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Robot machining of thin-walled workpieces with automatically reconfigurable fixturing through feature analysis

Procedia CIRP 2023年 120卷 1065-1070页

作者： Andreas Schütz Armin Lechler Alexander Verl Jürgen Fleischer Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) University of Stuttgart Seidenstrasse 36 70174 Stuttgart Germany Institute of Production Science (wbk) Karlsruhe Institute of Technology Gotthard-Franz-Straße 5 76131 Karlsruhe Gremany

Machining of large scale thin-walled workpieces pose high requirements for fixturing because of large stiffness changes due to material removal. Automatically reconfigurable fixtures that allow in-process reconfiguration are a promising approach. In this paper, an efficient approach based on feature analysis is proposed for fixture reconfiguration planning. For this, material removal is simulated. At discrete points in time, feature analysis is performed and an approximated local stiffness map is calculated with weight functions derived from the features’ parameterizations. Viable fixture configurations are then identified through root finding and validated through simulation in Ansys and experimentally with a fixture jig. In both validations, workpiece deformation for drilling operations could be reduced to an acceptable level enabling the implementation's future use in industrial application.

关键词： industrial robot robot machining fixture reconfigurable filexible

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Modeling and Identification of Load-Dependent Properties for Electrically Preloaded Rack-and-Pinion Drives

Modeling and Identification of Load-Dependent Properties for...

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European control Conference (ECC)

作者： Valentin Leipe Lukas Steinle Xabier Agirre Bikuña Armin Lechler Alexander Verl Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Stuttgart Germany Mondragon Unibertsitatea - Faculty of Engineering Arrasate-Mondragon Spain

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Rack-and-pinion drives are preferred feed drives for long travel distances and heavy loads in large machine tools. One of the advantages compared to other feed drive systems is consistent stiffness regardless of travel length. A primary challenge with rack-and-pinion drives is the achievable accu-racy due to backlash. To compensate for backlash, electrically preloaded systems are commonly used in machine tools. In the case of electrical preload between two motors, the system is more complex to identify because the feed drive specific properties cannot be directly assigned to the respective drive train. To address this issue, this paper presents a novel method for modeling and identifying the load-dependent stiffness and damping of an electrically preloaded system. For this purpose, a mathematical modeling based on experimental data from a test bench with industrial components is presented to separate the drive train specific properties. This allows the system behavior to be modeled more accurately and used for control approaches.

关键词： Damping Accuracy Drives Motors Mathematical models Data models machine tools

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Integration Challenges for Digital Twin Systems-of-Systems

Integration Challenges for Digital Twin Systems-of-Systems

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IEEE/ACM International Workshop on Software engineering for Systems-of-Systems (SESoS)

作者： Judith Michael Jé rô me Pfeiffer Bernhard Rumpe Andreas Wortmann Software Engineering RWTH Aachen University Germany Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) University of Stuttgart Germany

Research and industry leverage digital twins to monitor and control (cyber-physical) systems in various domains. For their efficient engineering, these twins need to become Systems-of-Systems (SoS), in which digital twins of smaller systems (e.g., a production machine) become parts of digital twins of larger systems (e.g., a factory). Yet, research on digital twins as SoS largely ignores reusing digital twins in SoS. Based on our experience in engineering digital twins with experts from various domains related to production systems engineering, we present insights on the challenges of composing and integrating that need to be addressed for efficient engineering of digital twins as SoS. These insights may guide future research on engineering digital twins as well as practitioners considering the challenges in building and composing digital twin systems-of-systems.

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Selection and Design Process for End-Effectors

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Procedia CIRP 2024年 130卷 969-976页

作者： Tobias Eberhardt Walter Schaaf Alexander Verl Graduate School of Excellence advanced Manufacturing Engineering (GSaME) University of Stuttgart Nobelstraße 12 70569 Stuttgart Germany Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) University of Stuttgart Seidenstraße 36 70174 Stuttgart Germany J. Schmalz GmbH Johannes-Schmalz-Straße 1 72293 Glatten Germany

Due to increasing economic competition, many companies are automating their production processes by means of handling systems. One very important component of any handling system is the end-effector, as it comes into direct contact with the handling object. Therefore, selecting and designing the best end-effector is of enormous importance for the safe and successful operation of handling systems. This paper presents a concept of a process for the systematic selection and design of end-effectors. The concept presented here exemplified for vacuum suction grippers. A first implementation showed the feasibility of the concept.

关键词： End-Effector Vacuum Handling System Selection Process Design Process Recommendation System

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A Novel Approach for Unsupervised Sensor Error Classification 57

A Novel Approach for Unsupervised Sensor Error Classificatio...

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18th IFAC Workshop on Time Delay Systems, TDS 2024

作者： Durnagöz, Samiha Eberhardt, Tobias Fuchs, Felix Reimann, Peter Ackermann, Thomas Fiedler, Maik Mayer, Mathias Huber, Marco F. Verl, Alexander Graduate School of Excellence Advanced Manufacturing Engineering GSaME University of Stuttgart Nobelstraße 12 Stuttgart70569 Germany Institute for Control Engineering of Machine Tools and Manufacturing Units ISW University of Stuttgart Seidenstraße 36 Stuttgart70174 Germany Institute of Industrial Manufacturing and Management IFF University of Stuttgart Allmandring 35 Stuttgart70569 Germany Universitätsstraße 38 Stuttgart70569 Germany AUDI AG NSU-Straße 1 Neckarsulm74172 Germany J. Schmalz GmbH Johannes-Schmalz-Straße 1 Glatten72293 Germany

The precise detection and classification of sensor errors is crucial for the optimal operation of smart manufacturing systems. Sensor errors, which can manifest in various forms, such as drifts or constant values, significantly challenge data integrity. This work introduces a novel approach that combines unsupervised learning and system simulations to detect and classify relevant sensor errors. Our approach avoids extensive manual labeling and uses only error-free simulated time series data to create initial embeddings of sensor data. This establishes a baseline for normal sensor behavior. To facilitate a precise identification of error types through a classifier, simulated sensor errors are introduced to create context-aware embeddings. We further analyze and compare conventional to advanced embedding methods such as TS2Vec. We apply our approach to a synthetic dataset obtained from a vacuum handling system and accurately identify and classify relevant sensor error types. Our work contributes to advancing operational efficiency and reliability in automated manufacturing processes within the Industry 4.0 framework by improving the detection capabilities for sensor errors. © 2024 The Authors.

关键词： Smart manufacturing

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Model-Based DevOps: Foundations and Challenges

Model-Based DevOps: Foundations and Challenges

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Conference on Model Driven engineering Languages and Systems Companion (MODELS-C)

作者： Benoit Combemale Jean-Marc Jézéquel Quentin Perez Didier Vojtisek Nico Jansen Judith Michael Florian Rademacher Bernhard Rumpe Andreas Wortmann Jingxi Zhang University of Rennes 1 Rennes France Software Engineering RWTH Aachen University Aachen Germany Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) University of Stuttgart Stuttgart Germany

Time-to-market and continuous improvement are key success indicators to deliver for Industry 4.0 Cyber-Physical Systems (CPSs). There is thus a growing interest in adapting DevOps approaches coming from software systems to CPSs. However, CPSs are made not only of software but also of physical parts that need to be monitored at runtime. In this paper, we claim that Model-Driven engineering can facilitate DevOps for CPSs by automatically connecting a CPS design model to its runtime monitoring, in the form of a digital twin.

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Edge-Cloud Automation of manufacturing machines: Analysis of Potential Designs and their Impact on the machine Performance

Edge-Cloud Automation of Manufacturing Machines: Analysis of...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Marco Giani Timur Tasci Patrick Wiener Alexander Verl Robert Bosch GmbH Renningen Germany Institute for Control Engineering of Machine Tools and Manufacturing Units Stuttgart Germany

The migration of industrial automation systems to an edge-cloud hosted on factory premises can reduce the operation costs of a manufacturing line and increase its flexibility thanks to computation scalability and use of cloud-native operation concepts. One limiting factor in the realization of edge-cloud automation systems (ECASs) lies in its operation mode over the network. This may result in tangible delays between the industrial process and the edge-cloud. This paper analyzes how the network delay affects the process performance of an industrial machine on the base of the real-time requirements of the control functions hosted in the edge-cloud. For this purpose, two potential scenarios for the migration of the control system of a real machine to the edge-cloud are implemented. Each scenario features the offloading of more real-time critical control functions to the edge-cloud, which is then tested under increasing load on the network. The study derives classes of automation functions according to the real-time requirements of a potential offloading to the edge-cloud. Furthermore, it shows how complementary designs of an ECAS can affect the performance of the automated systems. In this sense, the results reveal that firm real-time functions can be offloaded to the edge-cloud without any significant loss of performance even if non-real-time networking components are used. On the base of these results, potentials applications domains of ECASs are discussed prospectively and future work is proposed.

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Multi-axis two photon polymerization machine and software concept for the manufacturing of aspheric lenses on non-planar substrates

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Procedia CIRP 2023年 118卷 682-687页

作者： Daniel Kurth Simon Ristok Sopie Rühle Alexander Verl Harald Giessen Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) University of Stuttgart Seidenstr. 36 70174 Stuttgart Germany 4th Physics Institute and Research Center SCoPE University of Stuttgart Pfaffenwaldring 57 70569 Stuttgart Germany

The state of research in two-photon polymerization (TPP) additive manufacturing (AM) focuses on 2.5D systems. Consequently, the advantages known from other multi-axis AM (MAAM) processes, such as printing on non-planar surfaces, are unexplored. To experimentally examine the transferability of these to TPP AM, a printer and related software need to be developed. For the testing of our printing system, we focus on manufacturing aspheric lenses, using spherical glass lenses as blanks and printing the aspheric contour onto these non-planar substrates. The presented concept details how the existing process chain needs to be adapted, thus providing a prototype 5D TPP system for printing on specific non-planar surfaces.

关键词： Additive manufacturing (AM) Two-photon polymerization (TPP) Curved layer slicing Optics Non-planar substrates

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Online Parameterization of a Milling Force Model using an Intelligent System Architecture and Bayesian Optimization

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Procedia CIRP 2022年 107卷 1041-1046页

作者： B. Schmucker F. Trautwein R. Hartl A. Lechler M.F. Zaeh A. Verl Institute for Machine Tools and Industrial Management (iwb Technical University of Munich) Boltzmannstr. 15 85748 Garching Germany Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW University of Stuttgart) Seidenstr 36 70174 Stuttgart Germany

This paper presents an efficient and industry-ready system architecture that enables both the control of machining operations and the high-frequency acquisition of controller data and external sensor signals. Using the recorded data, a dexel-based mechanistic cutting force model, which enables the estimation of cutting forces for complex tool geometries in arbitrary machining operations, is parameterized via an instantaneous cutting force identification method. In the introduced identification method, Bayesian Optimization and Dynamic Time Warping are combined to avoid time-consuming and error-prone synchronization of measurement and simulation. The suitability of this approach was demonstrated by performing cutting experiments at various radial depths of cut and feed rates per tooth. Thereby, a good agreement between simulation and measurement could be observed.

关键词： Industry 4.0 machine tools Data Analytics Process Simulation

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Towards learning digital twin: case study on an anisotropic non-ideal rotor system

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Procedia CIRP 2024年 130卷 689-694页

作者： Zhibo Zhou Michael Walther Alexander Verl Robert Bosch GmbH Robert Bosch Campus 1 Renningen 71272 Germany Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Seidenstraße 36 Stuttgart 70174 Germany

In the manufacturing industry, the digital twin (DT) is becoming a central topic. It has the potential to enhance the efficiency of manufacturing machines and reduce the frequency of errors. In order to fulfill its purpose, a DT must be an exact enough replica of its corresponding physical object. Nevertheless, the physical object endures a lifelong process of degradation. As a result, the digital twin must be modified accordingly in order to satisfy the accuracy requirement. This article introduces the novel concept of ”learning digital twin (LDT),” which concentrates on the temporal behavior of the physical object and highlights the digital twin’s capacity for lifelong learning. The structure of a LDT is first described. Then, in-depth descriptions of various algorithms for implementing each component of a LDT are provided. The proposed LDT is validated on the simulated degradation process of an anisotropic non-ideal rotor system.

关键词： Digital twin Lifelong learning Model calibration

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