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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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Online Parameterization of a Milling Force Model using an Intelligent System Architecture and Bayesian Optimization 55

Online Parameterization of a Milling Force Model using an In...

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55th CIRP Conference on manufacturing Systems, CIRP CMS 2022

作者： Schmucker, B. Trautwein, F. Hartl, R. Lechler, A. Zaeh, M.F. Verl, A. Institute for Machine Tools and Industrial Management Iwb Technical University of Munich Boltzmannstr. 15 Garching85748 Germany Institute for Control Engineering of Machine Tools and Manufacturing Units ISW University of Stuttgart Seidenstr. 36 Stuttgart70174 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. © 2022 The Authors. Published by Elsevier B.V.

关键词： Data Analytics Industry 4.0 machine tools Process Simulation

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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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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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A sensitivity analysis approach to reduce economic risks in the design of edge-cloud automation systems in industry

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Procedia CIRP 2024年 128卷 526-531页

作者： Marco Giani Moritz Walker Michael Neubauer Nelly Frank Patrick Wiener Alexander Verl Robert Bosch GmbH Robert-Bosch-Campus 1 71272 Renningen Germany Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Seidenstr. 36 70174 Stuttgart Germany

The migration of field-level automation systems to an edge-cloud hosted on 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. Nonetheless, due to the novelty of the concept, a transparent life-cycle costs (LCC) assessment for edge-cloud automation systems (ECASs) is complex to achieve. In particular, uncertainties concerning costs related to the engineering and operation of an ECAS, such as commissioning delays or unexpected failures, increase the risks of investments towards its adoption. In this paper we propose a sensitivity analysis approach based on Monte Carlo (MC) life-cycle simulations. Through the approach, design parameters of an edge-cloud are investigated concerning their impact on the magnitude and distribution of LCC. The use of the approach is tested on a real-world case study to optimize the choice of the edge-cloud components and reduce the economic risks for its adoption. The proposed approach is expected to be integrated in early stages of the design process of a production line to identify cost-viable designs of ECASs, thus fostering the transition towards smart manufacturing systems.

关键词： edge-computing industrial automation Design methodology tools technologies

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Automated workflow generation supporting the value stream design of reconfigurable robot assembly cells

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Procedia CIRP 2024年 128卷 609-614页

作者： Marc Ungen David Kampert Benedikt Feldotto Elias Huber Oliver Riedel Robert Bosch GmbH Robert-Bosch-Campus 1 71272 Renningen Germany Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Seidenstr.36 70174 Stuttgart Germany

The paper addresses the automated generation of value stream optimized assembly workflows for reconfigurable robot cells. The focus is on the generation of asynchronous concurrent action workflows from assembly task descriptions. The main motivation is to enable automated search for optimized solutions within the enormous design spaces of these systems which results from their structural and procedural configurability. The proposed approach conducts task assignment, task sequencing and logical task planning (AI Planning) for multiple agents. The approach combines existing methods from the distinct research fields of robot task sequencing and discrete task planning. The contribution of the research is twofold. Firstly, the paper proposes a generic domain meta model enabling automated workflow generation for multi-agent assembly tasks. Secondly, a workflow generation approach based on artificial intelligence planning combined with the game-theoretic concept of extensive form games is presented. The approach iteratively generates a cooperative strategy for acting agents which is subsequently optimized in terms of individual strategies. The obtained logical action plans are further transformed into parametric concurrent action schedules to support downstream design activities. Both the meta model and the workflow generation approach are applied in a case study and provide valuable information on achievable assembly speedup.

关键词： Model-based systems engineering Artificial intelligence Design methodology tools technologies

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Energy Efficiency of ROS Nodes in Different Languages: Publisher-Subscriber Case Studies

Energy Efficiency of ROS Nodes in Different Languages: Publi...

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Workshop on Robotics Software engineering (RoSE)

作者： Michel Albonico Paulo Júnior Varela Adair José Rohling Andreas Wortmann Paraná (UTFPR) Federal University of Technology Francisco Beltrão Brazil Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) University of Stuttgart Germany

ISBN: (数字)9798400705663

ISBN: (纸本)9798350365160

The Robot Operating System (ROS) is a de facto standard for programming robotic systems. It currently provides well-established client libraries for two major languages: $\mathrm{C}++$ and Python. Different programming languages are known for their different abstraction levels, and as a consequence, their resource usage, including energy consumption. With energy efficiency being recurrently a quality requirement, it is important to understand how programming in those two languages may affect the energy consumption of robotic systems. In this study, we analyze the impact on energy consumption when programming ROS nodes in those two main supported languages. We design and conduct an empirical experiment on ROS 2 nodes implemented in C++ and Python for simple and well-documented topic-based examples. We statistically assess to what extent energy consumption is affected by language choice, where nodes programmed in $\mathrm{C}++$ presented a consistently better energy efficiency. A deeper analysis of the measured variables indicates that the energy efficiency difference between the two client libraries is closely related to the underlying *** CONCEPTS• Software and its engineering → Software design tradeoffs; • Computer systems organization → Robotics; • Hardware → Impact on the environment.

关键词： Energy consumption Standards organizations Programming Energy efficiency Libraries Software Software measurement

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Automated Configuration of Optimized Customer Specific Mechatronic Systems Using Behavior Models 17

Automated Configuration of Optimized Customer Specific Mecha...

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17th CIRP Conference on Intelligent Computation in manufacturing engineering, CIRP ICME 2023

作者： Stegmaier, Valentin Eberhardt, Tobias Schaaf, Walter Jazdi, Nasser Weyrich, Michael Verl, Alexander University of Stuttgart Nobelstraße 12 Stuttgart70569 Germany Institute of Industrial Automation and Software Engineering University of Stuttgart Pfaffenwaldring 47 Stuttgart70569 Germany Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Seidenstraße 36 Stuttgart70174 Germany J. Schmalz GmbH Johannes-Schmalz-Straße 1 Glatten72293 Germany

The diversity of component manufacturers' product portfolios is often one of their greatest assets, as it allows them to offer appropriate solutions to a wide variety of customers. However, this diversity is also one of the greatest challenges, as it often makes it nearly impossible to design an optimal mechatronic system by manually selecting components. This is where the Digital Twin of the components can help. By providing behavior models and relevant information, existing optimization algorithms can be used to optimize for system-relevant key performance indicators enabling an automated system design process. The method presented in this paper is validated for a vacuum gripping systems. © 2024 Elsevier B.V.. All rights reserved.

关键词： Smart manufacturing

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Data Model for Adaptive Robotic Construction in Architecture 55

Data Model for Adaptive Robotic Construction in Architecture

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55th CIRP Conference on manufacturing Systems, CIRP CMS 2022

作者： Wolf, Martin Kaiser, Benjamin Hügle, Sebastian Verl, Alexander Middendorf, Peter Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Seidenstr. 36 StuttgartD-70174 Germany Institute of Aircraft Design University of Stuttgart Pfaffenwaldring 31 StuttgartD-70569 Germany

Robotic fabrication plays an essential role for the construction industry in the pursuit of higher productivity and greater flexibility. Modular robotic systems enable the off-site prefabrication of components for high-performance building systems from wood or fiber composites, while digital planning methods and computational design enable the efficient planning of highly specialized and unique lightweight structures. However, existing manufacturing systems do not provide an interface between the two domains that allows to utilize their combined potential. manufacturing instructions for the individual components are currently generated directly from the design model and don't consider the precise setup and configuration of the modular system. The parametric description in computational design thus ends in the planning phase and is converted into a non-parametric description in native robot code, losing a lot of information in the process. For a continuous description of the building components from planning to fabrication, a data model for the parametric description of the fabrication tasks is therefore needed. We present and such a data model for timber and fiber prefabrication applications. The model allows the generic description of the manufacturing process within a task graph, which gives the manufacturing system a way of adapting the tasks to its specific configuration and setup. In addition to the description of jobs, tasks and actions, the model also includes a description of the considered manufacturing environment, as well as a monitoring component for the acquisition of sensor values and system configurations for individual tasks. This not only allows the identification of manufacturing issues like collisions ahead of time, but also enables the implementation of a feed-back loop for iterative optimization of the paths for any given environment. In this work, we give the design considerations for this data model. © 2022 The Authors. Published by Elsevier B

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Quantitative Evaluation of Automated Behavior Model Creation for Applications of Industrial Automation 57

Quantitative Evaluation of Automated Behavior Model Creation...

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18th IFAC Workshop on Time Delay Systems, TDS 2024

作者： Stegmaier, Valentin Eberhardt, Tobias Jazdi, Nasser Weyrich, Michael University of Stuttgart Nobelstraße 12 Stuttgart70569 Germany Institute of Industrial Automation and Software Engineering University of Stuttgart Pfaffenwaldring 47 Stuttgart70569 Germany Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Seidenstraße 36 Stuttgart70174 Germany J. Schmalz GmbH Johannes-Schmalz-Straße 1 Glatten72293 Germany

Creating behavior models for Digital Twins manually requires significant effort, which hinders their widespread use. One possible solution involves using automated approaches to create behavior models. There are several approaches available in literature. Different articles assume that automated creation of behavior models is superior to manual creation, but to provide comprehensive proof, an expert benchmark is necessary. This article conducts such a benchmark with 10 experts setting up 9 different behavior models. The recorded times are compared with an automated method for creating behavior models. The manual creation takes up to 54 times more time compared to the automated method. © 2024 The Authors.

关键词： Office automation

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