@article{scholars15750,
           title = {Prediction of corrosion properties of LENSTM deposited cobalt, chromium and molybdenum alloy using artificial neural networks},
            note = {cited By 1},
          volume = {62},
          number = {1-3},
             doi = {10.1504/IJMPT.2021.115212},
       publisher = {Inderscience Publishers},
         journal = {International Journal of Materials and Product Technology},
           pages = {152--166},
            year = {2021},
        abstract = {The corrosion properties of a material play an essential role in the life of metallic components, especially in biomedical and marine engineering applications. Cobalt-chrome-molybdenum alloy, a well-known biocompatible material, has been tested for its potentiodynamic properties. The samples are fabricated with laser engineered net shaping (LENSTM). Potentiodynamic polarisation tests are performed by scanning the samples at a rate of 2 mVs-1. The artificial neural network model has been developed for the prediction of the properties, as mentioned above, using the experimental data sets. The results of the model are found to be satisfactory as the overall R squared value is 0.9982. The developed model helps in estimating the potentiodynamic properties of the LENS deposited cobalt, chromium, and molybdenum materials with the process parameters that have not experimented, and it saves the experimental process time for various purposes. Copyright {\^A}{\copyright} 2021 Inderscience Enterprises Ltd.},
        keywords = {Biocompatibility; Chromium alloys; Cobalt alloys; Corrosive effects; Marine applications; Marine engineering; Molybdenum alloys; Seawater corrosion, Artificial neural network modeling; Cobalt-chrome-molybdenum alloys; Corrosion property; Engineering applications; Laser engineered net shaping; Metallic component; Potentiodynamics; Process parameters, Neural networks},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106877575&doi=10.1504\%2fIJMPT.2021.115212&partnerID=40&md5=a4333e2876037bcc06ce4da731822d49},
            issn = {02681900},
          author = {Shaik, N. B. and Mantrala, K. M. and Narayana, K. L.}
}