Quality control agents for adaptive visual inspection in production lines

Stroppa, L.; Rodrigues, Nélson; Leitão, Paulo; Paone, Nicola

doi:10.1109/iecon.2012.6389188

Cited by 14 publications

(8 citation statements)

References 8 publications

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“…Beyond some initial promising research and development activities, such as discussed in Theiss et al (2009) and Stroppa et al (2012), the required real-time capabilities of agent technology, which limit the applicability of agent-based systems, are still an open problem. Beyond some initial promising research and development activities, such as discussed in Theiss et al (2009) and Stroppa et al (2012), the required real-time capabilities of agent technology, which limit the applicability of agent-based systems, are still an open problem.…”

Section: Conclusion and Overviewmentioning

confidence: 99%

Agent-Based Control of Production Systems—and Its Architectural Challenges

2015

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Section: Conclusion and Overviewmentioning

confidence: 99%

Agent-Based Control of Production Systems—and Its Architectural Challenges

2015

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“…Diverse scripting methods between these programming languages currently exist, allowing the execution of multiple routines calls and interconnections. Among others, interconnected tools could be an image acquisition system, a processing module and a multiagent control platform [8].…”

Section: System Architecturementioning

confidence: 99%

“…By means of this definition, it is attainable to estimate the normalized relative quality of the images processed. The corresponding Q expression is presented in equation (8). …”

Section: A Parameters and Global Quality Functionmentioning

confidence: 99%

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Adaptive image pre-processing for quality control in production lines

Arroyo

Lima

Leitão

2013

2013 IEEE International Conference on Industrial Technology (ICIT)

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Abstract-Flexible and self-adaptive behaviours in automated quality control systems are features that may significantly enhance the robustness, efficiency and flexibility of the industrial production processes. However, most current approaches on automated quality control are based on rigid inspection methods and are not capable of accommodating to disturbances affecting the image acquisition quality, fact that hast direct consequences on the system´s reliability and performance. In an effort to address the problem, this paper presents the development of a self-adaptive software system designed for the pre-processing (quality enhancement) of digital images captured in industrial production lines. The approach introduces the use of scene recognition as a key-feature to allow the execution of customized image pre-processing strategies, increase the system's flexibility and enable self-adapting conducts. Real images captured in a washing machines production line are presented to test and validate the system performance. Experimental results demonstrate significant image quality enhancements and a valuable reliability improvement of the automated quality control procedures.

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“…Figure 1 shows part of the MAS and a schematic overview of some of the various information that can be exchanged between the QCA, the product tracking agent (PTA) and the product agent (PA), which are associated to the product type and to the individual product item, respectively, the independent meta-agent (IMA), which is associated to the whole production line, and the other resource agents (RA), which are associated to each process/machinery; altogether, these agents comprise the MAS. 13 The innovations of the particular example discussed here over standard machine vision systems 14 consist in developing self-optimizing strategies that allow the QCS to embed the complex behaviors of an agent, 15 so as to become an autonomous node of a MAS operating in the manufacturing plant. The context and the specific problem addressed to demonstrate these concepts are reported in the section ''The Robotized QCS'', including the hardware of the robot vision system, camera and illuminator.…”

Section: Introductionmentioning

confidence: 99%

Self-Optimizing Robot Vision for Online Quality Control

2015

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An online quality control system based on robot vision designed as a quality control agent is presented. It is part of a multi-agent system conceived for control of a production line at factory level, integrating quality control with process control. The implementation of self-optimizing behaviors aimed to improve reliability and confidence level of the diagnosis in a quality control system is also explained. Focus is on integration of advanced state-of-the-art experimental techniques to a real-world industrial problem. These concepts are developed for a robot vision system in eye-in-hand configuration for in-line inspections, which allows the flexibility necessary to implement agent behaviors. The design of the quality control station is presented. The set of self-optimization strategies, which have been implemented, is outlined. They consist in (a) the spatial repositioning of the vision system so as to optimize the line of sight and allow imaging of parts, which may randomly be hidden by other components and (b) self-optimization in image acquisition and processing. This increases the level of confidence in the diagnostic output. Tests on the laboratory prototype show the performance improvement of the inspection and allow to discuss the potential of agent technologies for in-line quality control.

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Quality control agents for adaptive visual inspection in production lines

Cited by 14 publications

References 8 publications

Agent-Based Control of Production Systems—and Its Architectural Challenges

Agent-Based Control of Production Systems—and Its Architectural Challenges

Adaptive image pre-processing for quality control in production lines

Self-Optimizing Robot Vision for Online Quality Control

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