Sensor Technology in Assembly Systems

Santochi, Marco; Dini, Gino

doi:10.1016/s0007-8506(07)63239-9

Cited by 29 publications

(9 citation statements)

References 85 publications

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“…The requirement for objects recognition system is met in various modern applications, where diverse objects of variable shapes and sizes ought to be dealt with. Point by point audits on the most recent sensor innovations and the exploration patterns for get together frameworks have demonstrated that vision systems are reasonable for objects recognition, review and robot taking care of utilisations (Santochi and Dini, 1998;Janocha and Seidel, 1988;Michalos et al, 2012;Vahrenkamp et al, 2009;Han et al, 2000Han et al, , 2001. Investigation has been done in the plan parts of the machine vision frameworks for modern applications (Golnabi and Asadpour, 2007), and has prompted to enhancements in dependability and item quality (Kodagali and Balaji, 2012;Beserra Gomes et al, 2013;Xie et al, 2008).…”

Section: Cad Parts Rendering Using Stlmentioning

confidence: 99%

Robust active vision industrial CAD parts recognition system

Jain

Meenu

Sardana

2018

IJIMR

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In automated assembly systems the machine parts identification is entirely different from simple object recognition; moreover the ability of humans to differentiate between correct and not correct machine parts is better but it is a difficult task for a machine. In general, with fast moving machine parts on the conveyor manual defect detection by human inspectors is impractical. Also it is expensive, inaccurate, subjective, eye straining and causes other health issues to quality control inspectors. A computer vision-based non-contact inspection technique is developed with image processing methods by considering these problems, for defect detection in industrial machine parts. The present work will help the industrial robot used in assembly process and industrial inspection systems. In this paper features-based industrial object detection techniques are implemented in MATLAB to recognise the presence of the industrial CAD parts in the query image. In the end the actual industrial tool images are also used to show the accuracy and robustness of the proposed machine vision system for industrial manufacturing automation.

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Section: Cad Parts Rendering Using Stlmentioning

confidence: 99%

Robust active vision industrial CAD parts recognition system

Jain

Meenu

Sardana

2018

IJIMR

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“…There exist numerous factors that influence the accuracy of vision algorithms, including the measured object characteristics (size, shape, color, texture), camera characteristics (camera resolution, quality of lenses), environment characteristics (pose, illumination) [2], [7]. In addition, the way the vision algorithms is developed plays a significant role for the quality of measurement.…”

Section: A Industrial Visionmentioning

confidence: 99%

Discrete event dataflow as a formal approach to specification of industrial vision systems

Semeniuta¹,

Falkman

2015

2015 IEEE International Conference on Automation Science and Engineering (CASE)

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Abstract-The need for more flexible manufacturing systems stimulates the adoption of industrial robots in combination with intelligent computing resources and sophisticated sensing technologies. In this context, industrial vision systems play a role of inherently flexible sensing means that can be used for a variety of tasks within automated inspection, process control and robot guidance. When vision sensing is used within a large complex system, it is of particular importance to handle the complexity by introducing the appropriate formal methods. This paper overviews the challenges arising during design, implementation and application of industrial vision systems, and proposes an approach, dubbed Discrete Event Dataflow (DEDF), allowing to formally specify vision dataflow in the context of larger systems.

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“…Sensor technology in assembly systems is usually used on component and machine level to realise, e.g., dexterous grippers and autonomous robots. In general, various types of sensors are used for different automated assembly operations [72]. Whereas existing sensors will be improved and new types of sensors will be developed, autonomous assembly additionally requires sensor integration, sensor fusion and artificial intelligence techniques such as artificial neural networks to process the sensor data.…”

Section: Control Systemsmentioning

confidence: 99%

“…Whereas existing sensors will be improved and new types of sensors will be developed, autonomous assembly additionally requires sensor integration, sensor fusion and artificial intelligence techniques such as artificial neural networks to process the sensor data. Furthermore, the integration of sensors within the control units of robots is required to replace human sensorial capabilities by machines [72]. Sensors on system level such as the traditional production data acquisition used for monitoring and control of assembly processes are outside the control loops of cells and machines [10].…”

Section: Control Systemsmentioning

confidence: 99%

Autonomous Processes in Assembly Systems

2007

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Today's assembly systems have to be flexible to adapt quickly to an increasing number and variety of products and changing demand volume. To manage these dynamics, flexible, reconfigurable, and autonomous assembly systems were proposed and partly realised in the last two decades. The flexibility and adaptability is realised by clustering the assembly system into subsystems and modules which get a certain degree of autonomy and control themselves in a decentralised way. This keynote paper will present the general principles of autonomy and the proposed concepts, methods and technologies to realise autonomous processes in assembly systems. Different approaches for design and autonomous operation of assembly will be explained and future trends towards fully autonomous components of an assembly system as well as autonomous parts and products will be discussed.

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Sensor Technology in Assembly Systems

Cited by 29 publications

References 85 publications

Robust active vision industrial CAD parts recognition system

Robust active vision industrial CAD parts recognition system

Discrete event dataflow as a formal approach to specification of industrial vision systems

Autonomous Processes in Assembly Systems

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