Kusampudi, N.; Diehl, M.: Inverse design of dual-phase steel microstructures using generative machine learning model and Bayesian optimization. International Journal of Plasticity 171, 103776 (2023)
Kusampudi, N.; Diehl, M.: Inverse design of dual-phase steel microstructures using generative machine learning model and Bayesian optimization. Working Group Microstructural Mechanics, Deutsche Gesellschaft für Materialkunde e.V., Applications of Machine Learning for Mechanical Behavior of Materials, Online (2022)
Diehl, M.; Kusampudi, N.: Using machine learning and crystal plasticity simulation to design damage resistant dual phase steels. Webinar: Metal Plasticity Seminar - Artificial Intelligence, Machine Learning and Big Data in Metal Plasticity, Leuven, Belgium (2021)
Diehl, M.; Kusampudi, N.; Kusche, C.; Raabe, D.; Korte-Kerzel, S.: Combining Experiments, Simulations, and Data Science to Understand Damage in Dual Phase Steels. International Conference on Plasticity, Damage, and Fracture, Riviera May, Mexico (2020)
Kusampudi, N.: Application of topological data analysis and sliding window embedding to analyze time series data from bird songs. Danish-Swedish summer school on TDA and spatial statistics, Aalborg, Sweden (2023)
Kusampudi, N.: Using Machine Learning and Data-driven Approaches to Predict Damage Initiation in Dual-Phase Steels. Master, Ruhr-Universität Bochum (2020)
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
Electron channelling contrast imaging (ECCI) is a powerful technique for observation of extended crystal lattice defects (e.g. dislocations, stacking faults) with almost transmission electron microscopy (TEM) like appearance but on bulk samples in the scanning electron microscope (SEM).
Water electrolysis has the potential to become the major technology for the production of the high amount of green hydrogen that is necessary for its widespread application in a decarbonized economy. The bottleneck of this electrochemical reaction is the anodic partial reaction, the oxygen evolution reaction (OER), which is sluggish and hence…
New product development in the steel industry nowadays requires faster development of the new alloys with increased complexity. Moreover, for these complex new steel grades, it is more challenging to control their properties during the process chain. This leads to more experimental testing, more plant trials and also higher rejections due to…