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Introduction of an industrial transfer learning use case systematization for machine tools 56

Introduction of an industrial transfer learning use case sys...

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

作者： Netzer, Markus Michelberger, Jonas Puchta, Alexander Verl, Alexander Fleischer, Jürgen wbk Institute of Production Science Karlsruhe Institute of Technology Kaiserstraße 12 Karlsruhe76131 Germany ISW Institute for Control Engineering of Machine Tools and Manufacturing Units Keplerstraße 7 Stuttgart70174 Germany

Traditional intelligent systems such as fault detection are mainly developed for isolated use on single machines and are mostly trained individually. A cross-machine knowledge transfer of intelligent systems holds an immense potential and can reduce implementation effort of intelligent systems. To enable a structured analysis of real-world transfer problems, an industry-relevant, far-reaching systematization of typical machine tool use cases is developed. This provides the overview over different typical use case classes from the industrial use case perspective. Based on the derived transfer criteria of each use case class, a conceptual approach of concrete transfer methods is proposed. © 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： industrial transfer learning knowledge transfer machine tools Transfer learning transfer learning systemization

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Efficient data acquisition for traceability and analytics 31

Efficient data acquisition for traceability and analytics

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31st CIRP Conference on Life Cycle engineering, LCE 2024

作者： Reinhardt, Heiner Mahdaviasl, Mahtab Prell, Bastian Mauersberger, Anton Klimant, Philipp Reiff-Stephan, Jörg Ihlenfeldt, Steffen Fraunhofer Institute for Machine Tools and Forming Technology IWU Reichenhainer Straße 88 Chemnitz09126 Germany Technical University of Applied Sciences Wildau Hochschulring 1 Wildau15745 Germany Virtual Technologies Hochschule Mittweida University of Applied Sciences Technikumplatz 17 Mittweida09648 Germany Institute of Machine Tools and Control Engineering Technische Universität Dresden Dresden01062 Germany

Implementing processes for traceability is required in various industries to assure product quality during manufacturing, provide evidence on required processing conditions or facilitate product recalls. Commonly, radio-frequency identification (RFID) or code recognition techniques (e.g. Data Matrix) are applied to track the flow of workpieces through a manufacturing system and link processing data accordingly. Although the analysis of tracking data is well-examined, we still see a gap in the research on the trade-off between data acquisition, data analytics and data quality. Here, we present a framework to increase the value of existing data by enabling data analytics while addressing common pitfalls and reducing the costs of data management. © 2024 Elsevier B.V.. All rights reserved.

关键词： Decision making

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Modulation-Frequency-Dependent Power Dissipation Model for Servo Drives With Adaptive PWM

Modulation-Frequency-Dependent Power Dissipation Model for S...

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International Workshop on Advanced Motion control (AMC)

作者： Manuel Weiss Florian Frick Armin Lechler Alexander Verl Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) University of Stuttgart Stuttgart Germany

The switching frequency of the power electronics in servo drives significantly impacts its power dissipation and process accuracy. To date, a constant high switching frequency is typically used, based on the strictest requirements of the manufacturing process. The novel adaptive PWM approach dynamically adjusts the switching frequency at runtime to reduce switching losses of the power electronics, while maintaining the required process accuracy. The switching frequency is set based on the requirements of the active operating state of the tooling machine. To determine the optimal switching frequency, a holistic understanding of power dissipation in servo drives is required. A novel modulation-frequency-dependent power dissipation model, incorporating the losses in the drive amplifier and in the motor due to current ripple effects, is presented in this paper. The new model is validated over a wide range of parameters, including different motors, voltages and currents. The evaluation of the model results in a better understanding of the modulation-frequency-dependent power dissipation in servo drives and enables the determination of the optimal PWM frequencies. It is shown that higher energy savings can be achieved at higher voltages, while current ripple effects limit the effectiveness of the adaptive PWM approach for motors with lower power ratings.

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Model-based automatic generation of digital twin models for the simulation of reconfigurable manufacturing systems for timber construction 55

Model-based automatic generation of digital twin models for ...

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55th CIRP Conference on Manufacturing Systems, CIRP CMS 2022

作者： Kaiser, Benjamin Reichle, Alexander Verl, Alexander Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Seidenstr. 36 StuttgartD-70174 Germany

Reconfigurable manufacturing systems adapt to changing requirements and therefore offer a solution to the challenges associated with increasing product variety and shorter product life cycles. This is especially helpful in the prefabrication in timber construction, as components and requirements vary widely between projects. However, reconfigurations must be quick to keep downtimes short. The planning of reconfigurations is a complex problem where digital twins and simulation models of the manufacturing systems are necessary for simulation and validation. In our timber prefabrication use case, building components and requirements change from project to project, and the manufacturing system needs to reconfigure frequently. The building components and the configuration of the manufacturing system are designed simultaneously during a co-design process to ensure the integrity of the manufacturing process in a changing manufacturing environment. Hence, this may require many iterations of the planning process. Each iteration needs validation through a simulation. However, the manual generation of simulation models is time-consuming and error-prone and thus poses an obstacle for the digital planning phase. This paper addresses this issue and presents a model-based approach to generate simulation models for reconfigurable manufacturing systems. Our system and its components are modeled in automation markup language as this domain-specific language allows us to model not only kinematics and geometry but also behavior, skills, and interfaces to check the compatibility of components. We apply the method to our use case with the IntCDC wood prefabrication system. The proposed method builds a component graph for a configuration of the manufacturing system and generates the required packages for the simulation in Gazebo and ROS. To build simulation models automatically, we apply model-to-model and model-to-text transformation techniques. The proposed method is suitable for integr

关键词： AutomationML IntCDC Reconfigurable Manufacturing System ROS Simulation Software Defined Manufacturing Timber Prefabrication URDF

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On data, information, and knowledge in the context of online simulation in industrial automation 57

On data, information, and knowledge in the context of online...

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18th IFAC Workshop on Time Delay Systems, TDS 2024

作者： Deubert, Darius Selig, Andreas Verl, Alexander Bosch Rexroth AG Bgm.-Dr.-Nebel-Straße 2 Lohr am Main97816 Germany Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Stuttgart70049 Germany

In the course of digitalization and Industry 4.0, data-based optimization strategies and AI technologies are increasingly applied in industrial automation. To achieve benefits from this, it is necessary to contextualize and interpret the widely available data and process them to value adding information and knowledge. This paper deals with the processing of data, information, and knowledge in the context of online simulation in industrial automation. It forms a basis by defining the terms within an industrial perspective and examining the data landscape regarding properties that get important in cyber-physical systems including online simulation. Subsequently the generation of resulting information and knowledge with online simulation is discussed. Within this work, by changing the point of view from a technical perspective to an abstraction it is shown that known principles from intelligent systems and knowledge engineering also hold for manufacturing systems that are supported by an online simulation system. Additionally, this contemplation points out that online simulation systems exceed the level of information by gaining and processing knowledge, which enables to infer new information. By this, the influence of online simulation in industrial automation on data, information, and knowledge handling is expounded comprehensively. © 2024 The Authors.

关键词： Smart manufacturing

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Adaptive CAM planning to support co-design in the building industry 32

Adaptive CAM planning to support co-design in the building i...

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32nd CIRP Design Conference, CIRP Design 2022

作者： Reichle, Alexander Ellwein, Carsten Verl, Alexander Institute For Control Engineering Of Machine Tools And Manufacturing Units University Of Stuttgart Seidenstr. 36 StuttgartD-70174 Germany

Co-design describes a collaborative design process between different stakeholders. These stakeholders usually come from various domains. To enable co-design, data has to be exchanged between the stakeholders. Current approaches to data management in the building industry are usually oriented along the process chain and therefore only allow a unidirectional flow of information. This is an obstacle in the implementation of co-design within the building industry. As more and more buildings are built by utilizing prefabricated modules to accelerate the on-site building process and to reduce the cost, a major effort in terms of implementing co-design is to link the design information of the building industry with the production information of the manufacturing industry. The problem occurs as the building industry mainly uses the fndustry Foundation Classes and the manufacturing industry uses the Standard for the Exchange of Product Model Data as a data format to exchange information. This leads to a break in the flow of information. Furthermore, information about the manufacturability of products, that is generated during the computer-aided manufacturing process, cannot be easily returned to the architect who can utilize the information to adapt the design in the computer-aided design process. This paper addresses these problems by defining a data processing pipeline that enables the conversion of the geometric design information stored in IFC into the STEP data format. From there, the CAM strategy can be defined and results in an executable G-Code. The information of the CAM planning is returned to the architect, who can adapt the design by interpreting the results of the CAM processing. To close the co-design loop, the G-Code for the design adaptions can be generated automatically by reusing the predefined CAM strategy. © 2022 The Authors.

关键词： Computer aided manufacturing

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Test-Driven Reward Function for Reinforcement Learning: A Contribution towards Applicable machine Learning Algorithms for Production Systems 15

Test-Driven Reward Function for Reinforcement Learning: A Co...

引用

15th CIRP Conference on Intelligent Computation in Manufacturing engineering, ICME 2021

作者： Jaensch, Florian Kübler, Karl Schwarz, Elmar Verl, Alexander Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Seidentraße 36 StuttgartD-70174 Germany

Reinforcement Learning algorithms find more and more application in fields where complex tasks need to be solved. The automation of production systems is one of those fields. Normally, programming a control system defines the automation strategy. Previous contributions by the authors have shown that a so-called agent can learn automation strategies for production systems using a Reinforcement Learning setup. However, the development of the reward function for the agent can be challenging and needs Reinforcement Learning domain knowledge. This paper introduces a novel approach in combining Test-Driven Development with Reinforcement Learning in order to solve the problem of a suitable reward function. In the presented approach predefined test cases are used to derive rewards for the agent. The use of an automated test framework allows for continuous learning sequences until all test cases are passed. An application example of a robot cell is used to demonstrate the novel approach and verify its suitability and usability. The first application shows promising results for further examination towards more application scenarios. © 2022 The Author(s).

关键词： Reinforcement learning

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Ambiguity Tolerant Commissioning Ontology: From an upper ontology to a domain specific implementation 15

Ambiguity Tolerant Commissioning Ontology: From an upper ont...

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15th CIRP Conference on Intelligent Computation in Manufacturing engineering, ICME 2021

作者： Ellwein, Carsten Hekeler, Marc Riedel, Oliver Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Seidenstr. 36 StuttgartD-70174 Germany

There are no standardized interfaces or descriptions for the outsourcing of production orders, e.g. through cloud manufacturing. For this reason, an upper ontology was developed in a preliminary work;associated models describe the entire process and parameters from the request for quotation over its return to the conclusion of the contract. Products can either be described by their final state or by the initial product in conjunction with the applied processes. In this paper, the process from this upper ontology to a concrete implementation is further elaborated and a middleware is introduced which offers a machine-readable interface to edit the ontology as well as various validation options are addressed. © 2022 Elsevier B.V.. All rights reserved.

关键词： Ontology

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Towards an Implementation of Simulation Based Digital Twins in Cyber-Physical Production Systems Environments

Towards an Implementation of Simulation Based Digital Twins ...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Shan Fur Samed Ajdinović Armin Lechler Alexander Verl Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Germany

The increasing digitization of the manufacturing industry offers a wide range of opportunities for the effective use of production data, which can lead to significant time and cost savings. One promising approach is the use of synchronous simulations during the actual operation of cyber-physical production systems (CPPS) to provide valuable insights into possible performance improvements and at the same time reduce the risk of costly downtime. This paper presents a novel concept that enables structured storage of CPPS production data as part of a digital twin that serves as a virtual replica of the physical production environment. In particular, this approach is based on the Asset Administration Shell (AAS), which provides a comprehensive framework for the management of production data and allows for a seamless integration with simulation models. Storing production data in a structured and standardized format can make data queries efficient, facilitate data processing, and provide a solid foundation for simulation model development, calibration, and validation. These simulation models can then be used to identify potential process improvements and test different scenarios, minimizing the risk of disruption to actual production.

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Identification of a Lumped-Parameter-Model for a Dual-Motor Robotic Drive Train with Backlash and Electrical Preload

Identification of a Lumped-Parameter-Model for a Dual-Motor ...

引用

International Symposium on Power Electronics, Electrical Drives, Automation and Motion, SPEEDAM

作者： Benedikt Kaiser Adrian Schäfer Andreas Schütz Nejila Parspour Armin Lechler Alexander Verl Institute of Electrical Energy Conversion Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Stuttgart Germany

ISBN: (数字)9798350387599

ISBN: (纸本)9798350387605

The mechanical interface of rotating robot joints require torque to speed ratios that are not suitable for an elec-tromagnetic direct drive. Typically, gearboxes are used to convert torque and speed, although these introduce non-linear backlash into the system. By using a dual-motor drive train, it is possible to electrically preload the gearbox and compensate for the backlash. In order to describe the system dynamics, a lumped-parameter-model is presented for which the parameters are determined to represent an experimental setup. Measurements are carried out in various coupling scenarios and the associated parameters of the subsystems of the models are identified. The measurement and the model are further adapted through optimization. Based on the evaluated transfer functions, the model is able to describe the basic behavior of the system. The measurements with coupled motors and activated preload indicate persistent differences from the actual system. However, the presented model may be applied in simulation and control to improve the dynamics of a dual-motor robot drive train with backlash-prone gearing and the possibility of electrical preload.

关键词： Couplings Adaptation models Transient response Torque System dynamics Transfer functions Electric variables measurement

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