Robotics for control strategy optimization and simulation in automatic cigarette sorter

doi:10.1016/j.compeleceng.2022.107801

2022-04-01

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DOI：10.1016/j.compeleceng.2022.107801

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摘要

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In the world of advancement and rapid growth, new retail marketization and distribution require a substantial degree of versatility and resilience in the manufacturing plant to accommodate demand transitions. A Digital Matching Structure (DMS) is built such that real-time data from the framework for processing the physical goods can be aggregated and then translated into the digital context. A collaborative design approach is implemented in the DMS to automate stacked cigarette labelling and sorting. The periodic optimal decisions can be achieved from the joint optimization model by obtaining online details from the specific unit for the management network and then feeding them to the quasi-physical simulation platform in the DMS for validation of results. A sorting robot built-in DMS can be configured to catch and sort cigarettes on a conveyor belt to ensure high performance and eliminate the possibility of manual sorting. Digital Matching Structure - Sorting Robot (DMS-SR) is the solution recommended for the proposed integration. This article explores how finely packaged cigarettes are handled on the conveyor belt and how the packaging line works to understand and filter the product. The proposed integration method is assessed using appropriate mathematical models for validating its consistency and verifying the accuracy of sorting. Eventually, the proposed autonomous system parameters have been enhanced by tests on a real machine to boost the sorting performance. It was found that better sorting could be obtained when the conveyor speed of around 0.36 m/s. When there is sufficient load, growing the momentum and shortening the declaration/accelerationtime will produce the optimum efficacy. The DMS-SR currently has a sorting output of about 1900 cigarettes per hour, highly precise.

Keyword:

Digital matching structure

Sorting robot

Joint optimization model