting Parameters on Surface Roughness in Grinding of 65G Steel, Tribology in Industry, 43(1), 167–176, (2021), doi: 10.24874/ti.1009.11.20.01 [Google Scholar]
- S. M. Deshmukh, R. D. Shelke, C. V. Bhusare, Optimization of Cylindrical Grinding Process Parameters of Hardened Material using Response Surface Methodology, International Journal of Innovative Science, Engineering & Technology, 3(11), 216–219, (2016). [Google Scholar]
- M.S. Phadke, Quality Engineering Using Robust Design, Printice Hall, (1989). [Google Scholar]
- S.K. Karna, R.V. Singh, R. Sahai, Application of Taguchi Method in Indian Industry, International Journal of Emerging Technology and Advanced Engineering, 2(11), 387–391, (2012). [Google Scholar]
- Swati S Sangale, A. D. Dongare, Optimization of the parameter in cylindrical grinding of mild steel rod (EN19) by Taguchi method, International Journal of Creative and Innovative Research in All Studies, 2(4), 67–73, (2019). [Google Scholar]
- M. Ganesan, S. Karthikeyan, N. Karthikeyan, Prediction and Optimization of Cylindrical Grinding Parameters for Surface Roughness Using Taguchi Method, IOSR Journal of Mechanical and Civil Engineering, 39–46, (2014). [Google Scholar]
- S. Deva Prasad, S. Vishal, P. Vishwa Sai, K. Bhargav, K. Rohith, Cylindrical grinding - experimental investigation and Taguchi study of process parameters on EN31 and mild steel, IOP Conf. Series: Materials Science and Engineering, Vol. 1057, No. 012066, pp.1–16, (2021), doi:10.1088/1757-899X/1057/1/012066 [Google Scholar]
- Rupesh J. Karande, S.M. Jadhav, Kshitij R. Patil, R. K. Nanwatkar, Optimization of Cylindrical Grinding Machine Parameters for Minimum Surface Roughness and Maximum MRR, Global Research and Development Journal for Engineering, 2(5), 62–68, (2017). [Google Scholar]
- Kshitij R Patil, Rupesh J Karande, Dadaso D. Mohite, Vishwas S Jadhav, Modeling and optimization of cylindrical grinding parameters for MRR and surface roughness, International journal of engineering sciences & research technology, 6(4), 498–503, (2017). [Google Scholar]
- Mukesh Kumar, Sukhjinder Singh, Khushdeep Goyal, To study the effect of grinding parameters on surface roughness and material removal rate of cylindrical of heat treated EN 47 steel, Journal of Mechanical Engineering, 45(2), 81–88, (2015). [Google Scholar]
- A. Babu, I.Balaguru, A.K. Shaik Dawood, Prediction and Optimization of Process Parameters for Cylindrical Grinding of Inconel 718 Alloy using Taguchi Approach, International Journal of Engineering Research & Technology, 3(26), 1–5, (2015). [Google Scholar]
- Dinesh Kumar Patel, Deepam Goyal, B. S. Pabla, Optimization of parameters in cylindrical and surface grinding for improved surface fnish, Royal society open science, 5(171906), 1–11, (2017). [Google Scholar]
- Gaurav Upadhyay, Ramprasad, Kamal Hassan, Optimization of Metal Removal Rateon Cylindrical Grinding for IS319 Brass Using Taguchi Method, International Journal of Engineering Research and Applications, 5(6), 63–67, (2015). [Google Scholar]
- K. Mekala, J. Chandradas, K. Chandrasekaran, T. T. M. Kannan, E. Ramesh, R. Narasing Babu, Optimization of cylindrical grinding parameters of austenitic stainless steel rods (AISI 316) by Taguchi method, International journal of mechanical engineering and robotics research, 3(2), 208–215, (2014). [Google Scholar]
- Karlapudi Gowtham, Sri. G. Gopi Nath, Optimization of Cylindrical Grinding Process Parameters on Material Removal Rate of EN21AM Steel, International journal & magazine of engineering technology management and research, 4 (8), 113–116, (2017). [Google Scholar]
- Nidiginti Guruchandra, B. Anjan Kumar Reddy, M. Chandra Sekhar Reddy, Optimization of Cylindrical Grinding Process Parameters on Material Removal Rate of EN21AM steel, International Journal of Engineering Research & Technology, 6(6), 623–631, (2017). [Google Scholar]
- H.C. Liao, Y.K. Chen, Optimizing multi-response problem in the Taguchi method by DEA based ranking method, International Journal of Quality & Reliability Management, 19(7), 825–837, (2002), doi: 10.1108/02656710210434766. [Google Scholar]
- S. Nguyen Hong, U. Vo Thi Nhu, Multi-objective Optimization in Turning Operation of AISI 1055 Steel Using DEAR Method, Tribology in Industry, 43(1), 57–65, doi: 10.24874/ti.1006.11.20.01 [Google Scholar]
- Vennela V. K. Lakshmi, Kambagowni Venkata Subbaiah, Arun Vikram Kothapalli, Kilparthi Suresh, Parametric optimization while turning Ti-6Al-4V alloy in Mist-MQCL (Green environment) using the DEAR method, Manufacturing Review, 7(38), 1–13, (2020), doi: 10.1051/mfreview/2020034. [Google Scholar]
- D. D. Trung, N.-T. Nguyen, D. H. Tien, H. L. Dang, A research on multi-objective optimization of the grinding process using segmented grinding wheel by Taguchi-Dear method, Eureka: Physics and Engineering, 1, 67–77, (2021), doi: 10.21303/2461-4262.2021.001612. [Google Scholar]
- V.V. Reddy, C.S. Reddy, Multi Response Optimization of EDM of AA6082 Material using Taguchi-DEAR Method, International Journal of Scientific & Engineering Research, 7(6), 215–219, (2016). [Google Scholar]
- Nguyen Van Thien, Hoang Tien Dung, Do Duc Trung, Ngo Cuong, Optimization Cutting Parameters When Grinding X12M Steel Using Hai Duong Grinding Wheel, International Journal of Engineering & Technology, 7(4), 6994–6996, (2018), doi: 10.14419/ijet.v7i4.28526 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.