Integrating UML diagrams for production control systems
Proceedings of the 22nd international conference on Software engineering
Proceedings of the 22nd international conference on Software engineering
Modeling Cooperative Business Processes and Transformation to a Service Oriented Architecture
CEC '05 Proceedings of the Seventh IEEE International Conference on E-Commerce Technology
X-Gen: a random test-case generator for systems and SoCs
HLDVT '02 Proceedings of the Seventh IEEE International High-Level Design Validation and Test Workshop
A Production Process Mixed Modeling for Marine Diesel Engine Based on IDEF0 and Petri Net
ISISE '08 Proceedings of the 2008 International Symposium on Information Science and Engieering - Volume 02
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The continuing growth in the complexity of production processes is driven mainly by the integration of smart and cheap devices, such as sensors and custom hardware or software components. This naturally leads to higher complexity in fault detection and management, and, therefore to a higher demand for sophisticated quality control tools. A production process is commonly modeled prior to its physical construction to enable early testing. Many simulation platforms were developed to assess the widely varying aspects of the production process, including physical behavior, hardware-software functionality, and performance. However, the efficacy of simulation for the verification of modeled processes is still largely limited by manual operation and observation. We propose a massive random-biased, ontology-based, test-generation methodology for system-level verification of production processes. The methodology has been successfully applied for simulation-based processor hardware verification and proved to be a cost-effective solution. We show that it can be similarly beneficial in the verification of production processes and control.