How to cite this paper
Prasad, K & Chakraborty, S. (2018). Application of the modified similarity-based method for cutting fluid selection.Decision Science Letters , 7(3), 273-286.
Refrences
Abhang, L.B., & Hameedullah, M. (2012). Selection of lubricant using combined multiple attribute decision making method. Advances in Production Engineering and Management,7(1), 39-50.
Anyaeche, C., Ighravwe, D., & Asokeji, T. (2017). Project portfolio selection of banking services using COPRAS and Fuzzy-TOPSIS. Journal of Project Management, 2(2), 51-62.
Axinte, D.A., Belluco, W., & De Chiffre, L. (2001). Reliable tool life measurements in turning - An application to cutting fluid efficiency evaluation. International Journal of Machine Tools and Manufacture,41(7), 1003-1014.
Cakir, O., Yardimeden, A., Ozben, T., & Kilickap, E. (2007). Selection of cutting fluids in machining processes. Journal of Achievements in Materials and Manufacturing Engineering,25(2), 99-102.
Chaghooshi, A.J., Janatifar, H., & Dehghan, M.(2014). An application of ahp and similarity-based approach to personnel selection. International Journal of Business Management and Economics, 1(1), 24-32.
Chakraborty, S., & Zavadskas, E.K. (2014). Applications of WASPAS method in manufacturing decision making. Informatica,25(1), 1-20.
Comadury, R., & Larsen-Basse, J. (1989). Tribology: The cutting edge. Mechanical Engineering.
De Chiffre, L., & Belluco, W. (2002). Investigations of cutting fluid performance using different machining operations. Lubrication Engineering,58, 22-29.
Deng, H.(1999). Multicriteria analysis with fuzzy pairwise comparison. International Journal of Approximate Reasoning, 21(3), 215-231.
Deng, H.(2007). A similarity-based approach to ranking multicriteria alternatives. International Conference on Intelligent Computing, Lecture Notes in Artificial Intelligence,4682, 253-262.
Deshamukhya, T., & Ray, A. (2014). Selection of cutting fluid for green manufacturing using analytical hierarchy process (AHP): A case study. International Journal of Mechanical Engineering and Robotics Research,3(1), 173-182.
Hubbard, K. M., Callahan, R.N., &Strong. S.D. (2008). A standardized model for the evaluation of machining coolant/lubricant costs. International Journal of Advanced Manufacturing Technology,36(1), 1-10.
Jagadish, & Ray, A. (2014a). Cutting fluid selection for sustainable design for manufacturing: An integrated theory. Procedia Materials Science,6, 450-459.
Jagadish,& Ray, A. (2014b). Green cutting fluid selection using MOOSRA method. International Journal of Research in Engineering and Technology,3(3), 559-563.
Jayal, A.D., & Balaji, A.K. (2009). Effects of cutting fluid application on tool wear in machining: Interactions with tool-coatings and tool surface features. Wear,267(9-10),1723-1730.
Kumar, E. S. R. R., & Prasad, J. S. R. (2014). A novel approach of obtaining theoretical values in selection of cutting fluid attributes. IOSR Journal of Mathematics, 10(5), 1-4.
Meciarova, J., & Stanovsky, M. (2011). Cutting fluids evaluation based on occupational health and environmental hazards. Engineering for Rural Development,10, 418-422.
Moradi, M., & Ebrahimi, E. (2014). Applying fuzzy AHP and similarity-based approach for economic evaluating companies based on corporate governance measures. Global Journal of Management Studies and Researches, 1(1), 10-20.
Nouioua, M., Yallese, M.A., Khettabi, R., Belhadi, S., & Mabrouki, T. (2017). Comparative assessment of cooling conditions, including MQL technology on machining factors in an environmentally friendly approach. International Journal of Advanced Manufacturing Technology, DOI: 10.1007/s00170-016-9958-5.
Prasad, K., & Chakraborty, S. (2016). Aquality function deployment-based model forcutting fluid selection. Advances in Tribology, http://dx.doi.org/10.1155/2016/3978102.
Rao, N.D., Srikant, R.R., & Rao, C.S. (2007). Influence of emulsifier content on properties and durability of cutting fluids. Journal of the Brazilian Society of Mechanical Science and Engineering,24(4), 396-400.
Rao, R.V., & Gandhi, O.P. (2001). Digraph and matrix method for selection, identification and comparison of metal cutting fluids. Journal of Engineering Tribology,215(1), 25-33.
Rao,R.V.(2007). Decision making in the manufacturing environment using graph theory and fuzzy multiple attribute decision making methods. Springer-Verlag, London.
Rao, R.V., & Patel, B.K. (2010). Decision making in the manufacturing environment using an improved PROMETHEE method. International Journal of Production Research,48(16), 4665-4682.
Safari, H., Khanmohammadi, E., Hafezamini, A., & Ahangari, S.S. (2013). A new technique for multi criteria decision making based on modified similarity method. Middle-East Journal of Scientific Research,14(5), 712-719.
Safari, H., & Ebrahimi, E.(2014). Using modified similarity multiple criteria decision making technique to rank countries in terms of human development index. Journal of Industrial Engineering and Management, 7(1), 254-275.
Sales, W.F., Diniz, A.E., Machado, A.R. (2001). Application of cutting fluids in machining processes. Journal of the Brazilian Society of Mechanical Sciences,23(2), 227-240.
Sadatrasool, M., Bozorgi-Amiri, A., & Yousefi-Babadi, A. (2016). Project manager selection based on project manager competency model: PCA–MCDM Ap-proach. Journal of Project Management, 1(1), 7-20.
Sandhya, S., & Garg, R. (2016). Implementation of multi-criteria decision making approach for the team leader selection in IT sector. Journal of Project Management, 1(2), 67-75.
Soković, M., & Mijanović, K. (2001). Ecological aspects of the cutting fluids and its influence on quantifiable parameters of the cutting processes.Journal of Materials Processing Technology,109(1-2), 181-189.
Sonkar, V., Abhishek, K., Datta, S., & Mahapatra, S. S. (2014). Multi-objective optimization in drilling of GFRP composites : A degree of similarity approach. Procedia Materials Science, 6, 538-543.
Sun, J., Ge, P., &Liu, Z. (2001). Two-grade fuzzy synthetic decision-making system with use of an analytic hierarchy process for performance evaluation of grinding fluids. Tribology International,34(10), 683-688.
Sutherland, J.W., Cao, T., Daneil, C.M., Yue, Y., Zheng, Y., Sheng, P., Bauer, D., Srinivasan, M., DeVor, R.E., Kapoor, S.G., & Skerlos, S.J. (1997). CFEST: An internet-based cutting fluid evaluation software testbed. North American Manufacturing Research Institution of the Society of Manufacturing Engineers, 25, 243-248.
Tan, X.C.,Lin, F., Cao, H.J., & Zang, H. (2002). A decision-making framework model of cutting fluid selection for green manufacturing and a case study. Journal of Materials Processing Technology,129(1-3), 467-470.
Tiwari, V.V., & Sharma, A.(2015). MADM for selection of vegetable based cutting fluids by SAW method and WPM method. International Journal of Research in Technology and Management,1(1), 16-27.
Anyaeche, C., Ighravwe, D., & Asokeji, T. (2017). Project portfolio selection of banking services using COPRAS and Fuzzy-TOPSIS. Journal of Project Management, 2(2), 51-62.
Axinte, D.A., Belluco, W., & De Chiffre, L. (2001). Reliable tool life measurements in turning - An application to cutting fluid efficiency evaluation. International Journal of Machine Tools and Manufacture,41(7), 1003-1014.
Cakir, O., Yardimeden, A., Ozben, T., & Kilickap, E. (2007). Selection of cutting fluids in machining processes. Journal of Achievements in Materials and Manufacturing Engineering,25(2), 99-102.
Chaghooshi, A.J., Janatifar, H., & Dehghan, M.(2014). An application of ahp and similarity-based approach to personnel selection. International Journal of Business Management and Economics, 1(1), 24-32.
Chakraborty, S., & Zavadskas, E.K. (2014). Applications of WASPAS method in manufacturing decision making. Informatica,25(1), 1-20.
Comadury, R., & Larsen-Basse, J. (1989). Tribology: The cutting edge. Mechanical Engineering.
De Chiffre, L., & Belluco, W. (2002). Investigations of cutting fluid performance using different machining operations. Lubrication Engineering,58, 22-29.
Deng, H.(1999). Multicriteria analysis with fuzzy pairwise comparison. International Journal of Approximate Reasoning, 21(3), 215-231.
Deng, H.(2007). A similarity-based approach to ranking multicriteria alternatives. International Conference on Intelligent Computing, Lecture Notes in Artificial Intelligence,4682, 253-262.
Deshamukhya, T., & Ray, A. (2014). Selection of cutting fluid for green manufacturing using analytical hierarchy process (AHP): A case study. International Journal of Mechanical Engineering and Robotics Research,3(1), 173-182.
Hubbard, K. M., Callahan, R.N., &Strong. S.D. (2008). A standardized model for the evaluation of machining coolant/lubricant costs. International Journal of Advanced Manufacturing Technology,36(1), 1-10.
Jagadish, & Ray, A. (2014a). Cutting fluid selection for sustainable design for manufacturing: An integrated theory. Procedia Materials Science,6, 450-459.
Jagadish,& Ray, A. (2014b). Green cutting fluid selection using MOOSRA method. International Journal of Research in Engineering and Technology,3(3), 559-563.
Jayal, A.D., & Balaji, A.K. (2009). Effects of cutting fluid application on tool wear in machining: Interactions with tool-coatings and tool surface features. Wear,267(9-10),1723-1730.
Kumar, E. S. R. R., & Prasad, J. S. R. (2014). A novel approach of obtaining theoretical values in selection of cutting fluid attributes. IOSR Journal of Mathematics, 10(5), 1-4.
Meciarova, J., & Stanovsky, M. (2011). Cutting fluids evaluation based on occupational health and environmental hazards. Engineering for Rural Development,10, 418-422.
Moradi, M., & Ebrahimi, E. (2014). Applying fuzzy AHP and similarity-based approach for economic evaluating companies based on corporate governance measures. Global Journal of Management Studies and Researches, 1(1), 10-20.
Nouioua, M., Yallese, M.A., Khettabi, R., Belhadi, S., & Mabrouki, T. (2017). Comparative assessment of cooling conditions, including MQL technology on machining factors in an environmentally friendly approach. International Journal of Advanced Manufacturing Technology, DOI: 10.1007/s00170-016-9958-5.
Prasad, K., & Chakraborty, S. (2016). Aquality function deployment-based model forcutting fluid selection. Advances in Tribology, http://dx.doi.org/10.1155/2016/3978102.
Rao, N.D., Srikant, R.R., & Rao, C.S. (2007). Influence of emulsifier content on properties and durability of cutting fluids. Journal of the Brazilian Society of Mechanical Science and Engineering,24(4), 396-400.
Rao, R.V., & Gandhi, O.P. (2001). Digraph and matrix method for selection, identification and comparison of metal cutting fluids. Journal of Engineering Tribology,215(1), 25-33.
Rao,R.V.(2007). Decision making in the manufacturing environment using graph theory and fuzzy multiple attribute decision making methods. Springer-Verlag, London.
Rao, R.V., & Patel, B.K. (2010). Decision making in the manufacturing environment using an improved PROMETHEE method. International Journal of Production Research,48(16), 4665-4682.
Safari, H., Khanmohammadi, E., Hafezamini, A., & Ahangari, S.S. (2013). A new technique for multi criteria decision making based on modified similarity method. Middle-East Journal of Scientific Research,14(5), 712-719.
Safari, H., & Ebrahimi, E.(2014). Using modified similarity multiple criteria decision making technique to rank countries in terms of human development index. Journal of Industrial Engineering and Management, 7(1), 254-275.
Sales, W.F., Diniz, A.E., Machado, A.R. (2001). Application of cutting fluids in machining processes. Journal of the Brazilian Society of Mechanical Sciences,23(2), 227-240.
Sadatrasool, M., Bozorgi-Amiri, A., & Yousefi-Babadi, A. (2016). Project manager selection based on project manager competency model: PCA–MCDM Ap-proach. Journal of Project Management, 1(1), 7-20.
Sandhya, S., & Garg, R. (2016). Implementation of multi-criteria decision making approach for the team leader selection in IT sector. Journal of Project Management, 1(2), 67-75.
Soković, M., & Mijanović, K. (2001). Ecological aspects of the cutting fluids and its influence on quantifiable parameters of the cutting processes.Journal of Materials Processing Technology,109(1-2), 181-189.
Sonkar, V., Abhishek, K., Datta, S., & Mahapatra, S. S. (2014). Multi-objective optimization in drilling of GFRP composites : A degree of similarity approach. Procedia Materials Science, 6, 538-543.
Sun, J., Ge, P., &Liu, Z. (2001). Two-grade fuzzy synthetic decision-making system with use of an analytic hierarchy process for performance evaluation of grinding fluids. Tribology International,34(10), 683-688.
Sutherland, J.W., Cao, T., Daneil, C.M., Yue, Y., Zheng, Y., Sheng, P., Bauer, D., Srinivasan, M., DeVor, R.E., Kapoor, S.G., & Skerlos, S.J. (1997). CFEST: An internet-based cutting fluid evaluation software testbed. North American Manufacturing Research Institution of the Society of Manufacturing Engineers, 25, 243-248.
Tan, X.C.,Lin, F., Cao, H.J., & Zang, H. (2002). A decision-making framework model of cutting fluid selection for green manufacturing and a case study. Journal of Materials Processing Technology,129(1-3), 467-470.
Tiwari, V.V., & Sharma, A.(2015). MADM for selection of vegetable based cutting fluids by SAW method and WPM method. International Journal of Research in Technology and Management,1(1), 16-27.