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Integrating Cloud computing, Bayesian Optimization, and Neural-Additive Modeling for Enhanced CAM Systems in 5-Axis Milling 34

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Integrating Cloud Computing, Bayesian Optimization, and Neur...

34th CIRP Design Conference, CIRP 2024

作者： Rudel, Viktor Vinogradov, Georg Ganser, Philipp Bergs, Thomas Vahl, Christopher Frings, Markus König, Valentina Schambach, Maximilian Dietzel, Stefan Königs, Michael Fraunhofer Institute for Production Technology IPT Steinbachstraße 17 Aachen52074 Germany RWTH Aachen University Campus-Boulevard 30 Aachen52074 Germany Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven 1 Sankt Augustin53757 Germany ModuleWorks GmbH Henricistraße 50 Aachen52072 Germany Merantix Momentum Merantix AI Campus Max-Urich-Straße 3 Berlin13355 Germany EXAPT Systemtechnik GmbH Theaterstraße 12 Aachen52062 Germany

This publication introduces the development and application of an advanced CAD/CAM/CAE system that leverages the computational capabilities of an edge-cloud infrastructure. The developed system consists of containerized technology models for various complex process planning simulation tasks in 5-axis-milling, such as toolpath calculation, cutter-workpiece-engagement, uncut chip geometry or cutting force simulations. Moreover, a Bayesian optimization (BO) algorithm is coupled with the system enabling multi-objective optimizations of the considered machining operations by varying predefined CAM-parameters. The result of the optimization consists of a set of Pareto-efficient solutions. Each solution realizes a different tradeoff between the technological objectives of the process planner. Since mastering the complexity of the design space is a major challenge in today's CAM programming workflow, a Neural-Additive Model (NAM) is coupled with the system improving guided search through the configuration/result space. This reduces the convergence time to an optimal CAM parameter set. The coupling of the cloud computing, multi-objective optimization and artificial intelligence method with a CAM-kernel is demonstrated based on the process design of 5-axis milling operations for a blade-integrated disk (blisk). © 2024 The Authors. Published by Elsevier B.V.

关键词： Milling (machining)

Static spherically symmetric solutions of Einstein field equations with radial dark matter

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arXiv 2017年

作者： Nikitin, Igor Department of High Performance Analytics Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt Augustin53757 Germany

We study a static spherically symmetric problem with a black hole and radially directed geodesic flows of dark matter. The obtained solutions have the following properties. At large distances, the gravitational field produces constant velocities of circular motion, i.e., flat rotation curves. At smaller distances, the field switches to Newtonian regime, then to Schwarzschild regime. Deviations from Schwarzschild regime start below the gravitational radius. The dark matter prevents the creation of event horizon, instead, a spherical region possessing extremely large redshift is created. The structure of space-time for the obtained solutions is investigated and the implications for the models of the galaxies are discussed. Copyright © 2017, The Authors. All rights reserved.

关键词： Gravitation

Wormhole solutions of RDM model

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arXiv 2017年

作者： Nikitin, Igor Department of High Performance Analytics Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt Augustin53757 Germany

The model of a spiral galaxy with radially directed flows of dark matter is extended by exotic matter, in a form of a perfect fluid with a linear anisotropic equation of state. The exotic matter is collected in the minimum of gravitational potential and opens a wormhole in the center of the galaxy. The flows of dark matter pass through the wormhole and form a mirror galaxy on the other side. The influence of model parameters to the shape of solution is studied, a solution matching parameters of Milky Way galaxy is computed. Copyright © 2017, The Authors. All rights reserved.

关键词： Galaxies

Informed machine learning - A taxonomy and survey of integrating prior knowledge into learning systems

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arXiv 2019年

作者： von Rueden, Laura Mayer, Sebastian Beckh, Katharina Georgiev, Bogdan Giesselbach, Sven Heese, Raoul Kirsch, Birgit Pfrommer, Julius Pick, Annika Ramamurthy, Rajkumar Walczak, Michal Garcke, Jochen Bauckhage, Christian Schuecker, Jannis The Fraunhofer IAIS Institute for Intelligent Analysis and Information Systems Sankt Augustin53757 Germany The Fraunhofer SCAI Institute for Algorithms and Scientific Computing Sankt Augustin53757 Germany The Fraunhofer ITWM Institute for Industrial Mathematics Kaiserslautern67663 Germany The Fraunhofer IOSB Institute for Optronics System Technologies and Image Exploitation Karlsruhe76131 Germany

Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads to the notion of informed machine learning. In this paper, we present a structured overview of various approaches in this field. We provide a definition and propose a concept for informed machine learning which illustrates its building blocks and distinguishes it from conventional machine learning. We introduce a taxonomy that serves as a classification framework for informed machine learning approaches. It considers the source of knowledge, its representation, and its integration into the machine learning pipeline. Based on this taxonomy, we survey related research and describe how different knowledge representations such as algebraic equations, logic rules, or simulation results can be used in learning systems. This evaluation of numerous papers on the basis of our taxonomy uncovers key methods in the field of informed machine learning. © 2019, CC BY.

关键词： Surveys

A Framework for Synthetic Power System Dynamics

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arXiv 2023年

作者： Büttner, Anna Plietzsch, Anton Anvari, Mehrnaz Hellmann, Frank Potsdam-Institute for Climate Impact Research Germany Fraunhofer Research Institution for Energy Infrastructures and Geothermal Systems Germany Fraunhofer Institute for Algorithms and Scientific Computing Germany

We present a modular framework for generating synthetic power grids that considers the heterogeneity of real power grid dynamics but remains simple and tractable. This enables the generation of large sets of synthetic grids for a wide range of applications. For the first time, our synthetic model also includes the major drivers of fluctuations on short-time scales and a set of validators that ensure the resulting system dynamics are plausible. The synthetic grids generated are robust and show good synchronization under all evaluated scenarios, as should be expected for realistic power grids. A software package that includes an efficient Julia implementation of the framework is released as a companion to the paper. © 2023, CC BY.

关键词： System theory

StarWatch: Radio Astronomical Monitoring in Virtual Environment

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StarWatch: Radio Astronomical Monitoring in Virtual Environm...

International Conference on Cyberworlds

作者： Stanislav Klimenko Valery Malofeev Igor Nikitin Institute of Computing for Physics and Technology Protvino Russia Pushchino Radio Astronomy Observatory Pushchino Russia Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin Germany

An application of Virtual Environment for real-time analysis of radio astronomical data is presented. Serving as an interface to radio astronomical databases or being applied to live data from the radio telescope, the application supports various data filters measuring signal-to-noise ratio (SNR), Doppler's drift, degree of signal localization on celestial sphere and other useful tools for signal extraction and classification. The application is created using Avango VE framework (***). The application has been used for analysis of radio astronomical data from SETI institute (***).

关键词： Signal to noise ratio Extraterrestrial measurements Radon Virtual environments Electronic mail Databases Telescopes

Deep neural networks and PIDE discretizations

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arXiv 2021年

作者： Bohn, Bastian Griebel, Michael Kannan, Dinesh Fraunhofer Center for Machine Learning Schloss Birlinghoven Sankt Augustin53754 Germany Fraunhofer Institute for Algorithms and Scientific Computing SCAI Schloss Birlinghoven Sankt Augustin53754 Germany Institute for Numerical Simulation University of Bonn Friedrich-Hirzebruch-Allee 7 Bonn53115 Germany

In this paper, we propose neural networks that tackle the problems of stability and field-of-view of a Convolutional Neural Network (CNN). As an alternative to increasing the network's depth or width to improve performance, we propose integral-based spatially nonlocal operators which are related to global weighted Laplacian, fractional Laplacian and inverse fractional Laplacian operators that arise in several problems in the physical sciences. The forward propagation of such networks is inspired by partial integro-differential equations (PIDEs). We test the effectiveness of the proposed neural architectures on benchmark image classification datasets and semantic segmentation tasks in autonomous driving. Moreover, we investigate the extra computational costs of these dense operators and the stability of forward propagation of the proposed neural *** Codes 65D15, 65L07, 68T05, 68W25, 47G20, 47G30 © 2021, CC BY.

关键词： Deep neural networks

High-level data mapping for clusters of SMPs

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High-level data mapping for clusters of SMPs

International Symposium on Parallel and Distributed Processing (IPDPS)

作者： S. Benkner T. Brandes Institute for Software Science University of Technology Vienna Austria GMD-German National Research Center for Information Technology SCAI-Institute for Algorithms and Scientific Computing Saint Petersburg Russia

A Minimum Set-Cover Problem with several constraints

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A Minimum Set-Cover Problem with several constraints

Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Jens Dörpinghaus Carsten Düing Vera Weil Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt Augustin Germany Department for Computer Science University of Cologne Germany

A lot of problems in natural language processing can be interpreted using structures from discrete mathematics. In this paper we will discuss the search query and topic finding problem using a generic context-based approach. This problem can be described as a Minimum Set Cover Problem with several constraints. The goal is to find a minimum covering of documents with the given context for a fixed weight function. The aim of this problem reformulation is a deeper understanding of both the hierarchical problem using union and cut as well as the non-hierarchical problem using the union. We thus choose a modeling using bipartite graphs and suggest a novel reformulation using an integer linear program as well as novel graph-theoretic approaches.

关键词： Bipartite graph Search engines Computer science Natural language processing Search problems Dynamic programming Expert systems