@misc{BesongBuhlHaerteletal., author = {Besong, Lemopi Isidore and Buhl, Johannes and H{\"a}rtel, Sebastian and Bambach, Markus}, title = {Increasing the Forming Limits in Hole Flanging of Dual-Phase (DP) 1000 Steel Using Punch Rotation}, series = {Key Engineering Materials}, volume = {Vol. 926}, journal = {Key Engineering Materials}, issn = {1662-9795}, doi = {10.4028/p-06y8un}, pages = {717 -- 723}, language = {en} } @misc{ImranDeillonSizovaetal., author = {Imran, Muhammad and Deillon, L{\´e}a and Sizova, Irina and Neirinck, Bram and Bambach, Markus}, title = {Process optimization and study of the co-sintering behaviour of Cu-Ni multi-material 3D structures fabricated by spark plasma sintering (SPS)}, series = {Materials \& Design}, volume = {Vol. 223}, journal = {Materials \& Design}, issn = {0264-1275}, doi = {10.1016/j.matdes.2022.111210}, language = {en} } @techreport{BeisegelBuhlIzaretal., author = {Beisegel, Jesse and Buhl, Johannes and Izar, Rameez and Schmidt, Johannes and Bambach, Markus and F{\"u}genschuh, Armin}, title = {Mixed-integer programming for additive manufacturing}, editor = {F{\"u}genschuh, Armin}, address = {Cottbus}, doi = {10.26127/BTUOpen-5731}, pages = {28}, language = {en} } @misc{BambachUensalSviridovetal., author = {Bambach, Markus and {\"U}nsal, Ismail and Sviridov, Alexander and Hama-Saleh, Rebar and Weisheit, Andreas}, title = {Hybrid manufacturing of sheet metals and functionalizing for joining applications via hole flanging}, series = {Production Engineering}, volume = {15}, journal = {Production Engineering}, number = {2}, issn = {0944-6524}, doi = {10.1007/s11740-020-01016-0}, pages = {223 -- 233}, language = {en} } @misc{HartRawungHornBuhletal., author = {Hart-Rawung, Thawin and Horn, Alexander and Buhl, Johannes and Bambach, Markus and Merklein, Marion}, title = {A unified model for isothermal and non-isothermal phase transformation in hot stamping of 22MnB5 steel}, series = {Journal of Materials Processing Technology}, volume = {313}, journal = {Journal of Materials Processing Technology}, issn = {1873-4774}, doi = {10.1016/j.jmatprotec.2023.117856}, language = {en} } @misc{SzyndlerHaertelBambach, author = {Szyndler, Joanna and H{\"a}rtel, Sebastian and Bambach, Markus}, title = {Machine learning of the dynamics of strain hardening based on contact transformations}, series = {Journal of Intelligent Manufacturing}, volume = {2025}, journal = {Journal of Intelligent Manufacturing}, publisher = {Springer}, doi = {10.1007/s10845-025-02577-6}, pages = {22}, abstract = {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.}, language = {en} } @misc{BabelGuruWeilandetal., author = {Babel, Christoph and Guru, Mahish and Weiland, Jakob and Bambach, Markus}, title = {Area of interest algorithm for surface deflection areas}, series = {Journal of intelligent manufacturing}, volume = {36}, journal = {Journal of intelligent manufacturing}, number = {6}, publisher = {Springer US}, address = {New York}, issn = {0956-5515}, doi = {10.1007/s10845-024-02437-9}, pages = {3869 -- 3885}, abstract = {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.}, language = {en} }