Varun Chaudhary1,Wei Teh2,Shilin Chen2,Shakti Padhy1,Cheng Tan2,Raju Ramanujan1
Nanyang Technological University1,Institute of Materials Research and Engineering2
Varun Chaudhary1,Wei Teh2,Shilin Chen2,Shakti Padhy1,Cheng Tan2,Raju Ramanujan1
Nanyang Technological University1,Institute of Materials Research and Engineering2
New materials exhibiting an attractive combination of magnetic, mechanical and electrical properties can play a vital role in improving the performance of next generation rotating electrical machines. However, developing such a material using conventional approaches is expensive, time consuming and misses googols of possible compositions and processing parameters. On the other hand, the accelerated discovery approach relies on high throughput synthesis, characterization, property evaluation, predictive machine learning (ML) and modelling to rapidly screen a huge number of compositions. The assessment of the structure as well as the magnetic, mechanical, and electrical properties of compositionally graded Co-Fe-Ni alloy samples processed by laser additive manufacturing (AM) was rapidly carried out. The AM process facilitated the rapid synthesis of a compositionally graded material library and the subsequent assessment of several properties. A large change in these properties was observed as a function of composition. Saturation magnetization varied from 60 to 230 emu/g, coercivity from 0.5 to 66.4 Oe, resistivity from 9.4 to 72.6 μΩ cm, microhardness from 90 to 400 Hv, ultimate tensile strength from 465 to 1167 MPa etc. Novel compositions, e.g., Co<sub>30</sub>Fe<sub>60</sub>Ni<sub>10</sub>, Co<sub>10</sub>Fe<sub>80</sub>Ni<sub>10</sub>, etc., with an optimum magnetic-mechanical-electrical property set have been identified by our approach. This work is supported by the AME Programmatic Fund by the Agency for Science, Technology and Research, Singapore under Grant No. A1898b0043.