Bajaj, P.; Hariharan, A.; Kini, A.; Kürnsteiner, P.; Raabe, D.; Jägle, E. A.: Steels in additive manufacturing: A review of their microstructure and properties. Materials Science and Engineering A: Structural Materials Properties Microstructure and Processing 772, 138633 (2020)
Hariharan, A.; Lu, L.; Risse, J.; Jägle, E. A.; Raabe, D.: Mechanisms Contributing to Solidification Cracking during laser powder bed fusion of Inconel-738LC. Alloys for Additive Manufacturing Symposium 2019 (AAMS2019), Chalmers University of Technology, Gothenburg, Sweden (2019)
Hariharan, A.: On the interfacial defect formation mechanism during laser additive manufac-turing of polycrystalline superalloys. Dissertation, Ruhr-Universität Bochum (2019)
Hariharan, A.: On the interfacial defect formation mechanism during laser additive manufacturing of polycrystalline superalloys. Dissertation, Ruhr-Universität Bochum (2019)
New machine learning framework makes metal recycling more sustainable without compromising on mechanical properties. Results now published in journal Science Advances.
Scientists of the Max-Planck-Institut für Eisenforschung pioneer new machine learning model for corrosion-resistant alloy design. Their results are now published in the journal Science Advances
Statistical significance in materials science is a challenge that has been trying to overcome by miniaturization. However, this process is still limited to 4-5 tests per parameter variance, i.e. Size, orientation, grain size, composition, etc. as the process of fabricating pillars and testing has to be done one by one. With this project, we aim to…
Atom probe tomography (APT) provides three dimensional(3D) chemical mapping of materials at sub nanometer spatial resolution. In this project, we develop machine-learning tools to facilitate the microstructure analysis of APT data sets in a well-controlled way.