Abstract
High strength-to-weight ratio, as well as enhanced fracture toughness and corrosion resistance, has made 2219Al–Cu alloys prospective structural materials for automobile, marine, aircraft and aerospace engineering. Microalloying (< 0.1 wt%) with trace elements such as Sn, In, Cd, Ag, Si, etc. is currently being explored for achieving even better mechanical properties, while maintaining lower density. Present research is aimed at investigating tribological behavior of rolled and peak-aged 2219Al alloy and same alloy with trace contents (0.06 wt%) of Cd. Dry sliding wear tests were conducted on pin-on-disk tribometer with four different loads and linearly reciprocating frequency, and the volumetric wear rates (Wv) were evaluated. Wear rate increased with increase in either load or frequency, while trace content of Cd improved the surface wear resistance of 2219Al alloy system. The Wv of both alloys was further modeled as a function of two independent and external working parameters of load and frequency, by artificial neural network (ANN). Wear rate values subsequently predicted under various processing conditions were compared and correlated with experimental results within satisfactory accuracy limits. Best-fit network architecture yielded excellent prediction of 100% wear rate values, within a percentage deviation of ± 10%, and with RMS error values of 2.44 and 1.42 for the investigated alloys. Such observation highlights the superior prediction capability of ANN technique in tribological modeling and characterization. Resulting from the intelligent processing and manufacturing system with ANN, working parameters may be subsequently formulated and optimized, based on tribological behavior of the material.
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Gogoi, S., Kumar, D., Banerjee, S., Kirtania, S., Kashyap, S. (2023). Tribological Characterization of Microalloyed Al-Cu Alloys by Artificial Neural Network Modeling. In: Arockiarajan, A., Duraiselvam, M., Raju, R., Reddy, N.S., Satyanarayana, K. (eds) Recent Advances in Materials Processing and Characterization. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-19-5347-7_7
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DOI: https://doi.org/10.1007/978-981-19-5347-7_7
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