Control logic verification for an automotive body assembly line using simulation

Park, Chang Mok; Park, Sang-Chul; Wang, Gi-Nam

doi:10.1080/00207540802350716

Cited by 12 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In their work, they discuss conducted orthogonal experiments to optimize the parameters of the hybrid propulsion system. Park et al [28] proposed an approach that verified the controller logic processes for the automobile industry via simulation. They described a state-based object model that created a virtual car body assembly line and a verification methodology using observed signal sequences during the simulation.…”

Section: Literaturementioning

confidence: 99%

A meta-model based simulation optimization using hybrid simulation-analytical modeling to increase the productivity in automotive industry

Dengız

İç

Belgin

2016

Mathematics and Computers in Simulation

View full text Add to dashboard Cite

Section: Literaturementioning

confidence: 99%

A meta-model based simulation optimization using hybrid simulation-analytical modeling to increase the productivity in automotive industry

Dengız

İç

Belgin

2016

Mathematics and Computers in Simulation

View full text Add to dashboard Cite

“…The generation of this information comes from a variety of tools and processes. During the early phase of planning and commissioning the manufacturing system, virtual commissioning is employed to create a system that adheres to specifications (Lee and Park 2014;Park, Park, and Wang 2009). In the next state when production has begun, the manufacturing process is monitored and visualised in a virtual replica of the actual system using the concept of digital twin (Tao et al 2018b).…”

Section: Smart Factoriesmentioning

confidence: 99%

Towards data-driven approaches in manufacturing: an architecture to collect sequences of operations

Farooqui

Bengtsson

Falkman

et al. 2020

International Journal of Production Research

View full text Add to dashboard Cite

The technological advancements of recent years have increased the complexity of manufacturing systems, and the ongoing transformation to Industry 4.0 will further aggravate the situation. This is leading to a point where existing systems on the factory floor get outdated, increasing the gap between existing technologies and state-of-the-art systems, making them incompatible. This paper presents an event-based data pipeline architecture, that can be applied to legacy systems as well as new state-of-the-art systems, to collect data from the factory floor. In the presented architecture, actions executed by the resources are converted to event streams, which are then transformed into an abstraction called operations. These operations correspond to the tasks performed in the manufacturing station. A sequence of these operations recount the task performed by the station. We demonstrate the usability of the collected data by using conformance analysis to detect when the manufacturing system has deviated from its defined model. The described architecture is developed in Sequence Planner -a tool for modelling and analysing production systems -and is currently implemented at an automotive company as a pilot project.

show abstract

“…In literature, PLC controlled device behaviour is usually represented by a state-based I/O model or by using a similar kind of automaton model [6, 21, 22]. A state-based I/O model of a device actually expresses the functional role of its control signals.…”

Section: Plat: Control Process Model Design and Working Proceduresmentioning

confidence: 99%

“…After reaching the end position, Robot 1 picks up the part from it and, then, the Part Loader returns back to its home position. In Figure 4(a), state-based I/O model of that Part Loader device is given (see, e.g., [21]). As can be seen, Part Loader is operated based on five control signals.…”

Section: Plat: Control Process Model Design and Working Proceduresmentioning

confidence: 99%

PLAT: An Automated Fault and Behavioural Anomaly Detection Tool for PLC Controlled Manufacturing Systems

Ghosh

Qin

Lee

et al. 2016

Computational Intelligence and Neuroscience

Self Cite

View full text Add to dashboard Cite

Operational faults and behavioural anomalies associated with PLC control processes take place often in a manufacturing system. Real time identification of these operational faults and behavioural anomalies is necessary in the manufacturing industry. In this paper, we present an automated tool, called PLC Log-Data Analysis Tool (PLAT) that can detect them by using log-data records of the PLC signals. PLAT automatically creates a nominal model of the PLC control process and employs a novel hash table based indexing and searching scheme to satisfy those purposes. Our experiments show that PLAT is significantly fast, provides real time identification of operational faults and behavioural anomalies, and can execute within a small memory footprint. In addition, PLAT can easily handle a large manufacturing system with a reasonable computing configuration and can be installed in parallel to the data logging system to identify operational faults and behavioural anomalies effectively.

show abstract

Control logic verification for an automotive body assembly line using simulation

Cited by 12 publications

References 9 publications

A meta-model based simulation optimization using hybrid simulation-analytical modeling to increase the productivity in automotive industry

A meta-model based simulation optimization using hybrid simulation-analytical modeling to increase the productivity in automotive industry

Towards data-driven approaches in manufacturing: an architecture to collect sequences of operations

PLAT: An Automated Fault and Behavioural Anomaly Detection Tool for PLC Controlled Manufacturing Systems

Contact Info

Product

Resources

About