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An optimization strategy for HMI panel recognition of CNC machines using a CNN deep-learning network
Concurrent Engineering ( IF 2.118 ) Pub Date : 2021-03-08 , DOI: 10.1177/1063293x21998083
Bo Guo 1 , Fu-Shin Lee 2 , Chen-I Lin 1 , Yuan-Jun Lin 1
Affiliation  

This paper suggests an optimization strategy to train a CNN deep-learning network, which successfully recognizing working status on the HMI panels of CNC machines. To verify the developed strategy, the research experiments using a prototype that consists of a CNC milling machine and an industrial robot. In the optimization strategy, the research first defines a length-varying hyperparameter list for the deep-learning network, and the entities in the list adjust themselves to optimize the model scales. During the optimization process, this paper adopts a two-stage training scheme that gradually augments image datasets to improve HMI control-panel recognition performances, such as recognition accuracy and recognition speed to identify the CNC machine working status. Using an open-source PyTorch platform, this research establishes a cloud-based distributed architecture to build training codes for the deep-learning network, in which an applicable optimization model is deployed to recognize the CNC control-panel working status. The optimization strategy employs minimal codes to rebuild the architecture and the least efforts to reform the manufacturing system. The optimally trained model provides up to a 99.34% CNC panel-message recognition accuracy and a high-speed recognition of 100 images in 0.6 s. Moreover, the developed optimization strategy enables the prediction of necessitated dataset augmentation to training a practically implemented CNN network.



中文翻译:

使用CNN深度学习网络的CNC机床HMI面板识别的优化策略

本文提出了一种训练CNN深度学习网络的优化策略,该网络可以成功识别CNC机床的HMI面板上的工作状态。为了验证所制定的策略,研究实验使用了由CNC铣床和工业机器人组成的原型。在优化策略中,研究首先为深度学习网络定义了一个长度可变的超参数列表,列表中的实体会自行调整以优化模型规模。在优化过程中,本文采用两阶段训练方案,逐步扩充图像数据集,以提高人机界面控制面板的识别性能,如识别精度和识别速度,以识别数控机床的工作状态。使用开源PyTorch平台,这项研究建立了一个基于云的分布式体系结构,用于为深度学习网络构建培训代码,其中部署了适用的优化模型以识别CNC控制面板的工作状态。优化策略采用最少的代码来重建体系结构,并以最少的努力来改造制造系统。经过最佳训练的模型可提供高达99.34%的CNC面板消息识别精度,并能在0.6 s内高速识别100张图像。此外,开发的优化策略可以预测必要的数据集扩充,以训练实际实施的CNN网络。优化策略采用最少的代码来重建体系结构,并以最少的努力来改造制造系统。经过最佳训练的模型可提供高达99.34%的CNC面板消息识别精度,并能在0.6 s内高速识别100张图像。此外,开发的优化策略可以预测必要的数据集扩充,以训练实际实施的CNN网络。优化策略采用最少的代码来重建体系结构,并以最少的努力来改造制造系统。经过最佳训练的模型可提供高达99.34%的CNC面板消息识别精度,并能在0.6 s内高速识别100张图像。此外,开发的优化策略可以预测必要的数据集扩充,以训练实际实施的CNN网络。

更新日期:2021-03-09
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