Hakam Nisar, Benfriha Khaled, Meyrueis Vincent, Liotard Cyril
Arts et Métiers, Institute of Technology (AMIT), 75013 Paris, France.
ERM Automatismes, 84200 Carpentras, France.
Sensors (Basel). 2024 Jun 29;24(13):4239. doi: 10.3390/s24134239.
The digitization of production systems has revolutionized industrial monitoring. Analyzing real-time bottom-up data enables the dynamic monitoring of industrial processes. Data are collected in various types, like video frames and time signals. This article focuses on leveraging images from a vision system to monitor the manufacturing process on a computer numerical control (CNC) lathe machine. We propose a method for designing and integrating these video modules on the edge of a production line. This approach detects the presence of raw parts, measures process parameters, assesses tool status, and checks roughness in real time using image processing techniques. The efficiency is evaluated by checking the deployment, the accuracy, the responsiveness, and the limitations. Finally, a perspective is offered to use the metadata off the edge in a more complex artificial-intelligence (AI) method for predictive maintenance.
生产系统的数字化彻底改变了工业监测。分析实时的自下而上的数据能够对工业过程进行动态监测。数据以各种类型收集，如视频帧和时间信号。本文着重于利用视觉系统的图像来监测计算机数控（CNC）车床的制造过程。我们提出了一种在生产线边缘设计和集成这些视频模块的方法。这种方法利用图像处理技术实时检测原材料部件的存在、测量过程参数、评估刀具状态并检查粗糙度。通过检查部署、准确性、响应性和局限性来评估效率。最后，提供了一个视角，即在更复杂的人工智能（AI）预测性维护方法中使用边缘元数据。
