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Abstract:

Purpose – This paper aims to study a commercially available industrial part feeder that uses an industrial robot arm and computer vision system. Three conveyor belts are arranged to singulate and circulate parts, bringing them under a camera where their pose is recognized and subsequently manipulated by the robot arm. The problem is addressed of optimizing belt speeds and hence throughput of this feeder that avoid: starvation, where no parts are visible to the camera and saturation, where too many parts prevent part pose detection or grasping.

Design/methodology/approach – Models are developed for intermittent and continuous motion feeding based on a 2D Poisson process. Renewal theory is applied to model intermittent motion and an M/G/1 queue with customer impatience to model continuous motion feeding. These models are verified using discrete event simulation.

Findings – The models predict and optimize feeder behaviour very accurately and it is possible to compute optimal settings for different part sizes and throughput sensitivity.

Practical implications – Feeder belt velocities are currently estimated based on intuition and ad hoc trial and error. The results provide a scientific alternative. The models are straightforward to implement and can provide velocity settings for feeders in industrial use.

Originality/value – This paper advances the scientific understanding of automation and part feeding.

Keywords:

Parts, Queuing theory, Robotics, Throughput accounting

Article Type:

Technical paper

Article URL:

http://www.emeraldinsight.com/10.1108/01445150710733360

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