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Notch impact toughness of laser beam welded thick sheets of cryogenic nickel alloyed steel X8Ni9

Procedia CIRP 2020年 94卷 627-631页

作者： S. Gook S. Krieger A. Gumenyuk A.M. El-Batahgy M. Rethmeier Fraunhofer Institute for Production Systems and Design Technology IPK Pascalstraße 8-9 10587 Berlin Germany Federal Institute for Materials Research and Testing BAM Unter den Eichen 87 12205 Berlin Germany Central Metallurgical R & D Institute CMRDI P. O. Box: 87 Helwan El-Tabbin Cairo Egypt Institut für Werkzeugmaschinen und Fabrikbetrieb IWF Technische Universität Berlin Straße des 17. Juni 135 10623 Berlin Germany

The paper deals with the investigations of the impact toughness of laser beam welded 14.5 mm thick sheets made of cryogenic steel X8Ni9 as a function of preheating. This 9% nickel alloyed steel is widely used in the liquefied natural gas (LNG) industry. An application of highly efficient welding processes such as high-power laser beam welding (LBW) in LNG sector requires an understanding of the interactions between the LBW process parameters and weld properties, in particular the impact toughness. The results show that the original fine-grained martensitic microstructure of the base metal (BM) is significantly changed by melting and crystallization during the LBW, what can lead to a decrease in the impact toughness of the weld metal (WM) below the required level. An optimal preheating temperature range leads to the favorable thermal welding cycle and is of remarkable importance for maintaining the notch impact toughness of laser beam welded joints of these thick steel sheets. A parameter window was identified in which V-notch impact toughness comparable to that of the BM at -196 °C was achieved.

关键词： cryogenic steel laser beam welding preheating welding thermal cycle microstructure hardness V-notch impact toughness

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design alternatives for internationally distributed learning factories in global production engineering

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Procedia Manufacturing 2020年 45卷 392-397页

作者： Bastian C. Schumacher Anja Steinbach Nguyen H. Vi Ahmet Yükseltürk Holger Kohl Jörg Krüger Huy Nguyen Quoc Technische Universität Berlin Chair of Sustainable Corporate Development Straße des 17. Juni 135 10623 Berlin Germany Technische Universität Berlin Chair of Industrial Automation Technology Straße des 17. Juni 135 10623 Berlin Germany Vietnamese-German University Le lai Binh Duong New city Vietnam Turkish-German University Sahinkaya Cd. No: 108 34820 Istanbul Turkey Fraunhofer Institute for Production Systems and Design Technology IPK Pascalstraße 8-9 10587 Berlin Germany

An internationally distributed learning factory (IDLF) provides opportunities to make students aware of intercultural challenges in global production networks. An IDLF is a scalable network of learning factories with value creation processes in spatially distributed locations. It enables collaboration and competition between student groups in distant locations and considers manual and automatic process methods. Due to the high number of design alternatives for IDLFs, a systematic approach for describing attractive characteristics is required. This paper shows design alternatives for IDLFs structural and process organization on the example of learning factories in Germany, Turkey and Vietnam.

关键词： learning factory design alternatives intercultural international

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Improvement of Defect Detectability in Machine Tools Using Sensor-based Condition Monitoring Applications

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Procedia CIRP 2018年 67卷 325-331页

作者： Giulio DEmilia Antonella Gaspari Eckhard Hohwieler Abdelhakim Laghmouchi Eckart Uhlmann University of LAquila via G. Gronchi 18 LAquila 67100 Italy Fraunhofer IPK-Institute for Production Systems and Design Technology 8-9 PascalStrae Berlin Germany 10587

The aim of this paper is to present a reliable methodology for condition monitoring of components of high performance centerless grinding machines. This enables the detection and localization of the defects on the ball screw. The fault detection is realized using a self-implemented classification algorithm and other pattern recognition algorithms. The diagnosis is based on acceleration and AE measurement data performed on an axis test rig using various damaged ball screws at different operating parameters. Moreover, the structure of the pattern recognition process will be introduced, this includes the signal pre-processing, the selection of the most suitable features for this specific application using MATLAB®. Finally, the evaluation of the developed solution will be showed. The actions allowing improving the accuracy of measurement data and the effectiveness of the processing algorithms, based on MATLAB® applications, are described throughout the paper. Satisfactory classification results will be obtained and discussed.

关键词： Condition monitoring Measurement uncertainty Acoustic emission Vibration Grinding

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Intuitive robot programming through environment perception, augmented reality simulation and automated program verification

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Procedia CIRP 2018年 76卷 161-166页

作者： Jonas Wassermann Axel Vick Jörg Krüger Technische Universität Berlin Pascalstrasse 8-9 10587 Berlin Germany Fraunhofer Institute for Production Systems and Design Technology IPK Pascalstrae 8-9 10587 Berlin Germany

The increasing complexity of products and machines as well as short production cycles with small lot sizes present great challenges to production industry. Both, the programming of industrial robots in online mode using hand-held control devices or in offline mode using text-based programming requires specific knowledge of robotics and manufacturer-dependent robot control systems. In particular for small and medium-sized enterprises the machine control software needs to be easy, intuitive and usable without time-consuming learning steps, even for employees with no in-depth knowledge of information technology. To simplify the programming of application programs for industrial robots, we extended a cloud-based, task-oriented robot control system with environment perception and plausibility check functions. For the environment perception a depth camera and pointcloud processing hardware were installed. We detect objects located in the robot’s workspace by pointcloud processing with ROS and the PCL and add them to the augmented reality user interface of the robot control. The combination of process knowledge from task-oriented application programming and information about available workpieces from automated image processing enables a plausibility check and verification of the robot program before execution. After a robot program has been approved by the plausibility check, it is tested in an augmented reality simulation for collisions with the detected objects before deployment to the physical robot hardware. Experiments were carried out to evaluate the effectiveness of the developed extensions and confirmed their functionality.

关键词： Industrial Robotics Robot Programming Augmented Reality

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Safe human robot collaboration — Operation area segmentation for dynamic adjustable distance monitoring

Safe human robot collaboration — Operation area segmentatio...

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International Conference on Control, Automation and Robotics (ICCAR)

作者： Martin J. Rosenstrauch Jörg Krüger Institute for Machine Tools and Factory Management Institute for Production Systems and Design Technology Fraunhofer IPK Berlin Germany Technische Universitat Berlin Berlin Berlin DE

A core challenge of human robot collaboration is to ensure safety. By renouncing guards potential hazards raise. In this paper an approach is presented where the actual used operating area of an industrial robot gets segmented out of his whole workspace. Therefore an industrial robot gets monitored with a time of flight sensor. Subsequently the recorded 3D data gets processed and the operating area segmented by applying an octree algorithm. The information thus determined about the hazard zone can be used for an application specific adjustment of safety periphery systems. This is exemplary realized by ultrasound distance sensor modules which get individually parametrized depending on the pre-segmented operating area.

关键词： Robot sensing systems Service robots Robot kinematics Monitoring Safety Three-dimensional displays

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Learning Factory for Industry 4.0 to provide future skills beyond technical training

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Procedia Manufacturing 2018年 23卷 27-32页

作者： Burkhard Schallock Christoffer Rybski Roland Jochem Holger Kohl Fraunhofer Institute for Production Systems and Design Technology (IPK) Pascalstr.8-9 Berlin 10587 Germany Technische Universität Berlin Pascalstr.8-9 Berlin 10587 Germany

The paper will describe the design of a learning factory for Industry 4.0 that addresses the growing demand for future skills of production staff. Existing learning factories often focus on the technical skills whereas this learning factory also trains decision making, group work and performance monitoring skills. The paper refers to the existing categorizes of learning factories and unveils its numerous features. The conceptual design includes theoretical and practical parts, which prove to be successful in a German learning factory that was realized by the authors. Especially, for the industry 4.0 environment, the layout consists of three stages of a production system, from manual to automatized manufacturing. The practical tasks cover the introduction of smart devices, connection of information flows as well as monitoring of performance. The didactical design of the training program provides a sustainable approach by not only realizing training courses but also includes preparation with management, mid-term coaching and success monitoring after the training. The learning factory is a part of a whole research institute for intelligent manufacturing in China including consultancy and application support. One of the underlying goals of the learning factory is to enable production staff for change management, decision making and innovation.

关键词： Learning Factory Industrie 4.0 Intelligent manufacturing Future skill demand Performance management

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Smart Life Cycle Monitoring for Sustainable Maintenance and Production – An example for Selective Laser Melting Machine

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Procedia Manufacturing 2017年 11卷 711-717页

作者： Uhlmann, Eckart Pontes, Rodrigo Pastl Laghmouchi, Abdelhakim Geisert, Claudio Hohwieler, Eckhard Fraunhofer Institute for Production Systems and Design Technology – IPK Berlin Germany Institute for Machine Tools and Factory Management IWF - Technische Universität Berlin Germany

Smart linking, evaluation and provision of information over the life cycle of a product are becoming growingly important. The use of information extracted from combination of monitoring data, product data, maintenance information, and from product utilisation data can increase the availability of production machines and reduce the costs and resources cause by machine downtime. Especially for new manufacturing technologies such as Selective Laser Melting, the storage and management of such information are crucially important to develop knowledge and improve the quality of the machines and their products. By acquiring data from the machine, processing them and calculating proper key performance indicators, the critical region where the failures are most commonly found and the critical subsystems responsible for the failures are identified. Moreover, using the historical data, the tolerances for those subsystems can be defined. © 2017 The Authors

关键词： data analysis life cycle monitoring selective laser melting Smart linking

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Decentralized Data Analytics for Maintenance in Industrie 4.0

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Procedia Manufacturing 2017年 11卷 1120-1126页

作者： Uhlmann, Eckart Laghmouchi, Abdelhakim Geisert, Claudio Hohwieler, Eckhard Fraunhofer Institute for Production Systems and Design Technology – IPK Berlin Germany Institute for Machine Tools and Factory Management IWF - Technische Universität Berlin Germany

Due to the increased digital networking of machines and systems in the production area, large datasets are generated. In addition, more external sensors are installed at production systems to acquire data for production and maintenance optimization purposes. Therefore, data analytics and interpretation is one of the challenges in Industrie 4.0 applications. Reliable analysis of data (e.g. internal and external sensors), such as system-internal alarms and messages produced during the operation, can be used to optimize production and maintenance processes. Furthermore, information and knowledge can be extracted from raw data and used to develop data-driven business models and services, e.g. offer new availability contracts for production systems. This paper illustrates an approach for decentralized data analytics based on smart sensor networks. © 2017 The Authors

关键词： algorithms classifier data analytics Industrie 4.0 production systems

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Cluster identification of sensor data for predictive maintenance in a Selective Laser Melting machine tool

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Procedia Manufacturing 2018年 24卷 60-65页

作者： Eckart Uhlmann Rodrigo Pastl Pontes Claudio Geisert Eckhard Hohwieler Fraunhofer Institute for Production Systems and Design Technology (IPK) Pascalstraße 8-9 Berlin 10587 Germany Technische Universität Berlin – Institute for Machine Tools and Factory Management (IWF) Pascalstraße 8-9 Berlin 10587 Germany

Selective laser melting has become one of the most current new technologies used to produce complex components in comparison to conventional manufacturing technologies. Especially, existing selective laser melting machine tools are not equipped with analytics tools that evaluate sensor data. This paper describes an approach to analyze and visualize offline data from different sources based on machine learning algorithms. Data from three sensors were utilized to identify clusters. They illustrate the normal operation of the machine tool and three faulty conditions. With these results, a condition monitoring system can be implemented that enables those machine tools for predictive maintenance solutions.

关键词： Selective laser melting sensor data machine learning clustering predictive maintenance

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Smoothed Particle Hydrodynamics simulation of the machining process of Inconel 718

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Procedia Manufacturing 2018年 18卷 1-11页

作者： Eckart Uhlmann Enrico Barth Fraunhofer Institute for Production Systems and Design Technology (IPK) Pascalstr. 8-9 10587 Berlin Germany Institute for Machine Tools and Factory Management (IWF) Technische Universität Berlin Pascalstr. 8-9 10587 Berlin Germany

The Smoothed Particle Hydrodynamics (SPH) is a meshfree simulation method which allows to describe the machining process of the nickel-based alloy Inconel 718. This paper analyses the accuracy of a developed simulation model for an orthogonal turning process. The simulation results show the occurring process forces. Subsequently, the model is extended with a variable chip thickness to consider the different engagement condition during a peripheral down milling process. The model allows to investigate the influence of geometry parameters of the cutting edge for possible tools designs with little economic and financial expenses.

关键词： nickel-based alloys milling tool Smoothed-particle hydrodynamics, simulation machining process tool development cutting forces

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