Publicación: SIGMA PHASE STABILIZATION BY NB DOPING IN A NEW HIGH-ENTROPY ALLOY IN THE FECRMNNICU SYSTEM: A STUDY OF PHASE PREDICTION AND NANOMECHANICAL RESPONSE
Fecha
2024
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Metals
Resumen
THE DEVELOPMENT OF HIGH-ENTROPY ALLOYS HAS BEEN HAMPERED BY THE CHALLENGE OF EFFECTIVELY AND VERIFIABLY PREDICTING PHASES USING PREDICTIVE METHODS FOR FUNCTIONAL DESIGN. THIS STUDY VALIDATES REMARKABLE PHASE PREDICTION CAPABILITY IN COMPLEX MULTICOMPONENT ALLOYS BY MICROSTRUCTURALLY PREDICTING TWO NOVEL HIGH-ENTROPY ALLOYS IN THE FCC + BCC AND FCC + BCC + IM SYSTEMS USING A NOVEL ANALYTICAL METHOD BASED ON VALENCE ELECTRON CONCENTRATION (VEC). THE RESULTS ARE COMPARED WITH MACHINE LEARNING, CALPHAD, AND EXPERIMENTAL DATA. THE KEY FINDINGS HIGHLIGHT THE HIGH PREDICTIVE ACCURACY OF THE ANALYTICAL METHOD AND ITS STRONG CORRELATION WITH MORE INTRICATE PREDICTION METHODS SUCH AS RANDOM FOREST MACHINE LEARNING AND CALPHAD. FURTHERMORE, THE EXPERIMENTAL RESULTS VALIDATE THE PREDICTIONS WITH A RANGE OF TECHNIQUES, INCLUDING SEM-BSE, EDS, ELEMENTAL MAPPING, XRD, MICROHARDNESS, AND NANOHARDNESS MEASUREMENTS. THIS STUDY REVEALS THAT THE ADDITION OF NB ENHANCES THE FORMATION OF THE SIGMA (?) INTERMETALLIC PHASE, RESULTING IN INCREASED ALLOY STRENGTH, AS DEMONSTRATED BY MICROHARDNESS AND NANOHARDNESS MEASUREMENTS. LASTLY, THE OVERLAPPING VEC RANGES IN HIGH-ENTROPY ALLOYS ARE IDENTIFIED AS POTENTIAL INDICATORS OF PHASE TRANSITIONS AT ELEVATED TEMPERATURES.
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sigma phasemachine learning, phase prediction, high-entropy alloys
