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检索条件"机构=Institute for Control Engineering of Machine Tools"

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Prediction of the configuration of objects in a bin based on synthetic sensor data 13

Prediction of the configuration of objects in a bin based on...

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13th CIRP Conference on Intelligent Computation in Manufacturing engineering, CIRP ICME 2019

作者： Fur, Shan Boughattas, Bilel Verl, Alexander Pott, Andreas Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Seidentraße 36 StuttgartD-70174 Germany

Bin picking describes a robot system that picks unsorted objects from a bin based on sensor data. The object recognition is one of the main difficulties when calculating the grasping pose. In the worst case, a faulty object recognition leads to a system standstill. This work introduces a framework concept for improving the object recognition by predicting the configuration of all the objects in the bin based on an initial scan in form of a point cloud. This is done by taking advantage of an online simulation, with the goal of reducing the total number of scan cycles, which leads to a reduced risk of failure and improves the overall performance of a bin picking system. © 2020 The Authors.

关键词： Object recognition

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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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Shop-floor service connector - A message-oriented middleware focused on the usability and infrastructure requirements of SMEs developing smart services 3

Shop-floor service connector - A message-oriented middleware...

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3rd IEEE International Conference on Knowledge Innovation and Invention, ICKII 2020

作者： Strljic, Matthias Milan Vollmann, Chris Riedel, Oliver Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Stuttgart70174 Germany Fraunhofer Institute for Industrial Engineering IAO Stuttgart70569 Germany

ISBN: (纸本)9781728193335

Digitalization causes an intensive change in the way software components are developed within the shop floor. The blending of technology domains with current automation technologies leads to high integration efforts with a low degree of reusability. This problem is further reinforced by a diverging system understanding within individual domains and the use of heterogeneous development platforms. Existing solutions from the field of cloud computing are applied in the context of the Industrial Internet of Things (IIoT). However, these systems do not fulfill the requirements of user-friendliness, resource demands, performance, and infrastructure, which is why they are only partially suitable for the current needs of small and medium-sized enterprises (SMEs). For this purpose, the communication middleware Shop-Floor-Service-Connector (SFSC) is developed. Based on the broker-less messaging system ZeroMQ, an overlaid SFSC protocol is implemented. Subsequently, an example scenario is used to illustrate the usage of the system. Finally, the next steps for further developing the middleware are presented. © 2020 IEEE.

关键词： Middleware

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Adaptive spindle speed variation to improve process stability 22

Adaptive spindle speed variation to improve process stabilit...

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22nd International Conference on Production Research, ICPR 2013

作者： Friedrich, J. Verl, A. Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Seidenstr. 36 StuttgartD-70174 Germany

A limiting factor of the performance of machine tools is the dynamic process behavior. Suppression of chatter is still an important measure to increase productivity. Spindle speed variation is a known possibility to reduce chatter vibrations without changes to the machine tool using the degree-of-freedom of the spindle speed. In this paper the new adaptive spindle speed variation (ASSV) method is presented. The ASSV algorithm automatically adapts the amplitude and the frequency of the spindle speed variation due to the current process stability conditions taking into account the machine capability and the process technology. A simulation model of the milling process is used to analyze and verify the functionality of the ASSV. With just using a simple P-or PI-controller in combination with the ASSV algorithm the depth of cut and thus the productivity can be increased to 150% in comparison to cutting with constant spindle speed with respect to the displacement of the tool tip.

关键词： machine tools

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Modeling Capabilities of Digital Twin Platforms - Old Wine in New Bottles?

Modeling Capabilities of Digital Twin Platforms - Old Wine i...

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Modellierung 2024

作者： Pfeiffer, Jérôme Lehner, Daniel Wortmann, Andreas Wimmer, Manuel Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Germany CDL-MINT Institute of Business Informatics - Software Engineering JKU Linz Austria

ISBN: (纸本)9783885797425

This extended abstract summarizes our paper [Pf22] on studying emerging modeling languages provided by digital twin platforms and contrasting them to established object-oriented modeling languages in the field of software engineering. This work has been originally published at the 18th European Conference on Modelling Foundations and Applications (proceedings are published in the Journal of Object Technology (JOT)) in 2022. © 2024 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Software engineering

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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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Self-adaptive digital twin reference architecture to improve process quality 33

Self-adaptive digital twin reference architecture to improve...

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33rd CIRP Design Conference

作者： Splettstößer, Ann-Kathrin Ellwein, Carsten Wortmann, Andreas Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Seidenstr. 36 StuttgartD-70174 Germany

The aim of software-defined manufacturing is a more adaptable production through software. This requires highly adaptive, versatile cyber-physical production systems (CPPSs), which react flexibly to changes and perform crucial adjustments. Self-adaptive digital twins (DTs) enable adaptability through monitoring the behavior of a CPPS, identifying problems, and determining adjustments for the optimization of processes. DTs work with an idealized representation of the CPPS. Changes in system behavior that negatively impact the process execution, e.g., caused by tool wear or variations of environmental influences, and, thus, the quality of the resulting product, are ignored. This ignorance leads to defective products, sub-optimal product quality, and hazardous CPPS states. To mitigate this, we present an approach to incorporate quality awareness into a model-driven reference architecture of a self-adaptive DT. The devised concept automatically analyzes and optimizes system behavior. To this end, a component for quality evaluation extends the reference architecture. The DT and CPPS are connected via OPC UA. To validate the approach, a fiber laser machine is utilized. The DT can operate both on a physical and a virtual laser machine. © 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)

关键词： Fiber lasers

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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...

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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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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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