Frederic E. Bock
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Deformation by design: data-driven approach to predict and modify deformation in thin Ti-6Al-4V sheets using laser peen forming Open
The precise bending of sheet metal structures is crucial in various industrial and scientific applications, whether to modify deformation in an existing component or to achieve specific shapes. Laser peen forming (LPF) is proven as an inno…
Data-driven and physics-based modelling of process behaviour and deposit geometry for friction surfacing Open
In the last decades, there has been an increase in the number of successful machine learning models that have served as a key to identifying and using linkages within the process-structure–property-performance chain for vastly different pr…
Data of: Data-driven and physics-based modelling of process behaviour and deposit geometry for friction surfacing Open
This dataset contains the data and models used in the research journal publication: "Data-driven and physics-based modelling of process behaviour and deposit geometry for friction surfacing " which was funded from the European Research Cou…
Data of: Data-driven and physics-based modelling of process behaviour and deposit geometry for friction surfacing Open
This dataset contains the data and models used in the research journal publication: "Data-driven and physics-based modelling of process behaviour and deposit geometry for friction surfacing " which was funded from the European Research Cou…
Hybrid Modelling by Machine Learning Corrections of Analytical Model Predictions towards High-Fidelity Simulation Solutions Open
Within the fields of materials mechanics, the consideration of physical laws in machine learning predictions besides the use of data can enable low prediction errors and robustness as opposed to predictions only based on data. On the one h…
Evaluation of mechanical property predictions of refill Friction Stir Spot Welding joints via machine learning regression analyses on DoE data Open
The high-potential of lightweight components consisting of similar or dissimilar materials can be exploited by Solid-State Joining techniques. Whereas defects such as pores and hot cracking are often an issue in fusion-based joining proces…
Mechanical Performance Prediction for Friction Riveting Joints of Dissimilar Materials via Machine Learning Open
Solid-state joining techniques have become increasingly attractive for joining similar and dissimilar materials because it enables further optimization of lightweight components. In contrast to fusion-based joining processes, solid-state j…
A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics Open
Machine learning tools represent key enablers for empowering material scientists and engineers to accelerate the development of novel materials, processes and techniques. One of the aims of using such approaches in the field of materials s…
Thinner and better: (Ultra-)low grammage bacterial cellulose nanopaper-reinforced polylactide composite laminates Open
One of the rate-limiting steps in the large-scale production of cellulose nanopaper-reinforced polymer composites is the time consuming dewatering step to produce the reinforcing cellulose nanopapers. In this work, we show that the dewater…