Welcome to my website!
I am a materials scientist at LKR Light Metals Technologies, a subsidiary of the Austrian Institute of Technology (AIT). We are based in Ranshofen, a town near Salzburg, Austria. I lead cooperative research projects, finding solutions that are beyond the frontier of knowledge and of high relevance to the aluminium, aerospace, and automotive industries. A focus of my research is materials characterization using scanning electron microscopy (SEM) and other methods. Below, find some news and links to my published scientific work.
In situ conductometry: A scalable method for furnace integration
July 2024
In our latest work, we demonstrate that simple, furnace-compatible electrical conductivity measurements can reveal microstructural changes during the homogenization of Al-Mg-Si alloys. We show that this in situ conductometry approach can track phase transformations and be used to predict extrudate grain structure via machine learning. The method bridges a long-standing gap between calorimetry and inline process control. The paper outlines practical sensor setups, compares the method to DSC, and proposes an industrial deployment path—all while remaining extremely cost-effective.
Gatan introduces Cipher™
September 2022
David Stowe (Gatan) and I at Ranshofener Leichtmetalltage, Salzburg, Austria, 2022
In 2021, a team led by me published the first method for spatially resolved quantification of lithium content in the scanning electron microscope, based on a composition by difference method using quantitative backscattered imaging and energy-dispersive X-ray spectrocopy [1]. We are partnering with Gatan, Inc. for the commercialization of this method; they independently assessed the accuracy of the method and found it to be better than 1.0 wt. % [2] – which is pretty amazing!Gatan recently anounced the launch of the commercial product based on our method, Cipher™, and I'm excited to share this with you: https://www.gatan.com/cipher-system-lithium-analysis
Selected publications
Pichlmann, L., Rafiezadeh, S., Hofbauer, M., Ocansey, E. D., & Österreicher, J. A. (2025). Predicting mechanical properties in aluminum alloys: A data-driven framework leveraging LLM-based data extraction and physics-based feature engineering. Materials Today Communications, 112843. — Presents an open-source framework that combines LLM-driven data extraction with physics-informed feature engineering to predict aluminum alloy mechanical properties.
Österreicher, J. A. (2025). Monte Carlo Simulation of the Tomato Salad Problem. Image Analysis and Stereology, 44(2), 99–103. — Uses an R-based Monte Carlo approach to quantify how slice thickness and distribution affect stereological bias in particle-size measurements.
Watzl, G., Ryzy, M., Österreicher, J. A., Arnoldt, A. R., Yan, G., Scherleitner, E., … (2025). Simultaneous laser-ultrasonic measurement of sound velocities and thickness of plates using combined mode local acoustic spectroscopy. Ultrasonics, 145, 107453. — Introduces CoMLAS, a non-contact method that simultaneously measures plate thickness and sound velocity via combined-mode laser ultrasonics.
Nietsch, J. A., Ott, A. C., Watzl, G., Cerny, A., Grabner, F. J., Grünsteidl, C., … (2024). Comparative study of elastic properties measurement techniques during plastic deformation of aluminum, magnesium, and titanium alloys: application to springback simulation. Meccanica, 1–18. — Compares multiple techniques (tensile tests, laser ultrasonics) for tracking elastic modulus changes during plastic deformation, showing laser ultrasound improves modulus accuracy for springback predictions.
Arnoldt, A., Österreicher, J. A., Schiffl, A., &Huml;oppel, H. W. (2024). Optimizing the Zn and Mg contents of Al–Zn–Mg wrought alloys for high strength and industrial-scale extrudability. Journal of Materials Research and Technology, 32, 2972–2982. — Identifies optimal Zn/Mg ratios for wrought Al–Zn–Mg alloys, balancing high strength (>350 MPa) and good extrudability with homogenization treatment.
Cerny, A., Grabner, F., Arnoldt, A. R., Kunschert, G., Mayr, J., Zickler, G. A., & Österreicher, J. A. (2024). Mechanisms of electrically assisted deformation of an Al–Mg alloy (AA5083-H111): Portevin–Le Chatelier phenotype transformation, suppression, and prolonged necking. Materials Science and Engineering: A, 910, 146865. — Explores how DC pulses alter PLC behavior in AA5083-H111, suppressing the serrated flow and extending uniform deformation.
Österreicher, J. A., Cerny, A., Arnoldt, A. R., Nietsch, J. A., Simson, C., Gneiger, S., … (2024). A systematic through-process rolling and extrusion study of four experimental high-strength Al-Mg-Si alloys. Results in Engineering, 23, 102384. — Presents an end-to-end study of rolling and extrusion, mapping microstructure and mechanical evolution in four high-strength Al–Mg–Si alloys.
Österreicher, J. A., Živanović, D., Walenta, W., Maimone, S., Hofbauer, M., … (2024). In situ conductometry for studying the homogenization of Al‑Mg‑Si alloys and predicting extrudate grain structure through machine learning. Materials & Design, 243, 113070. — Demonstrates furnace-scale conductometry for real-time homogenization monitoring and ML-based prediction of grain structure.
Österreicher, J. A., Pfeiffer, C., Kunschert, G., Weinberger, T., & Schlögl, C. M. (2024). Dissimilar friction stir welding and post-weld heat treatment of Ti-6Al-4V and AA7075 producing joints of unprecedented strength. Journal of Advanced Joining Processes, 9, 100213. — Reports record-strength dissimilar FSW joints between Ti-6Al-4V and AA7075, followed by heat treatment to enhance joint performance.
Arnoldt, A. R., Grohmann, L., Strommer, S., & Österreicher, J. A. (2024). Differential scanning calorimetry of age-hardenable aluminium alloys: effects of sample preparation, experimental conditions, and baseline correction. Journal of Thermal Analysis and Calorimetry, 149(10), 4425–4439. — Evaluates how sample prep and experiment setup affect DSC measurements in age-hardenable alloys and proposes improved baseline correction guidelines.
Ott, A. C., Weißensteiner, I., Arnoldt, A. R., Österreicher, J. A., & Papenberg, N. P. (2024). Automatic texture alignment by optimization method. Microscopy and Microanalysis, 30(2), 253–277. — Introduces an optimization-based algorithm for automatic texture alignment in microscopy images, improving the accuracy of structural analysis.