Improving transferability between different engineering stages in the development of automated material flow modules

For improving flexibility and robustness of the engineering of automated production systems (aPS) in case of extending, reducing or modifying parts, several approaches propose an encapsulation and clustering of related functions, e.g. from the electrical, mechanical or software engineering, based on a modular architecture. Considering the development of these modules, there are different stages, e.g. module planning or functional engineering, which have to be completed. A reference model that addresses the different stages for the engineering of aPS is proposed by AutomationML. Due to these different stages and the integration of several engineering disciplines, e.g. mechanical, electrical/electronic or software engineering, information not limited to one discipline are stored redundantly increasing the effort to transfer information and the risk of inconsistency. Although, data formats for the storage and exchange of plant engineering information exist, e.g. AutomationML, fixed domain specific structures and relations of the information, e.g. for automated material flow systems (aMFS), are missing. This paper presents the integration of a meta model into the development of modules for aMFS to improve the transferability and consistency of information between the different engineering stages and the increasing level of detail from the coarse-grained plant planning to the fine-grained functional engineering.Comment: 11 pages, https://ieeexplore.ieee.org/abstract/document/7499821

In-situ identification of material batches using machine learning for machining operations

Abstract In subtractive manufacturing, differences in machinability among batches of the same material can be observed. Ignoring these deviations can potentially reduce product quality and increase manufacturing costs. To consider the influence of the material batch in process optimization models, the batch needs to be efficiently identified. Thus, a smart service is proposed for in-situ material batch identification. This service is driven by a supervised machine learning model, which analyzes the signals of the machine’s control, especially torque data, for batch classification. The proposed approach is validated by cutting experiments with five different batches of the same specified material at various cutting conditions. Using this data, multiple classification models are trained and optimized. It is shown that the investigated batches can be correctly identified with close to 90% prediction accuracy using machine learning. Out of all the investigated algorithms, the best results are achieved using a Support Vector Machine with 89.0% prediction accuracy for individual batches and 98.9% while combining batches of similar machinability

Novel Approach using Risk Analysis Component to Continuously Update Collaborative Robotics Applications in the Smart, Connected Factory Model

Building on the idea of Industry 4.0, new models of the highly connected factory that leverage factory‐generated data to introduce cost‐effective automation and involve the human worker for creating higher added value are possible. Within this context, collaborative robots are becoming more common in industry. However, promises regarding flexibility cannot be satisfied due to the challenging process of ensuring human safety. This is because current regulations and standards require updates to the risk assessment for every change to the robotic application, including the parts involved, the robotic components, and the type of interaction within the workspace. This work presents a novel risk analysis software tool that was developed to support change management for adaptive collaborative robotic systems in the connected factory model. The main innovation of this work is the tool’s ability to automatically identify where changes have been made to components or processes within a specific application through its integration with a connected factory architecture. This allows a safety expert to easily see where updates to the risk assessment are required, helping them to maintain conformity with the CE marking process despite frequent changes. To evaluate the benefits of this tool, a user study was performed with an exemplary use-case from the SHOP4CF project. The results show that this newly developed technology for risk assessment has better usability and lower omission errors when compared to existing methods. Therefore, this study underlines the need for tools that can help safety engineers cope with changes in flexible robotics applications and reduce omission errors.publishedVersionPeer reviewe

Enabling Flexible Automation System Hardware - Dynamic Reconfiguration of a Real-Time Capable Field-Bus

The increasing demand for adaptive and flexible machines and plants raises the need for components and systems that can be dynamically reconfigured. Therefore, production facilities are especially needed to allow for connecting or disconnecting machine modules at runtime to allow for flexible production orders and varying product types. Many highly sophisticated approaches e.g. multi-agent systems and service-oriented architectures support reconfiguration at the application layer, but the communication link between the components' hardware has not yet been appropriately considered. However, the deterministic behavior of a field-bus is required to perform such a system reconfiguration of plants without violating the maximum cycle time of control applications. Considering these constraints, two different approaches are presented and discussed in this paper -- a single-master and a multi-master approach. Based on an extension of the standard EtherCAT state machine, it is shown how flexibility can increase by reconfiguration of the field-bus communication. An evaluation of the reconfiguration time illustrates the impact to the control application

Bridging the gap between discrete and continuous simulation of logistic systems in production

The demand for mass-customized products is increasing steadily. As a consequence, the requirements regarding flexibility and efficiency of automated production systems and their logistic parts are increasing as well. In order to evaluate different plant designs before the commissioning or due to necessary reconfigurations, model-based approaches in combination with simulation techniques can be applied. Therefore, the interaction of transported load and physical aspects of plant components, e.g. motor current, needs to be described and evaluated. To bridge the gap between the time-discrete behavior of manufactured products, i.e. their occurrence, and the continuous characteristics of a transportation system's modules, i.e. differential equations, in this work a model interface was developed and integrated into model classes of the Modelica modeling language. The correct behavior of the particular library items has been proven by tests of the logical behavior and the comparison to a real system

Agentenorientierte Verknüpfung existierender heterogener automatisierter Produktionsanlagen durch mobile Roboter zu einem Industrie-4.0-System

Die Anforderungen hinsichtlich der Flexibilität steigen durch die Herstellung komplexer, auf den Endkunden individualisierter Produkte (Mass Customization). Das Kapitel zeigt die Verknüpfung von Unternehmen mit verschiedenen Kernkompetenzen im Rahmen von Industrie 4.0. Eine Agentenplattform dient als Basis für die Zusammenarbeit verschiedener Unternehmen an einem gemeinsamen Produkt sowie dessen Transport durch mobile Roboter in einem gemeinsamen Produktionsnetzwerk. Die Aspekte der modellbasierten Entwicklung des Demonstrators sowie sein Verhalten unter realitätsnahen Einsatzbedingungen ergeben wichtige Faktoren für die Weiterentwicklung der Referenzarchitektur sowie die Portierung dieser in andere Domänen

Industrie 4.0 für die Roboterkooperation

Der Begriff Industrie 4.0 ist aktuell in aller Munde und wird auch stark diskutiert. Die von der Bundesregierung gestartete Initiative soll den Industriestandort Deutschland zukunftssicher und konkurrenzfähig halten. Viele Firmen und Forschungsinstitute beschäftigen sich aktuell mit dem Thema und entwickeln neue Ansätze, welche mithilfe von Demonstratoren veranschaulicht werden sollen. Der Begriff Industrie 4.0 wird dabei oft mit unterschiedlicher Bedeutung verwendet und ist deshalb schwer greifbar

Automated model generation in the electrical automotive driveline components

Model based techniques are increasingly applied to develop mechatronic systems. The Automated modeling methodology supports the model synthesis of electrical components included in the automotive driveline. Since the time to market of vehicles is decreasing steadily and the demand for efficiency and quality is increasing model based methods are required to enable the possibility of simultaneous engineering. The proposed solution is based on available measurement series of previously validated components. A model generation library enables the assembling of analogous models according to a predefined component classification. To increase the transparency and quality of the models experts can implement extensions and predefine parameters of the physical software models. Subsequently a parameter estimation identifies the values of the parameters by the objective to minimize the difference between measured values and simulation results