检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Design and Damping Characterization of Sandwich Composite Made of Particle-Filled Hollow Spheres and Steel Sheets

SSRN

引用

SSRN 2024年

作者： Zhou, Xin Penter, Lars Jehring, Ulrike Göhler, Hartmut Weißgärber, Thomas Ihlenfeldt, Steffen Chair of Machine Tools Development and Adaptive Control Institute of Mechatronic Engineering Technische Universität Dresden Germany Fraunhofer Institute for Machine Tools and Forming Technology IWU Germany Fraunhofer Institute for Manufacturing Technology and Advanced Materials IFAM Branch Lab Dresden Germany

Sandwich composites of particle-filled hollow sphere structures (PHSS) and steel plates offer outstanding lightweight and damping properties, making them ideal for high-speed machine tool components under dynamic loads. Previous research has focused on single particle-filled hollow sphere (PHS) and small-sized PHSS, leaving gap in understanding of the behavior of PHSS/steel sandwich composites. As a result, this material cannot yet be used in the design of machine tools. In this study, a test rig was developed, a design of experiments approach and response surface analysis were used to investigate the effect of design parameters (shell and core thickness), and particle parameters (filling ratio and particle size) on the damping performance of PHSS/steel sandwich. The experimental results indicate that the design parameters have a more significant influence on damping, so they should be given priority in the design of PHSS/steel sandwiches. Secondly, the correlation between design and particle parameters substantially affects the damping performance of the sandwich. Minimizing particle size, increasing filling ratio, thinning the face layer, and thickening the core layer significantly increase structural *** address the manufacturing tolerances of the cellular structure, this study develops a finite element (FE) model-based optimization approach that accurately determines the material parameters of PHSS. These parameters were used in a FE-model of PHSS/steel composite using identified contact parameters by minimizing the difference of measurement and simulation. It shows that the homogenized material model and linear model using global damping parameters accurately replicates the dynamic properties of PHSS/steel sandwich composite in low vibration modes. © 2024, The Authors. All rights reserved.

关键词： Damping

来源：评论

学校读者我要写书评

暂无评论

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

引用

Procedia CIRP 2024年 125卷 284-289页

作者： Uwe Scheithauer Stefan Holtzhausen Hajo Wiemer Fraunhofer Institute for Ceramic Technologies and Systems Group Additive and Hybrid Manufacturing Winterbergstr. 28 01277 Dresden Germany TUD Dresden University of Technology Institute of Machine Elements and Machine Design Chair of Virtual Product Development Group Advanced Geometric Modeling And Analysis 01062 Dresden Germany TUD Dresden University of Technology Institute of Mechatronic Engineering Chair of Machine Tools Development and Adaptive Controls Department of Machine Data Processing 01062 Dresden 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.

关键词： ceramics: additive manufacturing component design data science biomanufacturing

来源：评论

学校读者我要写书评

暂无评论

Reusing OPC UA information models in the Asset Administration Shell

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

引用

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.

关键词：

来源：评论

学校读者我要写书评

暂无评论

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...

引用

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

来源：评论

学校读者我要写书评

暂无评论

Mycelium-based-composites – Vision for substitution of fossil-based materials

引用

Procedia CIRP 2024年 125卷 78-83页

作者： Steffen Ihlenfeldt Stefan Schillberg Christoph Herrmann Simon Vogel Robar Arafat Simon Harst Chair of Machine Tools Development and Adaptive Controls Insitute of Mechatronic Engineering TU Dresden Helmholtzstraße 7a 01069 Dresden Germany Fraunhofer Institute for Machine Tools and Forming Technology IWU Pforzheimer Str. 7a 01189 Dresden Germany Chair of Molecular Biotechnology Worringerweg 1 RWTH Aachen University 52074 Aachen Germany Fraunhofer Institute for Molecular Biology and Applied Ecology IME Forckenbeckstr. 6 52074 Aachen Germany Chair of Sustainable Manufacturing & Life Cycle Engineering Institute of Machine Tools and Production Technology IWF Technische Universität Braunschweig Langer Kamp 19b 38106 Braunschweig Germany Fraunhofer Institute for Surface Engineering and Thin Films IST Riedenkamp 2 38108 Braunschweig Germany

For decades, fossil-based materials have formed the basis of an almost endless range of technical products. Through variable chemical composition and several additives, especially plastics can have a wide range of properties, which form the basis for the diversity of plastic-based products. While this variability enables many sustainability strategies, such as lightweighting, it is also impeding a fully circular economy. Therefore, in recent years, a number of new raw materials have been developed, but they can only cover a very limited part of the wide range of properties of fossil-based plastics. Another promising class of materials are composites based on fungal mycelium. However, these are mainly limited to the consumer sector, e.g. vegan leather, where there is smaller demand for durability, functionality etc. The step from consumer to engineering materials require the production (or growth) process to be reproducible within the necessary quality requirements. Cyber-physical production systems have the potential to realise necessary technical qualities despite e.g. quality fluctuations of the raw material and production processes that are susceptible to interference. For this reason, this paper analyses the state of the art in production of mycelium based composites, shows the existing gaps and draws a vision to close these gaps.

关键词： cyber-physical production biological transformation mycelium-based composites

来源：评论

学校读者我要写书评

暂无评论

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

引用

Procedia CIRP 2023年 118卷 537-542页

作者： Michal Rytir Jonas Rönnecke Albrecht Hänel Petr Kolar Steffen Ihlenfeldt Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Department of Industrial Production and Automation Jugoslávských partyzánů 1580/3 160 00 Prague 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 01062 Dresden Germany

关键词： In-process monitoring leakage model cutting force model spindle current edge computing

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

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.

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：