面向可重构制造系统的云集成策略

Bo Guo, Fu-Shin Lee, Chen-I Lin, Yun-qing Lu
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引用次数: 7

摘要

如今的制造业需要迅速重新配置生产线,以适应快速变化的市场。同时,进行系统重构还需要管理涉及的众多类型的制造设备。然而,由独家供应商提供的传统不兼容制造系统通常会增加制造成本并延长开发时间。本文提出了一种新的RMS框架,该框架旨在实现Redis主/从服务器机制,通过开发配置代码集成各种CNC制造设备、硬件控制手段和数据交换协议。在RMS框架中,每个制造设备或配件代表一个对象,识别的CNC控制面板图像特征、相关设备调谐参数、通信格式、操作程序和控制api等信息存储在Redis主云服务器数据库中。通过实现机器视觉技术获取数控控制器面板图像,一旦识别出嵌入的图像特征,系统就能有效地识别出数控加工的瞬时状态和响应信息。当需要对制造资源进行系统重新配置时,系统从Redis本地客户端服务器发出命令,检索存储在Redis主云服务器中的信息,在主云服务器中,注册的数控机床、机器人和内置配件的资源得到安全维护。然后,系统利用收集到的本地信息重新配置相关的制造资源并立即开始生产,从而能够在模拟市场中快速响应快速修改的订单。在原型RMS架构中,该方法利用使用不变图像特征提取算法获得的识别反馈视觉信息,有效地命令工业机器人在CNC控制面板上完成所需的动作,就像普通操作员每天在数控机床前进行制造一样。
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A cloud integrated strategy for reconfigurable manufacturing systems
Manufacturing industries nowadays need to reconfigure their production lines promptly as to acclimate to rapid changing markets. Meanwhile, exercising system reconfigurations also needs to manage innumerous types of manufacturing apparatus involved. Nevertheless, traditional incompatible manufacturing systems delivered by exclusive vendors usually increase manufacture costs and prolong development time. This paper presents a novel RMS framework, which is intended to implement a Redis master/slave server mechanism to integrate various CNC manufacturing apparatus, hardware control means, and data exchange protocols through developed configurating codes. In the RMS framework each manufacturing apparatus or accessory stands for an object, and information of recognized CNC control panel image features, associated apparatus tuned parameters, communication formats, operation procedures, and control APIs, are stored into the Redis master cloud server database. Through implementation of machine vision techniques to acquire CNC controller panel images, the system effectively identifies instantaneous CNC machining states and response messages once the embedded image features are recognized. Upon demanding system reconfigurations for the manufacturing resources, the system issues commands from Redis local client servers to retrieve the stored information in the Redis master cloud servers, in which the resources for registered CNC machines, robots, and built-in accessories are maintained securely. The system then exploits the collected information locally to reconfigure involved manufacturing resources and starts manufacturing immediately, and thus is capable to promptly response to fast revised orders in a comitative market. In a prototyped RMS architecture, the proposed approach takes advantage of recognized feedback visual information, which is obtained using an invariant image feature extraction algorithm, and effectively commands an industrial robot to accomplish demanded actions on a CNC control panel, as a regular operator does daily in front of the CNC machine for manufacturing.
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