TY - CHAP A1 - Hartmann, Jürgen A1 - Lenski, Philipp A1 - Ochs, Dennis A1 - Shandy, Amir A1 - Winterstein, A. A1 - Versch, Alexander A1 - Schiffler, Andreas T1 - Thermische Prozessüberwachung für additive Fertigungsverfahren BT - Temperatur 2020 Y1 - 2020 CY - Berlin ER - TY - CHAP A1 - Hartmann, Jürgen A1 - Ochs, Dennis A1 - Lenski, Philipp A1 - Schiffler, Andreas A1 - Versch, Alexander A1 - Manara, Jochen T1 - Thermal process monitoring for additive manufacturing BT - MSE 2020 Y1 - 2020 CY - Darmstadt ER - TY - CHAP A1 - Lutz, Benjamin A1 - Kisskalt, Dominik A1 - Regulin, Daniel A1 - Reisch, Raven A1 - Schiffler, Andreas A1 - Franke, Jörg T1 - Evaluation of deep learning for semantic image segmentation in tool condition monitoring T2 - 2019 18th IEEE international conference on machine learning and applications (ICMLA) Y1 - 2019 SP - 2008 EP - 2013 ER - TY - JOUR A1 - Lang, Silvio A1 - Engelmann, Bastian A1 - Schiffler, Andreas A1 - Schmitt, Jan T1 - A simplified machine learning product carbon footprint evaluation tool JF - Cleaner Environmental Systems N2 - On the way to climate neutrality manufacturing companies need to assess the Carbon dioxide (CO2) emissions of their products as a basis for emission reduction measures. The evaluate this so-called Product Carbon Footprint (PCF) life cycle analysis as a comprehensive method is applicable, but means great effort and requires interdisciplinary knowledge. Nevertheless, assumptions must still be made to assess the entire supply chain. To lower these burdens and provide a digital tool to estimate the PCF with less input parameter and data, we make use of machine learning techniques and develop an editorial framework called MINDFUL. This contribution shows its realization by providing the software architecture, underlying CO2 factors, calculations and Machine Learning approach as well as the principles of its user experience. Our tool is validated within an industrial case study. KW - Management, Monitoring, Policy and Law KW - Environmental Science (miscellaneous) KW - Renewable Energy, Sustainability and the Environment KW - Environmental Engineering Y1 - 2024 U6 - https://doi.org/10.1016/j.cesys.2024.100187 SN - 2666-7894 VL - 13 PB - Elsevier BV ER - TY - JOUR A1 - Höfflin, Dennis A1 - Sauer, Christian A1 - Schiffler, Andreas A1 - Manara, Jochen A1 - Hartmann, Jürgen T1 - Pixelwise high-temperature calibration for in-situ temperature measuring in powder bed fusion of metal with laser beam JF - Heliyon N2 - High-temperature calibration methods in additive manufacturing involve the use of advanced techniques to accurately measure and control the temperature of the build material during the additive manufacturing process. Infrared cameras, blackbody radiation sources and non-linear optimization algorithms are used to correlate the temperature of the material with its emitted thermal radiation. This is essential for ensuring the quality and repeatability of the final product. This paper presents the calibration procedure of an imaging system for in-situ measurement of absolute temperatures and temperature gradients during powder bed fusion of metal with laser beam (PBF-LB/M) in the temperature range of 500 K–1500 K. It describes the design of the optical setup to meet specific requirements in this application area as well as the procedure for accounting the various factors influencing the temperature measurement. These include camera-specific effects such as varying spectral sensitivities of the individual pixels of the sensor as well as influences of the exposure time and the exposed sensor area. Furthermore, influences caused by the complex optical path, such as inhomogeneous transmission properties of the galvanometer scanner as well as angle-dependent transmission properties of the f-theta lens were considered. A two-step fitting algorithm based on Planck's law of radiation was applied to best represent the correlation. With the presented procedure the calibrated thermography system provides the ability to measure absolute temperatures under real process conditions with high accuracy. Y1 - 2024 U6 - https://doi.org/10.1016/j.heliyon.2024.e28989 SN - 2405-8440 VL - 10 IS - 7 PB - Elsevier BV ER -