Temporal and Flexible Automation of Machine Tools

Lienenlüke, Lars; Gründel, L.; Storms, Simon; Herfs, Werner; Königs, Michael; Servos, Michael

doi:10.1109/ines.2018.8523978

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…Neben zahlreichen ortsfesten Lösungen, wie bspw. für Fräsbearbeitungen [12,13], wurden in den letzten Jahren auch mobile Roboterzellen [8,14,15], teilweise als kollaborative Lösungen [13,16], entwickelt. Aktuelle Bestrebungen bestehen darin, Robotersysteme für ihre Aufgabe weiter zu flexibilisieren (Plug-and-Produce-Paradigma) [14], unter anderem im Bereich der flexiblen Steuerung und intuitiven Programmierung von Robotersystemen [11], und damit die industrielle Verbreitung zu fördern.…”

Section: Stand Der Technik Bei Der Programmierung Und Steuerung Von R...unclassified

Flexible BPMN-Steuerung für Robotersysteme/Flexible BPMN-based control for robotic systems

Kreuter,

Hold,

Schlund

et al. 2024

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Bei der Programmierung und Steuerung von Robotersystemen fehlen Standardisierungen, wodurch Experten bei Änderungen in den Randbedingungen mit Zeit- und Personalaufwand nachprogrammieren. Um den Programmieraufwand zu senken, wird ein Software-Framework zur BPMN-basierten Programmierung und Orchestrierung von skillbasierten Robotersteuerungen vorgestellt. Ein Bildverarbeitungs-Modul unterstützt bei der Parametrierung der Skills.

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Section: Stand Der Technik Bei Der Programmierung Und Steuerung Von R...unclassified

Flexible BPMN-Steuerung für Robotersysteme/Flexible BPMN-based control for robotic systems

Kreuter,

Hold,

Schlund

et al. 2024

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“…This hinders just-in-time deliveries, critical for meeting customer expectations [2]. For SMEs, bottlenecks are primarily associated with the setup of machines and robots required at every production change, due in part to the low degree of automation and low number of collaborative robots employed [1,3,4].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Literature Review: Key Performance Indicators on Feeding-as-a-Service

Monetti,

Bertoni,

Maffei

2024

Advances in Transdisciplinary Engineering

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In the evolving landscape of modern manufacturing, a novel concept known as Feeding-as-a-Service (FaaS) is emerging, part of the larger Automation-as-a-Service (AaaS) framework. FaaS aims to optimize feeding systems in cloud manufacturing environments to meet the demands of mass customization and allow for quick responses to production changes. Therefore, it fits into the Manufacturing-as-a-Service (MaaS) system as well. As the manufacturing industry undergoes significant transformations through automation and service-oriented models, understanding how FaaS fits into the other frameworks is essential. This study presents a systematic literature review with two primary objectives: first, to contextualize FaaS within AaaS and MaaS, highlighting similarities, differences, and distinctive characteristics; second, to identify and clarify the essential Key Performance Indicators (KPIs) crucial for its strategic implementation. KPIs are pivotal metrics guiding organizations toward manufacturing excellence. In this context, common KPIs focus on efficiency and quality, such as resource utilization, and error rates. Other KPIs are also crucial, such as the ones related to cost reduction and customer satisfaction. For FaaS, the most relevant include also data security, data management, and network speed. This research provides a valuable KPI framework for FaaS developers, aiding in strategic decision-making and deployment in industrial settings. It also contributes to a broader understanding of KPIs in manufacturing, which benefits both researchers and industrial practitioners. The results of the review, though, fail to address other crucial indicators for ‘as-a-Service’ business, such as Churn Rate and Total Contract Value. Future research will address these limitations through methods ranging from questionnaires to practitioner interviews, with the aim of gathering the knowledge needed for real-world implementations.

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“…Typical applications include basic handling applications (e.g., for parts, pallets) as well as more complex processes, such as welding or the assembly of parts ( Siciliano and Khatib, 2016 ). To add the necessary skills to the robot, assets, like grippers, sensors, and actuators, are applied to form task-specific static or mobile robot cells ( Lienenluke et al, 2018 - 2018; Wojtynek et al, 2019 ; Sanneman et al, 2020 ).…”

Section: Introduction: Current Challenges Of Controls For Robot Cellsmentioning

confidence: 99%

Flexible skill-based control for robot cells in manufacturing

Wiese

Abicht²,

Friedrich³

et al. 2022

Front. Robot. AI

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Decreasing batch sizes lead to an increasing demand for flexible automation systems in manufacturing industries. Robot cells are one solution for automating manufacturing tasks more flexibly. Besides the ongoing unifications in the hardware components, the controllers are still programmed application specifically and non-uniform. Only specialized experts can reconfigure and reprogram the controllers when process changes occur. To provide a more flexible control, this paper presents a new method for programming flexible skill-based controls for robot cells. In comparison to the common programming in logic controllers, operators independently adapt and expand the automated process sequence without modifying the controller code. For a high flexibility, the paper summarizes the software requirements in terms of an extensibility, flexible usability, configurability, and reusability of the control. Therefore, the skill-based control introduces a modularization of the assets in the control and parameterizable skills as abstract template class methodically. An orchestration system is used to call the skills with the corresponding parameter set and combine them into automated process sequences. A mobile flexible robot cell is used for the validation of the skill-based control architecture. Finally, the main benefits and limitations of the concept are discussed and future challenges of flexible skill-based controls for robot cells are provided.

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Temporal and Flexible Automation of Machine Tools

Cited by 5 publications

References 4 publications

Flexible BPMN-Steuerung für Robotersysteme/Flexible BPMN-based control for robotic systems

Flexible BPMN-Steuerung für Robotersysteme/Flexible BPMN-based control for robotic systems

A Systematic Literature Review: Key Performance Indicators on Feeding-as-a-Service

Flexible skill-based control for robot cells in manufacturing

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