Details
Title
A Frontier Statistical Approach Towards Online Tool Condition Monitoring and Optimization for Dry Turning Operation of SAE 1015 SteelJournal title
Archives of Metallurgy and MaterialsYearbook
2021Volume
vol. 66Issue
No 3Authors
Affiliation
Chinnasamy, Moganapriya : Kongu Engineering College, Department of Mechanical Engineering, Perundurai – 638060, Tamil Nadu State, India ; Rathanasamy, Rajasekar : Kongu Engineering College, Department of Mechanical Engineering, Perundurai – 638060, Tamil Nadu State, India ; Kaliyannan, Gobinath Velu : Kongu Engineering College, Department of Mechatronics Engineering, Perundurai – 638060, Tamil Nadu State, India ; Paramasivam, Prabhakaran : Kongu Engineering College, Department of Mechanical Engineering, Perundurai – 638060, Tamil Nadu State, India ; Jaganathan, Saravana Kumar : Bionanotechnology Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam ; Jaganathan, Saravana Kumar : Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam ; Jaganathan, Saravana Kumar : Department of Engineering, Faculty of Science and Engineering, University of Hull, HU6 7RX, United KingdomKeywords
Coated inserts ; Microflown sensor ; flank wear ; I- Kaz ; neural networkDivisions of PAS
Nauki TechniczneCoverage
901-909Publisher
Institute of Metallurgy and Materials Science of Polish Academy of Sciences ; Committee of Materials Engineering and Metallurgy of Polish Academy of SciencesBibliography
[1] M. Noordin, V. Venkatesh, S. Sharif, J. Mater. Process. Tech. 185 (1-3), 83-90 (2007). DOI: https://doi.org/10.1016/j.jmatprotec.2006.03.137[2] C. Moganapriya, M. Vigneshwaran, G. Abbas, A. Ragavendran, V.C. Harissh Ragavendra, R. Rajasekar, Mater. Today, Proceeding (2020).
[3] A.M. Ravi, S.M. Murigendrappa, P.G. Mukunda, T. Indian I. Metals 67 (4), 485-502 (2014). DOI: https://doi.org/10.1007/s12666-013-0369-0
[4] A.P. Kulkarni, V.G. Sargade, Mater. Manuf. Process 30 (6), 748- 755 (2015). DOI: https://doi.org/10.1080/10426914.2014.984217
[5] C. Moganapriya, R. Rajasekar, K. Ponappa, R. Venkatesh, S. Jerome, Mater. Today. Proceeding 5 (2), 8532-8538 (2018). DOI: https://doi.org/10.1016/j.matpr.2017.11.550
[6] G .C. Rosa, A.J. Souza, E.V. Possamai, H.J. Amorim, P.D. Neis, Wear 376, 172-177 (2017). DOI: https://doi.org/10.1016/j.wear.2017.01.088
[7] A. Alok, M. Das, Measurement 133, 288-302 (2019). DOI: https://doi.org/10.1016/j.measurement.2018.10.009
[8] R . Yigit, E. Celik, F. Findik, S. Koksal, Int. J. Refract. Hard. Met. 26 (6), 514-524 (2008). DOI: https://doi.org/10.1016/j.ijrmhm.2007.12.003
[9] R . Horváth, Á. Drégelyi-Kiss, G. Mátyási, Acta Polytech. Hung. 11 (2), 137-147 (2014).
[10] R . Kumar, P.S. Bilga, S. Singh, J. Clean Prod. 164, 45-57 (2017). DOI: https://doi.org/10.1016/j.jclepro.2017.06.077
[11] M.K. Gupta, P. Sood, V.S. Sharma, J. Clean Prod. 135, 1276-1288 (2016). DOI: https://doi.org/10.1016/j.jclepro.2016.06.184
[12] S . Pai, T. Nagabhushana, Handbook of Research on Emerging Trends and Applications of Machine Learning, 2020 IGI Global.
[13] A.K. Jain, B.K. Lad, J. Intell. Manuf. 30 (3), 1423-1436 (2019). DOI: https://doi.org/10.1007/s10845-017-1334-2
[14] R . Teti, K. Jemielniak, G. O’Donnell, D. Dornfeld, CIRP Ann. 59 (2), 717-739 (2010). DOI: https://doi.org/10.1016/j.cirp.2010.05.010
[15] C. Moganapriya, R. Rajasekar, K. Ponappa, R. Venkatesh, R. Karthick, Arch. Metall. Mater. 62 (3), 1827-1832 (2017). DOI: https://doi.org/10.1515/amm-2017-0276
[16] H .B. Ulas,T. Indian I. Metals 67 (6), 869-879 (2014). DOI: https://doi.org/10.1007/s12666-014-0410-y
[17] S . Thangarasu, S. Shankar, T. Mohanraj, K. Devendran, P. I. Mech. Eng. C.-J. Mec. 234 (1), 329-342 (2019).
[18] J .A. Ghani, M. Rizal, M.Z. Nuawi, C.H. Che Haron, M.J. Ghazali, M.N.A. Rahman. Trans. Tech. Publ. 2010.
[19] S . Oraby, D. Hayhurst, Int. J. Mach. Tools Manuf. 44 (12-13), 1261-1269 (2004). DOI: https://doi.org/10.1016/j.ijmachtools.2004.04.018