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Bio-inspired control of automated stem cell production

Procedia CIRP 2020年 88卷 600-605页

作者： Péter Egri Balázs Cs. Csáji Krisztián B. Kis László Monostori József Váncza Jelena Ochs Sven Jung Niels König Robert Schmitt Christian Brecher Simon Pieske Stephan Wein Centre of Excellence in Production Informatics and Control Institute for Computer Science and Control Kende u. 13-17 Budapest 1111 Hungary Fraunhofer Institute for Production Technology Steinbachstr. 17 Aachen 52074 Germany Laboratory for Machine Tools and Production Engineering (WZL) RWTH Aachen University Campus-Boulevard 30 Aachen 52074 Germany

The possible role of stem cells in medical treatments can hardly be overestimated. Today they are produced – almost without exemption – with significant human involvement using adaptive protocols that take the growth behavior of the biological material into account. Automated production platforms are being developed and tested in a number of research laboratories with the main goals of improving reproducibility, as well as increasing quality and throughput. However, automated stem cell production differs from the traditional manufacturing processes in (1) the inherent diversity of the products (stem cells), (2) their varying growth rates and process times, (3) the need for their regular observation and process adaptation, and, therefore, (4) for mixed-initiative production control. A distinctive feature of the domain is the symbiotic co-existence and co-evolution of the technical, ICT and biological ingredients in production structures. A challenging way to overcome these issues is the use of biologically-inspired control algorithms. In the paper the application of reinforcement learning is proposed for this purpose. As a first step, a digital simulation of the stem cell production was performed in order to generate patterns for the training process and to test the approach. In addition to the description of the concept, the paper also presents initial research results.

关键词： Stem cell production Digital simulation machine learning Reinforcement-based control Biological transformation

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Web-based Platform for Data Analysis and Monitoring

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Procedia CIRP 2019年 86卷 31-36页

作者： Colin Reiff Stefan Oechsle Florian Eger Alexander Verl Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) University of Stuttgart Seidenstrasse 36 70174 Stuttgart Germany

With the increasing number of sensors installed in production systems and the associated amount of recorded data, a high potential for optimizing manufacturing processes is offered. Based on the gathered information, data analyzing methods can be used to identify correlations and thus, optimize complex multi-stage production systems with the goal of zero-defect manufacturing. For this purpose, multiple statistical methods have to be applied, which is often a time-consuming task and currently requires trained and experienced specialists. Furthermore, the investigated data must be kept up to date for the early recognition of changes possibly leading to cost-intensive scrap and production downtimes. To address these issues, a web-based platform with an intuitive user interface was developed. The platform can access and process various data sources using modular methods. In addition, as part of continuous monitoring, the results of these analyzing steps can be dynamically calculated. The aim of the platform is to make complex analysis processes accessible to machine operators and thus to combine domain expertise and statistical knowledge. The paper describes the underlying architecture and the relevant interfaces of the platform.

关键词： Data Analysis Monitoring Process Optimization

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Reinforcement Learning Approach to Vibration Compensation for Dynamic Feed Drive Systems

Reinforcement Learning Approach to Vibration Compensation fo...

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Artificial Intelligence for Industries (AI4I)

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

ISBN: (数字)9781728140872

ISBN: (纸本)9781728140889

Vibration compensation is important for many domains. For the machine tool industry it translates to higher machining precision and longer component lifetime. Current methods for vibration damping have their shortcomings (e.g. need for accurate dynamic models). In this paper we present a reinforcement learning based approach to vibration compensation applied to a machine tool axis. The work describes the problem formulation, the solution, the implementation and experiments using industrial machine tool hardware and control system.

关键词： Vibrations machine tools Learning (artificial intelligence) Hardware Optimization Feeds Entropy

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Reinforcement Learning of a Robot Cell control Logic using a Software-in-the-Loop Simulation as Environment

Reinforcement Learning of a Robot Cell Control Logic using a...

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Artificial Intelligence for Industries (AI4I)

作者： Florian Jaensch Akos Csiszar Janik Sarbandi Alexander Verl Institute of Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Stuttgart Germany

ISBN: (数字)9781728140872

ISBN: (纸本)9781728140889

This paper introduces a method for automatic robot programming of industrial robots using reinforcement learning on a Software-in-the-loop simulation. The focus of the the paper is on the higher levels of a hierarchical robot programming problem. While the lower levels the skills are stored as domain specific program code, the combination of the skills into a robot control program to solve a specific task is automated. The reinforcement learning learning approach allows the shopfloor workers and technicians just to define the end result of the manufacturing process through a reward function. The programming and process optimization is done within the learning procedure. The Software-in-the-loop simulation with the robot control software makes it possible to to interpret the real program code and generate the exact motion. The exact motion of the robot is needed in order to find not just an optimal but also a collision-free policy.

关键词： Optimization Learning (artificial intelligence) Service robots Robot control Software

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Method for load-capable path planning in multi-axis fused deposition modeling

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Procedia CIRP 2019年 84卷 335-340页

作者： Frederik Wulle Martin Wolf Oliver Riedel Alexander Verl Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) University of Stuttgart Seidenstr. 36 70174 Stuttgart Germany

Additive Manufacturing (AM) technologies have evolved from a niche application for rapid prototyping into a crucial technological and economically significant industry for functional parts. However, many processes cannot guarantee the required component properties for industrial applications. Here, anisotropic strength properties introduced in Fused Deposition Modeling (FDM) are major constraints. These production-related strength properties originate in the weak bonds between plastic beads and thus between layers, which considerably affects the strength of the part in multi-axial stress conditions. Multi-axis FDM printing enables the diminishment of this effect by using Curved Layer Slicing. However, suitable path planning tools have not been developed to realize this approach on an industrial level. This paper describes a method for strength optimization under consideration of the manufacturing boundary conditions. This path planning method is based on a subdivision of the CAD part into sub-volumes, which comprise homogeneous sets of principal stress trajectories, as determined by the Finite Element Analysis. Subsequently, the sub-volumes are sliced individually using a Curved Layer Slicing approach and paths on the different layers are aligned with the principal stress trajectories. This method generates and uses strength-oriented meta data, which closes a crucial interface between CAD, CAM and AM processes.

关键词： Additive Manufacturing Design optimization machine tool Polymer

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Production planning and control systems – a new software architecture Connectivity in target

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Procedia CIRP 2019年 79卷 361-366页

作者： Carsten Ellwein Anja Elser Oliver Riedel Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Seidenstr. 36 D-70174 Stuttgart Germany

Since the rise of production planning and control systems those systems were subject to changes and further development due to new requirements and possibilities. Recently, Industry 4.0 and especially the cloud manufacturing paradigm brought up new requirements as connectivity demands, which are not fulfilled by current realizations of production planning and control systems. To adapt such systems to connectivity requirements, current software architectures have to be revised. Therefore, within this paper a new software architecture for production planning and control systems is introduced providing a basis for data exchange in-between such systems.

关键词： Planning control systems Software architecture Industry 4.0 Cloud manufacturing

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Real-time co-simulation for the virtual commissioning of production systems

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Procedia CIRP 2019年 79卷 397-402页

作者： Christian Scheifele Alexander Verl Oliver Riedel Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) University of Stuttgart Seidenstr. 36 Stuttgart 70174 Germany

During virtual commissioning, the real control technology is put into operation in advance using a model-based digital twin of the production system. The growing demands on the significance of digital twins result in the need for higher model depths and model scopes of the behavior models used. However, new approaches to enhance the usable computing power in a real-time simulation are required. This paper presents a real-time co-simulation platform for virtual commissioning. The platform includes the integration of powerful technology-specific simulation solutions based on integration interfaces and a real-time co-simulation architecture based on partitioning, parallelization, synchronization and data exchange mechanisms.

关键词： real-time co-simulation hardware-in-the-loop simulation virtual commissioning digital twin virtual CNC physics-based simulation

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Test case generation for production systems with model-implemented fault injection consideration

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Procedia CIRP 2019年 79卷 268-273页

作者： Karl Kübler Elmar Schwarz Alexander Verl Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) University of Stuttgart Seidenstr. 36 70174 Stuttgart Germany

Complex production systems, which have to handle product diversity and short product life cycles, can only be stable and efficient when successfully tested prior to their start-up. Also, changes in automation software within the life cycle of a production system should be verified through repetitive testing before applying the new software version. Therefore, test automation in combination with fault injection on a virtual model of the production system is seen as the next step to improve the verification and validation of a production system. This work presents an approach to achieve negative test cases for a test automation framework.

关键词： Negative testing Test automation Virtual commissiong Hardware-in-the-loop

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Automated OPC UA address space generation from existing data structures

Automated OPC UA address space generation from existing data...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Christian von Arnim Sebastian Friedl Armin Lechler Alexander Verl Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) University of Stuttgart Stuttgart Germany

If existing communication systems are converted to OPC UA, an OPC UA information model is required for the existing data structures. In small systems, an information model can be created manually with reasonable effort. In systems with numerous data structures, however, the effort for manual creation is too demanding. In order to continue using the existing data structures in the code without having to handle OPC UA-specific data structures, a transformation between the existing data structures and the OPC UA information model is necessary. This article presents a possibility of generating an OPC UA information model by a description of the existing data structures, e.g. generated based on ROS messages. The description of data structures is evaluated at runtime and linked with the existing data structures. The different representations for the different access types (read, request/response) in the OPC UA information model are discussed. In addition, an OPC UA client can read the information model from the descriptions of the data structures and write the read data back to the original data structure. In this way, both the provision of the data via OPC UA and the reception are possible. The adaptation effort of the existing software is minimal, since the existing structures are automatically populated and can still be used as usual.

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Collective Cloud Manufacturing for Maintaining Diversity in Production through Digital Transformation

Collective Cloud Manufacturing for Maintaining Diversity in ...

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IEEE Conference on Commerce and Enterprise Computing, CEC

作者： Matthias Milan Strljic Oliver Riedel Armin Lechler Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) University of Stuttgart Stuttgart Germany

In the area of production, individual domains are growing more and more closely together. Producing value-added chains and complex supply chains is becoming increasingly intertwined in the process of production planning. The exchange of data between the involved partners plays a critical role. The needs of the customer, the current order situation of the supplier as well as the manufacturing process dependencies of the manufacturer must be coordinated in a symbiotic manner to achieve efficient production. Therefore, this paper presents a variant of cloud manufacturing, collective cloud manufacturing, which focuses on the formation of cross-company symbioses to exchange production information in an environment isolated for data protection via an evolutionary needs model. For this purpose, a previously presented concept and the challenges of industry strategies are described to show how the concept of collective cloud manufacturing can meet the requirements of digitalization.

关键词： Production Manufacturing Cloud computing Data models Solid modeling Companies

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