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The simulation of subtractive manufacturing processes has a long history in engineering. Corresponding predictions are utilized for planning, validation and optimization, e.g., of CNC-machining processes. With the up-rise of flexible robotic machining and the advancements of computational and algorithmic capability, the simulation of the coupled machine-process behaviour for complex machining processes and large workpieces is within reach. These simulations require fast material removal predictions and analysis with high spatial resolution for multi-axis operations. Within this contribution, we propose to leverage voxel-based concepts introduced in the computer graphics industry to accelerate material removal simulations. Corresponding schemes are well suited for massive parallelization. By leveraging the computational power offered by modern graphics hardware, the computational performance of high spatial accuracy volumetric voxel-based algorithms is further improved. They now allow for very fast and accurate volume removal simulation and analysis of machining processes. Within this paper, a detailed description of the data structures and algorithms is provided along a detailed benchmark for common machining operations.

We introduce a novel hybrid methodology that combines classical finite element methods (FEM) with neural networks to create a well-performing and generalizable surrogate model for forward and inverse problems. The residual from finite element methods and custom loss functions from neural networks are merged to form the algorithm. The Finite Element Method-enhanced Neural Network hybrid model (FEM-NN hybrid) is data-efficient and physics-conforming. The proposed methodology can be used for surrogate models in real-time simulation, uncertainty quantification, and optimization in the case of forward problems. It can be used to update models for inverse problems. The method is demonstrated with examples and the accuracy of the results and performance is compared to the conventional way of network training and the classical finite element method. An application of the forward-solving algorithm is demonstrated for the uncertainty quantification of wind effects on a high-rise buildings. The inverse algorithm is demonstrated in the speed-dependent bearing coefficient identification of fluid bearings. Hybrid methodology of this kind will serve as a paradigm shift in the simulation methods currently used.

ABSTRACT:Cities can be seen as complex systems of heterogeneous processes with a high variety of different influences (e.g. social, infrastructural, economic, and political impacts). This especially applies for tasks concerning urban development of existing assets. The optimization of traffic flows, reduction of emissions, improvement of energy efficiency, but also urban climate and landscape planning issues require the involvement of many different actors, balancing different perspectives, and divergent claims. The increasing complexities of planning and decision processes make high demands on professionals of various disciplines, government departments, and municipal decision-makers. In the long term, topics like urban resilience, energy management, risk and resource management have to be taken into account and reflected in future projects, but always related to socio-spatial and governmental aspects. Accordingly, it is important to develop models to be able to understand and analyze the outcomes and effects of governmental measures and planning to the urban environment. Thus, a more systematic approach is needed -going away from welldefined city models to city system models. The purpose is to describe urban processes not only quantitatively, but to grasp their qualitative complexity and interdependencies, by modeling and simulating existing urban systems. This contribution will present the City System Model (CSM) concept closely related to an Urban Energy Planning use case, will highlight the methodology, and focus on first results and findings from an ongoing interdisciplinary research project and use case to improve the basis of information for decision-makers and politicians about urban planning decisions.