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20th CIRP Conference on Modeling of Machining Operations in Mons, CIRP CMMO 2025

作者： Jing, Yuan Gong, Guanchen Hänel, Albrecht Ihlenfeldt, Steffen Fraunhofer Insitute for Machine Tools and Forming Technology IWU Chemnitz Germany Chair of Machine Tools Development and Adaptive Controls Institute Fo Mechatronic Engineering Technische Universität Dresden Dresden Germany

Industry 4.0 has significantly improved data efficiency by leveraging key technologies such as the Internet of Things and machine Learning. Among these key technologies, digital twins stand out by offering a promising approach to intelligently utilize this data. In the virtual representation of a physical asset, data reflects the conditions of the physical entity, while models simulate and predict its behavior. In this paper, a hybrid cutting force model is proposed for digital twins of helical end mills, focusing on cutting force analysis during the utilization phase of the machining process. This model combines a fairly mature physical process modelling approach with a data-driven method, specifically a neural network trained on real process data, to address the limitations inherent in their respective applications. The physics-based model provides meaningful constraints on the neural network's training, ensuring reliable cutting force prediction, particularly in scenarios with limited process data availability. The cutter's profile, generated by the geometric model, and the cutter-workpiece engagement maps, derived from the virtual machining model, together serve as inputs for the hybrid cutting force model. © 2025 The Author(s).

关键词： Machining centers

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Integrating Hybrid Physics-Data Approaches for Enhanced Cutting Force Modeling in Digital Twins of Helical End Mills

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Procedia CIRP 2025年 133卷 358-363页

作者： Yuan Jing Guanchen Gong Albrecht Hänel Steffen Ihlenfeldt Fraunhofer Insitute for Machine Tools and Forming Technology IWU Chemnitz Germany Chair of Machine Tools Development and Adaptive Controls Institute fo Mechatronic Engineering Technische Universität Dresden Dresden Germany

Industry 4.0 has significantly improved data efficiency by leveraging key technologies such as the Internet of Things and machine Learning. Among these key technologies, digital twins stand out by offering a promising approach to intelligently utilize this data. In the virtual representation of a physical asset, data reflects the conditions of the physical entity, while models simulate and predict its behavior. In this paper, a hybrid cutting force model is proposed for digital twins of helical end mills, focusing on cutting force analysis during the utilization phase of the machining process. This model combines a fairly mature physical process modelling approach with a data-driven method, specifically a neural network trained on real process data, to address the limitations inherent in their respective applications. The physics-based model provides meaningful constraints on the neural network’s training, ensuring reliable cutting force prediction, particularly in scenarios with limited process data availability. The cutter’s profile, generated by the geometric model, and the cutter-workpiece engagement maps, derived from the virtual machining model, together serve as inputs for the hybrid cutting force model.

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Coupled CFD model of tool environment and workspace to determine the convective heat transfer in jet cooling of milling processes in machine tools 20

Coupled CFD model of tool environment and workspace to deter...

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20th CIRP Conference on Modeling of Machining Operations in Mons, CIRP CMMO 2025

作者： Brier, Steffen Geist, Alexander Glänzel, Janine Naumann, Christian Regel, Joachim Dix, Martin Ihlenfeldt, Steffen Chemnitz University of Technology Professorship Production Systems and Processes Reichenhainer Str. 70 Chemnitz09126 Germany Fraunhofer-Institute for Machine Tools and Forming Technology IWU Reichenhainer Str. 88 Chemnitz09126 Germany Technical University Dresden Institute for Machine Tools Development and Adaptive Controls Helmholtzstraße 10 Dresden01069 Germany

The responsible use of resources is an essential part of the manufacturing of industrial products. This includes the economical use of cooling lubricant and requires precise knowledge of the cooling mechanisms and their effect on the accuracy of the machine tool and thus the thermal error. A temporal and spatial resolution of the dynamic coolant flow near the tool and in the entire workspace in a single model would require a large simulation time. Therefore, a composite model was developed, that consists of a near-tool model and a larger surrounding workspace model. The required static near-tool cooling lubricant distribution is obtained via data discretization methods from a separate static simulation that resolves the turbulence of the cooling lubricant created by the tool rotation. The identified coolant distribution is integrated into a CFD near-tool model (with simplified tool geometry) which is coupled with a surrounding CFD workspace model. The workspace model is thus able to identify the effects of the coolant wetting on the machine surface temperature and finally, using thermo-elastic FEM simulations, on the resulting thermal error. This approach allows the composite model to simulate the entire workspace with reduced simulation effort and map the coolant-influenced heat transfer coefficients on the machine surface. © 2025 The Author(s).

关键词： Milling machines

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Coupled CFD model of tool environment and workspace to determine the convective heat transfer in jet cooling of milling processes in machine tools

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Procedia CIRP 2025年 133卷 280-285页

作者： Steffen Brier Alexander Geist Janine Glänzel Christian Naumann Joachim Regel Martin Dix Steffen Ihlenfeldt Chemnitz University of Technology Professorship Production Systems and Processes Reichenhainer Str. 70 09126 Chemnitz Germany Fraunhofer-Institute for Machine Tools and Forming Technology IWU Reichenhainer Str. 88 09126 Chemnitz Germany Technical University Dresden Institute for Machine Tools Development and Adaptive Controls Helmholtzstraße 10 01069 Dresden Germany

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Automated porosity assessment of parts produced by Laser Powder Bed Fusion using Convolutional Neural Networks 54

Automated porosity assessment of parts produced by Laser Pow...

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54th CIRP Conference on Manufacturing Ssystems, CMS 2021

作者： Klein, Jan Jaretzki, Martin Schwarzenberger, Michael Ihlenfeldt, Steffen Drossel, Welf-Guntram Technische Universität Dresden Department of Machine Tools Development and Adaptive Controls 01069 Germany NOthnitzer Str. 44 Dresden01187 Germany

Laser Powder Bed Fusion (LPBF) is especially interesting for applications in industries with high quality requirements. There are different expensive and time-consuming strategies for quality assurance. A cheaper and faster approach is to analyze the data acquired during fabrication. In this work Convolutional Neural Networks (CNN) are investigated as a tool for data analysis of meltpool monitoring data. The goal is to automatically distinguish between porous and non-porous part regions. Therefore, the training data is categorized based on CT-scans of the test specimens. For increased interpretability of the results, Gradient-Weighted Class Activation Maps (Grad-CAM) are used. © 2021 Elsevier B.V.. All rights reserved.

关键词： Porosity

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New gentle extraction method of hemp bast strips for use as bio-based reinforcing fibers in highly loaded fiber composites

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Procedia CIRP 2024年 125卷 178-183页

作者： Steffen Ihlenfeldt Jens Müller Christoph Peukert Ludwig Rogall TUD Dresden University of Technology Chair of Machine Tools Development and Adaptive Controls (LWM) Dresden 01069 Germany Fraunhofer Institute for Machine Tools and Forming Technology IWU Chemnitz 09126 Germany

Nowadays, fiber composites are among the most important construction materials in lightweight design. Increasingly, attempts are being made to replace fossil raw materials with bio-based materials in order to improve their recyclability and reduce greenhouse gas emissions. While processes suitable for industrial use already exist for the production of biopolymers as matrix materials, there is still a considerable need for research in the production of bio-based reinforcing fibers. For hemp bast strips (HBS), promising utilization options arise due to very good mechanical properties. However, current processes for extracting such raw materials from bast fiber plants result in damage to the fiber structure because they use biochemical decomposition processes and/or squeeze and crush large disordered amounts of stalk along their entire length. This strongly reduces the application potential of the raw materials. A newly developed principle extracts the HBS with minimal impact by dividing single hemp stalks only at one end and then isolating them from the wood core by pulling on the resulting wood fragments. In this manner, the fiber structure and the good mechanical properties are largely retained and the HBS obtained can be stored in parallel alignment for further processing with little effort.

关键词： natural fiber composites hemp decortication mechanical properties industrial application

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Automated porosity assessment of parts produced by Laser Powder Bed Fusion using Convolutional Neural Networks

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Procedia CIRP 2021年 104卷 1434-1439页

作者： Jan Klein Martin Jaretzki Michael Schwarzenberger Steffen Ihlenfeldt Welf-Guntram Drossel Technische Universität Dresden Chair of Machine Tools Development and Adaptive Controls 01069 Dresden Fraunhofer Institute for Machine Tools and Forming Technology (IWU) Nöthnitzer Str. 44 01187 Dresden Germany

关键词： Additive Manufacturing Laser Beam Melting LPBF Process Monitoring Quality Assessment Porosity In-process sensing Melt pool monitoring CNN Neural Networks

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Reusing OPC UA information models in the Asset Administration Shell

Reusing OPC UA information models in the Asset Administratio...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Weiss Arno Reichelt Dirk SAP SE Chair of Machine Tools Development and Adaptive Controls TU Dresden Dresden Germany Department of Informatics HTW Dresden Fraunhofer IWU Dresden Germany

OPC UA and the Asset Administration Shell cover neighboring domains in modern industrial information architectures. Since their meta-models differ fundamentally, correct integration is vital for sustained interoperability. This paper shows how enterprise information systems benefit from using transformed OPC UA information models as a proxy for shopfloor transparency. All transformation rules facilitating this meta-model mapping are motivated and specified. When implemented, a new Submodel mirroring an OPC UA Nodeset can be built up automatically carrying all necessary context from its origins into the realm of cross-company data exchange.

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Influence of engagement parameters on estimation of tangential cutting force using high-frequency process data of the machine tool 16

Influence of engagement parameters on estimation of tangenti...

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16th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2022

作者： Rytir, Michal Rönnecke, Jonas Hänel, Albrecht Kolar, Petr Ihlenfeldt, Steffen Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Department of Industrial Production and Automation Jugoslávských partyzánu 1580/3 Prague160 00 Czech Republic Fraunhofer Institute for Machine Tools and Forming Technology IWU Department of Production Digital Twin Nöthnitzer Straße 44 Dresden Germany Technische Universität Dresden Institute of Mechatronic Engineering Chair of Machine Tools Development and Adaptive Controls Dresden01062 Germany

The machining of high-value products needs advanced quality control to avoid the risk of expensive follow-up operations performed on parts with unacceptable quality. This situation requires the in-process or close to in-process control of the machining operation. The tangential cutting force is a parameter that characterizes the force interaction between the specific workpiece material and the specific tool geometry. This paper presents a method for a tangential cutting force calculation along the given tool path based on processing of the high-frequency data acquired from the machine tool control system with support of the geometric material removal model. The calculated data are compared with the reference dynamometer results. The influence of the variable engagement parameters is demonstrated and discussed from the perspective of the in-process workpiece quality control. © 2023 Elsevier B.V.. All rights reserved.

关键词： machine tools

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Additive Manufacturing of Ceramics opens the door to Components for Biomanufacturing Applications with high Functionality 6

Additive Manufacturing of Ceramics opens the door to Compone...

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6th CIRP Conference on BioManufacturing, BioM 2024

作者： Scheithauer, Uwe Holtzhausen, Stefan Wiemer, Hajo Fraunhofer Institute for Ceramic Technologies and Systems Group Additive and Hybrid Manufacturing Winterbergstr. 28 Dresden01277 Germany TUD Dresden University of Technology Institute of Machine Elements and Machine Design Virtual Product Development Group Advanced Geometric Modeling And Analysis Dresden01062 Germany TUD Dresden University of Technology Institute of Mechatronic Engineering Machine Tools Development and Adaptive Controls Department of Machine Data Processing Dresden01062 Germany

Significant advancements in production technology, materials, component design, and data science foster innovation across industries, including biomanufacturing. Ceramic materials, adapted additive manufacturing processes, and novel construction methods hold immense innovation potential due to ceramics' exceptional thermal, chemical, and mechanical properties. Additive manufacturing enables the creation of intricate ceramic components with unprecedented functionality. Hybridizing manufacturing technologies and materials facilitates direct integration of additional features like sensors and actuators. Despite their potential, ceramic materials remain underutilized. This article aims to spark a discourse on leveraging high-performance ceramic components in biomanufacturing, leveraging superior material properties, innovative design methods, additive manufacturing, process data analysis, and AI. © 2024 The Authors.

关键词： Ceramic materials

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