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Touch-based Augmented Reality Marking Techniques on Production Parts

Procedia CIRP 2022年 107卷 517-521页

作者： Arno Schmetz Martin Bellgardt Vincent Wehrwein Torsten Kuhlen Christian Brecher Fraunhofer Institute for Production Technology IPT Steinbachstr. 17 Aachen 52074 Germany Fraunhofer Research Institution for Battery Cell Production FFB Mendelstr. 11 Münster 48149 Germany Visual Computing Institute RWTH Aachen University Kopernikusstr. 6 Aachen 52074 Germany Laboratory for Machine Tools WZL RWTH Aachen University Steinbachstr. 19 Aachen 52074 Germany

In the machining of production parts, the quality of the surface is subject to a variety of influences. Tool wear, thermal effects and vibrations can affect the surface quality of the machined object drastically. Some of those effects on the surface, like marks from vibrations, can be seen by machining experts directly on the object without measurement hardware or metrology systems. Those pieces of information held by the experts are valuable to the surface finishing staff, the metrology staff and data scientists, who could use them to improve both the product and the process. However, to enable this exchange of information, the relevant data needs to be indicated in a spatially resolved and machine-readable format. Additionally, the experts need to be able to mark the areas for all kinds of different requirements in a fast and suitable manner without possibly damaging the part itself. Our approach for this task is using a touch-based Augmented Reality system that allows the users to mark these areas of interest on the production part digitally without touching the production part itself. Therefore, we propose two different marking techniques - Shift&Freeze Marking and Relative Marking - based on literature from pointing tasks. We performed user trials to evaluate the usability, task load and overall performance of those techniques showing a general suitability for touch-based AR marking of relevant regions on productions parts.

关键词： Augmented Reality Data Augmentation Data Preprocessing Industry 4.0

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Retrieving properties of manufacturing systems from traceability data for performance evaluation and material flow simulation

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Procedia CIRP 2021年 104卷 20-25页

作者： Heiner Reinhardt Marc Münnich Bastian Prell Roman Arnold Fabian Krippner Marek Weber Frank Seifert Matthias Putz Fraunhofer Institute for Machine Tools and Forming Technology IWU Reichenhainer Straße 88 09126 Chemnitz Germany Technical University of Applied Science Wildau Hochschulring 1 15745 Wildau Germany Volkswagen Sachsen GmbH Motorenwerk Chemnitz Kauffahrtei 47 09120 Chemnitz Germany Chemnitz University of Technology Straße der Nationen 62 09111 Chemnitz Germany

The operation of manufacturing systems is increasingly accompanied by data-driven continuous improvement processes and product traceability is required. Commonly, radio-frequency identification (RFID) technology is applied to track the flow of a uniquely-identifiable workpiece along various stations or waypoints within a factory. Based on an automotive use case, this paper describes how to analyze the resulting traceability data in order to identify several properties of a manufacturing system. The acquired knowledge can support performance evaluation and facilitate model building for material flow simulation as a foundation for digital twins and cyber-physical production systems.

关键词： Knowledge management Algorithm Analysis Condition monitoring Manufacturing system Complexity Man-machine system Pattern recognition

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Methodology for the self-optimizing determination of additive manufacturing process eligibility and optimization potentials in toolmaking

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Procedia CIRP 2022年 107卷 1539-1544页

作者： Tammo Dannen Benedikt Schindele Marcel Prümmer Kristian Arntz Thomas Bergs Laboratory for Machine Tools and Production Engineering (WZL) RWTH Aachen University Campus-Boulevard 30 52074 Aachen Germany Fraunhofer Institute for Production Technology IPT Steinbachstraße 17 52074 Aachen Germany Lehrstuhl für Fertigungsverfahren und Werkzeugmaschinen FH Aachen Goethestraße 1 52064 Aachen Germany

Additive Manufacturing (AM) of metallic workpieces faces a continuously rising technological relevance and market size. Producing complex or highly strained unique workpieces is a significant field of application, making AM highly relevant for tool components. Its successful economic application requires systematic workpiece based decisions and optimizations. Considering geometric and technological requirements as well as the necessary post-processing makes deciding effortful and requires in-depth knowledge. As design is usually adjusted to established manufacturing, associated technological and strategic potentials are often neglected. To embed AM in a future proof industrial environment, software-based self-learning tools are necessary. Integrated into production planning, they enable companies to unlock the potentials of AM efficiently. This paper presents an appropriate methodology for the analysis of process-specific AM-eligibility and optimization potential, added up by concrete optimization proposals. For an integrated workpiece characterization, proven methods are enlarged by tooling-specific figures. The first stage of the approach specifies the model’s initialization. A learning set of tooling components is described using the developed key figure system. Based on this, a set of applicable rules for workpiece-specific result determination is generated through clustering and expert evaluation. Within the following application stage, strategic orientation is quantified and workpieces of interest are described using the developed key figures. Subsequently, the retrieved information is used for automatically generating specific recommendations relying on the generated ruleset of stage one. Finally, actual experiences regarding the recommendations are gathered within stage three. Statistic learning transfers those to the generated ruleset leading to a continuously deepening knowledge base. This process enables a steady improvement in output quality.

关键词： Additive manufacturing AM Laser-Powder Bed Fusion L-PBF Binder Jetting BJ Directed Energy Deposition DED Clustering Cluster analysis Candidate selection Part identification Workpiece optimization Tooling Key figure system Process automation Manufacturing process selection Software

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Oxidized tool electrodes for optimized electro-discharge drilling 20

Oxidized tool electrodes for optimized electro-discharge dri...

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20th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2020

作者： Uhlmann, E. Polte, M. Streckenbach, J. Dinh, N.C. Börnstein, J. Wolf, C.-S. Camin, B. Institute for Machine Tools Factory Management IWF Technische Universität Berlin Germany Fraunhofer Institute for Production Systems and Design Technology IPK Germany Institute for Materials Science and Technology Chair of Metallic Materials Technische Universität Berlin Germany

ISBN: (纸本)9780995775176

The production of components for automotive and aerospace industry by conventional machining is still limited by hardness and strength of the workpiece materials. Electro-discharge drilling is used for machining electrically conductive materials without any limitation due to mechanical properties. Electro-discharge drilling causes debris in the working gap, which leads to arcs and short circuits on the lateral surface with negative effects on processing results and process duration. Due to these arcs and short circuits limited drilling depth, increased tool wear, conicity of boreholes and process instabilities are still challenges in electro-discharge drilling. In this work, a new approach for passivation of the tool electrode material by oxidation is shown. Different oxidation processes for tool electrodes made of brass were applied and analysed. Surface modified tool electrodes were used for electro-discharge drilling of Elmax, SuperClean from the company VOELSTAPINE AG, Linz, Austria. The investigation was carried out on the machine tool AGIETRON Compact 1 from the company AGIE SA, Losone, Switzerland, for machining of through holes with a depth of t = 33.5 mm using different surface modified tool electrodes with a diameter of d = 0.8 mm. First results show a reduction of the erosion duration by 20 % for the application of thermally oxidized brass. The investigation of different defined oxide layer thicknesses and electrical resistances for specific applications are part of this ongoing work. This work is funded by the German Research Foundation (DFG). Copyright © Proceedings of the 20th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2020. All rights reserved.

关键词： Oxidation

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Experimental setup for the investigation of the transport of different gas bubble sizes during electrochemical machining based on similarity theory using particle image velocimetry

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Procedia CIRP 2022年 113卷 417-421页

作者： E. Tchoupe L. Heidemanns A. Klink T. Herrig M. Klaas D. Lauwers W. Schröder Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University Campus-Boulevard 30 Aachen 52074 Germany Fraunhofer Institute for Production Technology IPT Steinbachstraße 17 52074 Aachen Germany Chair of Fluid Mechanics and Institute of Aerodynamics (AIA) RWTH Aachen University Wüllnerstr. 5a 52062 Aachen Germany

The major advantage of electrochemical machining (ECM) is the ability to machine materials regardless of their mechanical properties such as hardness or temperature resistance. This property of the ECM process makes it ideal for processing difficult-to-machine materials such as high-performance nickel-based alloys and single-crystal alloys. However, it is difficult to predict a priori the appropriate tool electrode for a given workpiece contour. This is not only due to the constantly changing properties of the electrolyte during machining, but also to the transient nature of the working gap. To simplify tool design in ECM, a better understanding of the various phenomena that occur, such as gas formation and its propagation in the working gap, is necessary. This paper presents an experimental investigation of the transport of different gas bubble sizes during ECM by up-scaling the working gap according to the principles of similarity theory. The multiphase flow is analyzed using 3D particle image velocimetry, which will serve for validation of future simulation models.

关键词： Electrochemical machining Particle Image Velocimetry Similarity approach

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Investigation of cutting mechanisms in the machining of Ceramic Matrix Composites (CMCs)

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Procedia CIRP 2021年 101卷 330-333页

作者： T. Bergs P. Ganser D. Fruh J. Reisberg Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University Campus-Boulevard 30 52074 Aachen Germany Fraunhofer Institute for Production Technology IPT Steinbachstrasse 17 52074 Aachen Germany

Ceramic Matrix Composites (CMCs) are increasingly used in numerous engineering fields, e.g. the aerospace, automotive, energy & nuclear industry due to their higher permissible material temperature and their lower weight compared to metallic materials. Thanks to the quasi-ductile material behaviour, it is possible to carry out time- and cost-efficient finishing with a geometrically defined cutting edge. In this study the cutting mechanisms are analysed in fundamental cutting tests on an orthogonal cutting test bench by variance of the undeformed chip thickness. For the evaluation cutting force measurements, high-speed recordings, surface qualities measurements and chip analyses were conducted.

关键词： Composites Machining Cutting

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The Concept of Digital Twin and Digital Shadow in Manufacturing

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Procedia CIRP 2021年 101卷 81-84页

作者： Thomas Bergs Sascha Gierlings Thomas Auerbach Andreas Klink Daniel Schraknepper Thorsten Augspurger Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University Campus Boulevard 30 52074 Aachen Germany Fraunhofer Institute for Production Technology Steinbachstraße 17 52074 Aachen Germany

There still is a lacking comprehensive understanding of the relevant terms Digital Twin (DT) and Digital Shadow (DS) in particular within the application on the field of manufacturing technology. However, a common, transparent understanding of the concept and its requirements interferes with the implementation and application of these powerful tools. In this sense, the following publication gives a new definition of the concept within the field of manufacturing technology and shows specific applications.

关键词： Manufacturing Digital Twin Digital Shadow Process chain Process model

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Expert System for the machine Learning Pipeline in Manufacturing

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IFAC-PapersOnLine 2021年第1期54卷 128-133页

作者： M. Frye J. Krauß R.H. Schmitt Fraunhofer Institute for Production Technology IPT Steinbachstraße 17 Aachen 52074 Germany Laboratory for Machine Tools WZL RWTH Aachen University Cluster Production Engineering 3A 540 Aachen 52074 Germany

Many success stories already exist with regard to the implementation of machine Learning (ML) and Artificial Intelligence (AI) in manufacturing. However, companies with traditional focus on production technologies face challenges in conducting AI-projects successfully and lack knowledge of which obstacles may occur and how to decide in the implementation phase. In this paper, we develop an approach that focuses on the methodological necessary steps for the successful application of ML and AI in manufacturing. Optimization potentials and decisions to be made are outlined in every step. A main focus is put on optimizing hyperparameters of ML-models as one promising approach for improving overall ML-model performance. An expert system is presented that enables the selection of suitable hyperparameter optimization techniques. The concept is validated based on manufacturing of compressor components of a turbofan engine.

关键词： machine Learning Artificial Intelligence Manufacturing Production Expert Systems Hyperparameter Optimization

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Development and Validation of Bone Models using Structural Dynamic Measurement Methods

Development and Validation of Bone Models using Structural D...

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作者： Neupetsch, Constanze Hensel, Eric Werner, Michael Meißner, Sven Troge, Jan Drossel, Welf-Guntram Rotsch, Christian Chemnitz University of Technology Professorship for Adaptronics and Lightweight Design in Production Chemnitz Germany Fraunhofer Institute for Machine Tools and Forming Technology IWU Chemnitz/Dresden Germany

Vibration measurement and signal analysis methods are common to evaluate the functionality and characteristics of technical components in different industrial and scientific areas. Modal analysis for example is a standard method to characterize the dynamic behavior of a structure and enables the development of validated bone models. The state of the art of analyzing bone structures does not include the modal damping, although it has a significant influence on the dynamic characteristics. Within the presented investigations, the modal analyses have been performed contactless with respect to excitation and response acquisition, which implies that there are no influences of shakers or sensor couplings. Therefore, an automatic impulse hammer and a 3D Scanning Laser Doppler Vibrometer were used for excitation and response detection. Various supports of the test specimens, surface pretreatments, excitation points and excitation impulses were examined to optimize the measurement setup and process. Experimental modal analysis data were analyzed by curve fitting methods to determine the modal parameters. To evaluate different structures and effects of damping, 3D printed artificial bones and animal in vitro bones were used to perform the measurements. To produce the cortical layer of the artificial bone models, volume models were generated based on medical image data and printed by polyamide-based selective laser sintering. The cancellous bone was represented by different foam fillings for the artificial bones. Thereby, the variation of the porosity was achieved by using different mixing ratios of polyurethane foam and hardener. Furthermore, the modal damping parameters were determined from the measurement of animal bones. The measurement time was optimized during the practical implementation of the parameter determination to minimize the influence of drying and decomposition processes on the measurement results. © 2019 by Walter de Gruyter Berlin/Boston.

关键词： Structural dynamics

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Benchmarking of Data Preprocessing Methods for machine Learning-Applications in Production

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Procedia CIRP 2021年 104卷 50-55页

作者： Maik Frye Johannes Mohren Robert H. Schmitt Fraunhofer Institute for Production Technology IPT Steinbachstraße 17 52074 Aachen Germany Laboratory for Machine Tools WZL RWTH Aachen University Cluster Production Engineering 3A 540 Aachen 52074 Germany

The application of machine learning (ML) is becoming increasingly common in production. However, many ML-projects in production fail due to poor data quality. To increase the quality, data needs to be preprocessed. Hundreds of methods exist for data preprocessing (DPP) that are selected manually depending on use-case requirements. For these reasons, DPP is currently performed unstructured and accounts for 80 % of ML-projects’ duration. Thus, we introduce a structured DPP-approach, in which DPP-methods are recommended based on production use-case requirements by benchmarking identified DPP-methods according to ML-model performance on five data sets. The approach is validated through two new use-cases.

关键词： Data Preprocessing Data Preparation machine Learning Artificial Intelligence Production Manufacturing

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