Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
Session Overview
Session
SES 8.3: Smart Factories and Industrial IoT
Time:
Thursday, 29/Jun/2017:
9:00am - 10:00am

Session Chair: Dusan Sormaz
Location: Aula O (first floor)

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Presentations

105. Simulation based Validation of Effects through ICT enabled Real-Time-Capability in Production Planning: an Example from Engineer-to-Order Plant Building Industry

Patrick Dallasega1, Rafael A. Rojas C.1, Erwin Rauch1, Dominik T. Matt1,2

1Free University of Bolzano, Italy; 2Fraunhofer Italia Research s.c.a.r.l., Innovation Engineering Center (IEC), via Macello 57, Bolzano, 391

With the actual trend towards Industry 4.0, new technologies will provide the digitalization of general data and make it available in real-time and worldwide through the Internet. Information, in the specific, becomes the new gold for the smart and digital factory of the future. Especially in production planning and control, a real-time decision-making capability represents high potentials for optimizations along the whole supply chain. Moreover, the ability modeling the behavior of those systems endows decision-makers with forecasting possibilities. In this paper, a simulation-based approach is presented to validate and verify the effects of an ICT-enabled and nearly real time capable production planning approach. The approach has been applied in an example from Engineer-to-Order (ETO) plant building industry, where engineering elaborates the technical design, fabrication produces the components and installation teams perform the final assembly on-site. Traditionally production planning is centralized following a master schedule that rarely is up to date, ignoring deviations on-site. As a result, components are manufactured and delivered in advance creating non-value adding activities and inventory levels. Furthermore, if production planning is based on not current data, the priority in order-release at the fabrication shop is not optimized for a Just-in-Time delivery on-site. Thus, in this paper, we propose a decentralized and ICT-supported near real-time capable production planning approach.


333. Trustworthiness Requirements for Manufacturing Cyber-Physical Systems

Radu Babiceanu, Remzi Seker

Embry-Riddle Aeronautical University, United States of America

Distributed manufacturing operations include cyber-physical systems vulnerable to cyber-attacks. Long time not considered a priority, cybersecurity jumped to the forefront of manufacturing concerns due to the need to network together legacy, newer equipment, and entire operation centers. This paper proposes trustworthiness solutions for integrated manufacturing physical-cyber worlds, where trustworthiness is defined to complement system dependability requirements with cybersecurity requirements, such that the resulting manufacturing cyber-physical system delivers services that can justifiably be trusted. Acknowledging the inevitability of cyber-attacks, the paper models the cybersecurity component using the resilient systems framework, where system resilience is viewed as preservation of a required state of cybersecurity.


254. Virtual commissioning of camera-based quality assurance systems for mixed model assembly lines

Nils Piero1, Michael Schmitt2

1GSaME Uni Stuttgart, Germany; 2Fraunhofer Institute for Computer Graphics Research IGD, 64283 Darmstadt, Germany

Mixed model assembly lines are subject to increasing complexity due to increasing variants variety per assembly line, short product life cycles and increased product complexity. Especially in the automotive production the integration of new variants and segregation of discontinued variants in short intervals lead to constant change in mixed model assembly lines. The demand for high-quality products compels the quality assurance to provide methods that meet these changing requirements.

Existing methods of quality assurance usually answer the questions to completeness, correct component variants and correct position of mounted components based on simple image processing techniques. Real images are compared to reference images in order to perform a binary classification of individual test features. The required reference images are extracted from real images. Those methods can only be applied when the production of a new model has been running for some time and extensive manual teaching processes have been completed. Quality assuring systems should be able to meet these changes and be in action for early phases in the product ramp-up. Thus, possible production errors can be detected early and countermeasures can be initiated.

We propose a new Computer Vision based technique to ensure the quality of the automotive aggregate assembly. It uses CAD Data that is already available from the product planning phase to check all product variants on the assembly line for misplaced or wrong components. One does not need to collect data from real images to teach the decision making algorithm. This solution can hence be used already in the ramp-up phase.

Our method can also easily adapt to changes of production, where existing methods need to redo the whole teaching process, we just need to set the new nominal position of the part in the reference coordinate system. This can even be done semi automatically and synchronously with the changes in the assembly line.



 
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