Communication architecture for multiple distributed large volume metrology systems

Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2021

2022

Self Cite

In Line-less Mobile Assembly Systems (LMAS) the mobilization of assembly resources and products enables rapid physical system reconfigurations to increase flexibility and adaptability. The clean-floor approach discards fixed anchor points, so that assembly resources such as mobile robots and automated guided vehicles transporting products can adapt to new product requirements and form new assembly processes without specific layout restrictions. An associated challenge is spatial referencing between mobile resources and product tolerances. Due to the missing fixed points, there is a need for more positioning data to locate and navigate assembly resources. Distributed large-scale metrology systems offer the capability to cover a wide shop floor area and obtain positioning data from several resources simultaneously with uncertainties in the submillimeter range. The positioning of transmitter units of these systems becomes a demanding task taking visibility during dynamic processes and configuration-dependent measurement uncertainty into account. This paper presents a novel approach to optimize the position configuration of distributed large-scale metrology systems by minimizing the measurement uncertainty for dynamic assembly processes. For this purpose, a particle-swarm-optimization algorithm has been implemented. The results show that the algorithm is capable of determining suitable transmitter positions by finding global optima in the assembly station search space verified by applying brute-force method in simulation.

Section: Approaches For Transmitter Positioningmentioning

confidence: 99%

Section: Particle-swarm-optimizationmentioning

confidence: 99%

Transmitter Positioning of Distributed Large-Scale Metrology Within Line-Less Mobile Assembly Systems

Nicksch

Hüttner

Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2021

2022

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“…For the positioning at the target point and in order to exactly determine the deviation between the real and desired position, a fine-tuning and control of the movement by measurement systems with a lower uncertainty is necessary. This dynamic change in the requirement for measurement uncertainty during the process can, for example, be met by using additional, external measurement systems and including them into the metrological reference frame Forbes et al, 2013;Wang et al, 2011;Ghidary et al, 1999;Li et al, 2013;Montavon et al, 2017). As a result, the role of production metrology changes away from the acquisition and connection of individual characteristics towards a holistic description of the products using additional data from the entire product life cycle.…”

Section: Challenges and Trends For Metrology Inmentioning

confidence: 99%

“…Current developments are the STEP data format for product data within the ISO 10303 series (ISO, 2018), the RAMI model for the architecture description of Industry 4.0 components (VDI, 2017;DIN, 2016;Löwen et al, 2017Löwen et al, , 2016 and OPC-UA as a communication protocol for machine data (Candido et al, 2010;Hannelius et al, 2008;Montavon et al, 2017). Further approaches can be found in context-based data formatting (Perera et al, 2014a), self-describing data models (NetCDF 2017), a universal description of metadata (Guenther and Radebaugh, 2004;Koza, 2003) and especially adapted descriptions for sensor networks in Li et al (2013), Qingping et al (2014), Huang and Javed (2008), Goos et al (2003), Compton et al (2009), Barnaghi et al (2009) and Kim et al (2008).…”

Section: Metrology Architecture For Cyber-physical Production Systemsmentioning

confidence: 99%

Sensor information as a service – component of networked production

Voigtmann

2018

J. Sens. Sens. Syst.

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Abstract. Metrology has a key position in networked, adaptive production, with the task of a holistic and valid assessment of the state of various production scenarios. With the diminishing focus on a device-specific development towards an adaptive production network, which is less hierarchical in the sense of the "Internet of production", and with the focus on the properties of cyber-physical systems (CPSs), new opportunities for the strengthening of metrology arise. Characteristic of these CPSs are sensors for multi-modal data acquisition, actuators for interaction with the environment, distributed computing power and the ability to spontaneously or permanently network itself. They form the basis for the creation of a "digital shadow" and thus are essential components of a model for process control. Current trends and challenges for metrology in networked production, such as multi-sensor systems, model-based measurements, virtual measurement processes or the integration into adaptable production systems, broaden the boundaries of future requirements of metrology, in particular with regard to its flexibility, speed and compatibility. A prerequisite is a scalable, specifiable information fusion. A solution to this is the service-based provision of sensor information, measurement data and decisions, which can be flexibly adapted to task-specific requirements. For this concept of "sensor information as a service", development stages and prerequisites for its implementation as well as affected areas are discussed.

“…Since there are less fixed anchor points for localization and navigation of assembly resources, factory-wide positioning data of assembly resources become relevant. [11,12,27,40]. Therefore, distributed metrology systems must provide a coordinate system for spatial referencing on the entire shop floor [7].…”

Section: Introductionmentioning

confidence: 99%

Virtual indoor-GPS for measurement uncertainty determination in reconfigurable environments

Nicksch

Sabzehi

2022

Prod. Eng. Res. Devel.

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In Line-less Mobile Assembly Systems, the mobilization of assembly resources and products enables rapid physical system reconfigurations to increase flexibility and adaptability. The clean floor approach discards fixed anchor points, so that assembly resources such as mobile robots and automated guided vehicles transporting products can adapt to new products and form new processes. Associated challenges are accurate spatial referencing between mobile resources to meet assembly tolerance requirements. There is a need for more accurate positioning data to locate and navigate mobile assembly resources. An indoor-GPS, as a distributed large-scale metrology system, is able to cover a wide shop floor area and to obtain positioning data with uncertainties in the submillimeter range. The measurement uncertainty of such a system depends on the spatial distribution of the transmitters and the receiver positions. To be able to validate positioning tolerance requirements of an assembly process, measurement uncertainties must be determined. Virtual measurements simulate measurement processes and model dependencies between the environment and the metrology system. This work presents a novel approach for a virtual indoor-GPS to determine measurement uncertainties during a process and to evaluate the measurement process capability. Experiments show the validity of the virtual indoor-GPS which can be used as a planning tool for metrology system setups within Line-less Mobile Assembly Systems.