2019
DOI: 10.1007/s00170-019-03798-9
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Reinforcement learning–based design of orienting devices for vibratory bowl feeders

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Cited by 10 publications
(3 citation statements)
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References 27 publications
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“…In general, the application papers presented real case studies that best represent the theoretical models and enrich the methodological description. For example, a physics simulation was proposed by Stocker et al (2019) [38] to train the operators and avoid mistakes at multiple positions and measure the subsequent configuration efficiency. A case study in the field of industrial manufacturing was also presented.…”
Section: Application Papersmentioning
confidence: 99%
“…In general, the application papers presented real case studies that best represent the theoretical models and enrich the methodological description. For example, a physics simulation was proposed by Stocker et al (2019) [38] to train the operators and avoid mistakes at multiple positions and measure the subsequent configuration efficiency. A case study in the field of industrial manufacturing was also presented.…”
Section: Application Papersmentioning
confidence: 99%
“…Vibration generators are widely employed in manufacturing plants to exert vibrations with prescribed frequency, amplitude, and spatial shape, as required by the process. Meaningful examples are vibratory feeders, often adopted to convey small parts in manufacturing plants, packaging lines, or flexible assembly cells [1][2][3][4] by exploiting the forced vibrations of a surface (named tray or trough) where the conveyed objects are placed. The vibration frequency should be consistent with the features of the conveyed objects [5,6].…”
Section: Motivationsmentioning
confidence: 99%
“…Stocker et al [55] outlines a method which modifies this conventional process using reinforcement learning to automate the vibratory bowl feeders design. To enable this, a software agent is used to model the placement of traps on multiple positions and measure the subsequent configuration efficiency.…”
Section: Editorial Overview Of This Special Issue Papers and Their Comentioning
confidence: 99%