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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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Software-defined Manufacturing: Data Representation

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Procedia CIRP 2023年 118卷 360-365页

作者： Carsten Ellwein Rebekka Neumann Alexander Verl Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Seidenstraße 36 70174 Stuttgart Germany

Due to constantly decreasing product cycles, a fast adaptability of manufacturing plants is a prerequisite for profitable operation. The paradigm of Software-defined Manufacturing (SDM) pursues this overriding goal by decoupling software from mechanical and electrical components. In this way, SDM allows the free definition of functionality, purely through software, limited only by the physical limitations of the machines and systems. This paper analyzes existing approaches that pursue the goal of digitally representing manufacturing to enable flexible adaptation. Based on this analysis, a model for the data representation is proposed that allows consistent use across the entire supply chain.

关键词： Software defined manufacturing Data representation Manufacturing ontology

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Recurrent Neural Network for Modelling a Contractive Soft Actuator 56

Recurrent Neural Network for Modelling a Contractive Soft Ac...

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56th International Symposium on Robotics, ISR Europe 2023

作者： Kienzlen, Annika Zürn, Manuel Kazemi, Shahab Xu, Weiliang Cheng, Leo K. Strommel, Martin Verl, Alexander Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Germany Department of Mechanical & Mechatronics Engineering University of Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Department of Electrical & Electronic Engineering University of Auckland New Zealand

ISBN: (纸本)9783800761418

Robotic stomach simulators are designed and employed to model stomach motility and gain insights into the digestive process. These simulators consist of multiple nonlinear soft actuators. Their modelling enhances the controllability and optimisation possibilities and shows their geometry variations and deformations. By leveraging a dynamic model, it becomes possible to design time-efficient model-based controllers that handle the nonlinearities of the soft robot. However, the modelling of soft actuators is challenging due to their infinite number of degrees of freedom. In this paper, we propose a novel approach that maps the dynamics of a contractive soft actuator to a neural network. The short calculation time of neural networks combined with their ability to model complex relationships make them a promising approach for controlling soft actuators. Since the contractive behaviour of the soft actuator is difficult to capture, training data can be generated using a physical simulation model. The results demonstrate that a simple recurrent neural network can model similar dynamics to a physical simulation model with significantly reduced calculation times. Since a fast and precise computer model is necessary to capture the complex dynamics of the soft actuators accurately for control purposes, the recurrent neural network is promising. © VDE VERLAG GMBH Berlin Offenbach.

关键词： contractive actuation deep learning recurrent neural network soft robotics

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Decentralized Motion control for a Novel Planar Motor Intralogistics System 32

Decentralized Motion Control for a Novel Planar Motor Intral...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Steinle, Lukas Helfesrieder, Nico Lechler, Armin Verl, Alexander Montazeri, Ali Wang, Jinfan University of Stuttgart Institute for Control Engineering of Machine Tools and Manufacturing Units Stuttgart Germany FL Technology GmbH Griesheim Germany Southern University of Science and Technology FL Technology GmbH Shenzhen China

ISBN: (纸本)9798350399714

Modern production environments place high demands on the logistics systems that are used. High flexibility and high throughput are expected, combined with high availability and efficient operation. To meet these requirements, this paper presents a novel intralogistics system based on the functionality of a planar motor. Following the operating principle of a moving stator, the vehicles are entirely passive and the hardware for the generation of motion is integrated into the floor. This is achieved in the form of compact, decentrally interconnected floor units, several of which are simultaneously involved in a motion. In order to control this system with satisfactory reliability and performance, the theoretical foundations for a proper understanding of the system are examined before a system model is derived and parameterized through measurements. On this basis a specifically designed motion control system is developed, that features precise planar movement, decentralized computation and compensation of position dependent characteristics. Thereafter, a method for model-based parameterization is outlined, that takes into account robustness against disturbances and parameter variations. Finally, the developed approach is implemented on the actual hardware and validated in experimental investigations in terms of path accuracy. © 2023 IEEE.

关键词： Floors

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Analysis and comparison of optimization techniques supporting the engineering planning of reconfigurable robot cells 56

Analysis and comparison of optimization techniques supportin...

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56th CIRP International Conference on Manufacturing Systems, CIRP CMS 2023

作者： Ungen, Marc Kampert, David Riedel, Oliver Robert Bosch Gmbh Robert-Bosch-Campus 1 Renningen71272 Germany Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Seidenstr.36 Stuttgart70174 Germany

The present paper studies the automated optimization of reconfigurable robot cells to support engineering planning activities. The specific type of reconfigurable robot cells under investigation allows an arbitrary arrangement of functional machine modules on a base plane accessible to the robotic manipulators. The research is motivated by the fast-growing configuration space of reconfigurable robot cells which results from the configurability of the cell layout and the performed workflows. Searching for optimized configurations within this configuration space is an iterative procedure which currently requires lots of manual efforts and expert assessment skills for each iteration. Performing many optimization iterations requires much time, which typically results in only a few performable iterations and thus in suboptimal configurations. To compensate for this, automated support for the underlying planning problem is required. The specific planning problem under investigation concerns finding motion-time optimized layout configurations for fixed workflows. This planning problem was chosen because of its strong association with the productivity of robot cells and the fact that it is the basis for further considerations on the influences of performed workflows. The main contribution of the research is a guideline concerning the decomposition of the mentioned optimization problem. This decomposition concerns the specific formulation of the optimization problem as well as its solving principle. The approach of the paper is to compare combinations of single- and multi-objective problem formulations as well as single- and multi-step solving procedures. Respective variants of evolutionary and conventional algorithms are tested for solving the optimization problems. The experiments are conducted using a specifically developed simulation and optimization framework which supports layout reconfiguration, robot path planning, and time parametrization based on state-of-the-art m

关键词： Adaptability engineering planning Optimization Reconfigurable manufacturing systems Reconfigurable robot cells

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Anisotropic Damping and Stiffness of laminated steel parts using adhesive bonding – an empirical influence study

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Procedia CIRP 2023年 120卷 428-433页

作者： Nico Helfesrieder Michael Neubauer Armin Lechler Alexander Verl University of Stuttgart Institute for Control Engineering of Machine Tools and Manufacturing Units Seidenstraße 36 70174 Stuttgart Germany

Layered part manufacturing enables part topology to be optimized internally by structuring individual layers separately, which is especially promising for large machine parts. However, the resulting part properties as a function of the selected manufacturing parameters are largely unexplored. Therefore, this paper investigates the impacts of the slicing direction, the sheet thickness as well as the adhesive on anisotropic damping and stiffness values of adhesively bonded steel parts. A cantilever beam of symmetric cross-section is chosen as an easy-to-manufacture geometry. Three redundant specimens were prepared for each of six parameter variations investigated within this study, resulting in a total of 18 investigated beams. The results indicate highly anisotropic stiffness and damping as a function of the layer build-up direction and it was found that the modal damping rises with increasing amount of adhesive. These findings must be taken into account for future applications of layered lightweight machine parts and their simulative predictability should be studied.

关键词： Layer Laminated Manufacturing (LLM) Laminated Object Manufacturing (LOM) Adhesive Bonding Damping Experimental Modal Analysis (EMA)

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An engineering methodology to assess the economic viability of edge-cloud automation systems in manufacturing 56

An engineering methodology to assess the economic viability ...

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56th CIRP International Conference on Manufacturing Systems, CIRP CMS 2023

作者： Giani, Marco Frank, Nelly Wiener, Patrick Verl, Alexander Robert Bosch GmbH Robert-Bosch-Campus 1 Renningen71272 Germany Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Seidenstr. 36 Stuttgart70174 Germany

The migration of industrial automation systems (IASs) 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. However, the unclear assessment of costs of an edge-cloud automation system (ECAS) is a significant concern for manufacturing experts, which disincentives its adoption. An ECAS is expected to generate different costs than a traditional IAS, which depends on the requirements of the automation functions hosted in the edge-cloud. Because of this, methods to evaluate various realization concepts of an ECAS concerning their economic viability are required to support its engineering. In this paper, we provide a methodology that helps practitioners to define design specifications for an ECAS and assess their life-cycle costs (LCCs). To provide a robust and transparent LCCs assessment, we leverage the Monte Carlo method, which is a well-affirmed technique to address costs uncertainties. To do so, a cost model has been developed and implemented in a discrete-event simulator to calculate upfront and running costs of an ECAS. The application of the methodology is shown through an exemplar case study and its results are discussed. The presented work helps practitioners to evaluate whether an ECAS is economically viable for a manufacturing scenario, thus increasing the acceptance for this technology and fostering the transition towards flexible and smart manufacturing systems. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 56th CIRP International Conference on Manufacturing Systems 2023.

关键词： edge-cloud automation Edge-cloud computing engineering methods life-cycle costs

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Deep Learning-Based Instance Segmentation for Feature Extraction of Branched Deformable Linear Objects for Robotic Manipulation 19

Deep Learning-Based Instance Segmentation for Feature Extrac...

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19th IEEE International Conference on Automation Science and engineering, CASE 2023

作者： Zürn, Manuel Kienzlen, Annika Klingel, Lars Lechler, Armin Verl, Alexander Ren, Shiyi Xu, Weiliang Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Seidenstr. 36 Stuttgart70174 Germany The University of Auckland Department of Mechanical and Mechatronics Engineering 20 Symonds St. Auckland1001 New Zealand

ISBN: (纸本)9798350320695

Branched deformable linear objects, like wire harnesses, are vastly used in the electrical industry to interconnect multiple electronic components while allowing small relative motions. Their deformable nature gives them an infinite-dimensional configuration space, which is challenging for robotic manipulation and requires sensor feedback about the actual configuration. Cameras have been used before in automation attempts as sensor feedback of branched deformable linear objects. However, image processing is sensible to occlusions and unstructured environments. This paper presents a method of using deep learning-based instance segmentation networks to extract topological features like connectors, clips, branch points and overlapping points on the branched deformable linear object. The topological features originate from features that can be used for matching a stretched representation of the branched deformable linear object to its sensor representation. Our results show that the network is capable of extracting the features for different datasets with high accuracy. Furthermore, this paper evaluates the use of preliminary results in the training process to facilitate annotation by predicting the labels. For now, this paper only uses 2D images of the branched deformable linear objects. Future work will also use depth images, such that individual deformable linear object features, such as length or radius, can be extracted from the images. We anticipate the usage of wire harness automation with robotic manipulation, augmented reality support of the worker or quality control of wire harnesses. © 2023 IEEE.

关键词： Feature extraction

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Systematic Component-Oriented Language Reuse

Systematic Component-Oriented Language Reuse

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

作者： Jérôme Pfeiffer Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Stuttgart Germany

Modern-day software is increasingly developed by domain experts. Domain-specific languages (DSLs) help them to bridge the conceptual gap between the problem and the solution domain. engineering these languages is complex, as it requires comprehensive white-box knowledge of constituents of DSLs, i.e., syntax and semantics, and their composition and realization in respective technological spaces of language workbenches. For efficient language engineering reusing existing languages by composition is essential. For this purpose, various language composition and variability mechanisms have been developed. However, in the literature, there exists no approach enabling systematic language reuse holistically, i.e., including syntax and semantics, in a black-box fashion. We propose an approach for the systematic component-oriented reuse of DSLs (SCOLaR) that supports the integrated reuse of syntax, well-formedness rules, and semantics via black-box language components that are composable along their interface in a language family. We implemented a prototypical version of our solution with the MontiCore language workbench, that supports embedding and aggregation of grammar-based languages with translational semantics realized with code-generators. In the future, we plan to improve our implementation with the support of other language workbenches and to provide a comprehensive evaluation with a series of case studies. With our work, we aim to advance the field of software language engineering by providing a novel method for black-box language reuse capturing syntax and semantics definitions for textual external, translational DSLs. This paper outlines our approach by stating the problem, motivating our research question, and proposing our solution, our plan for evaluation, and presenting expected contributions.

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Feature-Space Reinforcement Learning for Robotic Manipulation

Feature-Space Reinforcement Learning for Robotic Manipulatio...

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First International Conference on Transdisciplinary AI (TransAI)

作者： Ralf Gulde Khanh Nguyen Marc Tuscher Oliver Riedel Alexander Verl Institute of Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Stuttgart Germany

Reinforcement Learning (RL) has gained popularity for developing intelligent robots, but challenges such as sample inefficiency and lack of generalization persist. The choice of observation space significantly influences RL algorithms' sample efficiency in robotics. While end-to-end learning has been emphasized, it increases complexity and inefficiency as the agent must re-learn forward and inverse kinematics. To address these issues, we propose a straightforward approach that utilizes readily available control techniques, such as forward and inverse kinematics, to capitalize on domain knowledge. Our approach involves enhancing the observation space with task-space features and utilizing task-space inverse kinematics. Our contributions include a proposal for mathematical formulation and a framework for RL algorithms in robotics.

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