TY - GEN A1 - Emdadi, Aliakbar A1 - Bolz, Sebastian A1 - Buhl, Johannes A1 - Weiß, Sabine A1 - Bambach, Markus T1 - Laser Powder Bed Fusion Additive Manufacturing of Fe3Al-1.5Ta Iron Aluminide with Strengthening Laves Phase T2 - Metals Y1 - 2022 U6 - https://doi.org/10.3390/met12060997 SN - 2075-4701 VL - 12 IS - 6 ER - TY - GEN A1 - Hart-Rawung, Thawin A1 - Buhl, Johannes A1 - Härtel, Sebastian A1 - Bambach, Markus T1 - Intelligent Iterative Experimental Design to Achieve Maximum Model Quality for Phase Change of 22MnB5 T2 - Key Engineering Materials Y1 - 2022 U6 - https://doi.org/https://doi.org/10.4028/p-0cnty5 VL - 926 SP - 2031 EP - 2039 PB - Verlag Trans Tech Publications Ltd ER - TY - GEN A1 - Besong, Lemopi Isidore A1 - Buhl, Johannes A1 - Härtel, Sebastian A1 - Bambach, Markus T1 - Increasing the Forming Limits in Hole Flanging of Dual-Phase (DP) 1000 Steel Using Punch Rotation T2 - Key Engineering Materials KW - Frictional Heat KW - High Formability KW - Hole Flanging KW - Punch Rotation Y1 - 2022 U6 - https://doi.org/10.4028/p-06y8un SN - 1662-9795 VL - Vol. 926 SP - 717 EP - 723 ER - TY - GEN A1 - Imran, Muhammad A1 - Deillon, Léa A1 - Sizova, Irina A1 - Neirinck, Bram A1 - Bambach, Markus T1 - Process optimization and study of the co-sintering behaviour of Cu-Ni multi-material 3D structures fabricated by spark plasma sintering (SPS) T2 - Materials & Design KW - Multi-material KW - Spark plasma sintering KW - Sintering behavior KW - Master sintering curve KW - Master sintering surface Y1 - 2022 U6 - https://doi.org/10.1016/j.matdes.2022.111210 SN - 0264-1275 VL - Vol. 223 ER - TY - RPRT A1 - Beisegel, Jesse A1 - Buhl, Johannes A1 - Izar, Rameez A1 - Schmidt, Johannes A1 - Bambach, Markus A1 - Fügenschuh, Armin ED - Fügenschuh, Armin T1 - Mixed-integer programming for additive manufacturing Y1 - 2021 U6 - https://doi.org/10.26127/BTUOpen-5731 CY - Cottbus ER - TY - GEN A1 - Bambach, Markus A1 - Ünsal, Ismail A1 - Sviridov, Alexander A1 - Hama-Saleh, Rebar A1 - Weisheit, Andreas T1 - Hybrid manufacturing of sheet metals and functionalizing for joining applications via hole flanging T2 - Production Engineering KW - Hybrid manufacturing KW - Laser metal deposition KW - Lightweight design KW - Hole flanging Y1 - 2021 U6 - https://doi.org/10.1007/s11740-020-01016-0 SN - 0944-6524 SN - 1863-7353 VL - 15 IS - 2 SP - 223 EP - 233 ER - TY - GEN A1 - Hart-Rawung, Thawin A1 - Horn, Alexander A1 - Buhl, Johannes A1 - Bambach, Markus A1 - Merklein, Marion T1 - A unified model for isothermal and non-isothermal phase transformation in hot stamping of 22MnB5 steel T2 - Journal of Materials Processing Technology Y1 - 2023 U6 - https://doi.org/10.1016/j.jmatprotec.2023.117856 SN - 1873-4774 SN - 0924-0136 VL - 313 ER - TY - GEN A1 - Szyndler, Joanna A1 - Härtel, Sebastian A1 - Bambach, Markus T1 - Machine learning of the dynamics of strain hardening based on contact transformations T2 - Journal of Intelligent Manufacturing N2 - Dislocation density-based models offer a physically grounded approach to modeling strain hardening in metal forming. Since these models are typically defined by Ordinary Differential Equations (ODEs), their accuracy is constrained by both, the model formulation and the parameter identification process. Machine Learning (ML) provides an alternative by allowing models to be constructed directly from experimental data, bypassing the accuracy limitations of explicitly defined models. However, applying ML to ODEs introduces the need for novel training techniques. This work presents a new approach for developing neural ODE models for flow curve description, utilizing a contact transformation to simplify the problem of learning an ODE into a learning a multivariate function. Y1 - 2025 UR - https://link.springer.com/article/10.1007/s10845-025-02577-6 U6 - https://doi.org/10.1007/s10845-025-02577-6 VL - 2025 PB - Springer ER - TY - GEN A1 - Babel, Christoph A1 - Guru, Mahish A1 - Weiland, Jakob A1 - Bambach, Markus T1 - Area of interest algorithm for surface deflection areas T2 - Journal of intelligent manufacturing N2 - In the automotive industry, the process of deep drawing is used for producing most of the outer surface panels. There, surface defects can occur while stamping the part. This paper proposes an area of interest (AOI) algorithm to filter possible surface deflection areas of finite element method (FEM) simulation results. The FEM is well established in the area of sheet metal forming and has shown accurate results in showing surface defects like waviness and sink marks. These two defect types are also the targeted systematic defects. In these deep drawing simulations, every manufacturing step of the sheet metal is calculated and the resulting stresses and strains are analyzed. The paper presents a newly developed post processing method for detecting surface in-corrections on basis of FEM simulation results. The focus of the method is to be independent of an experts knowledge. It should be able to be used by a wide range of non-expert applicants, unlike other post-processing methods know in today’s literature. A comparison between several machine learning (ML) approaches is made. It is shown, that the developed method outperforms current state of the art approaches in terms of the recall rate. In addition, a contour tree dataset of a FEM simulation in combination with an ML approach can be successfully used to learn a multidimensional relationship between the nodes. KW - Machine learning KW - Graph neural network KW - Finite element method KW - Deep drawing KW - Contour tree Y1 - 2025 U6 - https://doi.org/10.1007/s10845-024-02437-9 SN - 0956-5515 SN - 1572-8145 VL - 36 IS - 6 SP - 3869 EP - 3885 PB - Springer US CY - New York ER -