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Meeting MS&T26: Materials Science & Technology
Symposium Advanced Coatings for Wear and Corrosion Protection
Presentation Title Galvanic Compatibility of Wear-Resistant Laser-Clad Fe–Mn–Al–Cr–C Coatings with A356 Aluminum
Author(s) Vikram Varadaraajan
On-Site Speaker (Planned) Vikram Varadaraajan
Abstract Scope Ferrous wear-resistant coatings on aluminum substrates can form unfavorable galvanic couples in chloride service, driving sacrificial substrate loss. We applied combinatorial mixed-elemental-powder directed energy deposition to twelve Fe–(15,25,35)Mn–(2,6)Al–(2,4)Cr–0.2C compositions and characterized phase evolution, dry-sliding wear, and potentiodynamic corrosion across the full matrix, with zero-resistance ammetry (ZRA) against A356 aluminum for compositions spanning the manganese range. Standard corrosion screening did not predict coupling behavior: Fe–15Mn–6Al–4Cr, the lowest-Icorr composition in the matrix (0.6 µm/yr, below A356 itself), coupled strongly cathodic, while Fe–35Mn–2Al–2Cr, with a twentyfold higher corrosion rate, coupled net anodic and suppressed substrate loss. Polarization-derived Ecorr proximity alone did not resolve polarity; resting open-circuit potentials were closer than scanned values suggested, and dissolution-rate asymmetry between coating and substrate appears to govern which member turns anodic. Manganese content links wear resistance, self-corrosion, and galvanic compatibility in opposing directions, making coupled-current screening a necessary complement to potentiodynamic characterization for coatings on aluminum substrates.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Developing Predictive Control for Reactive Plasma Spray Deposition of Nitride Coatings
Galvanic Compatibility of Wear-Resistant Laser-Clad Fe–Mn–Al–Cr–C Coatings with A356 Aluminum
Microstructural Evolution in a New Inorganic Trivalent Chromium Electrodeposition Solution
Optimization of Composition of Functionally Active Mixtures for Titanium Chromium Coating to Improve Wear Resistance of Carbon Steels
Sensing, Predicting and Validating Coating Lifetime Performance via Electrochemical Testing and Machine Learning
Tribological and Anti-Scaling Performance of Graphene-Enriched Thin Polyether Ether Ketone (PEEK) Coatings

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