Web-Based Human-Machine Interface for CNC Systems and LSTM-Based Security Enhancements

With the ongoing digital transformation of industry, web-based human-machine interfaces (HMIs) are gaining increasing importance. In motion control systems, HMIs play a vital role in facilitating user interaction for monitoring and control. Recent advancements in web technologies have spurred interest in adopting modern web-based interfaces for motion control systems, as they not only enhance user experience but also facilitate the integration of advanced technologies such as machine learning. This paper presents a case study on the design and implementation of a web-based HMI for CNC (computer numerical control) and proposes machine learning-based security enhancements. The system is developed using Python and Flask for the backend service, and Node.js with Vue.js for the frontend client interface. LinuxCNC, an open-source motion control software, serves as the control engine, while communication is established via WebSocket and RESTful APIs to exchange command and status. Experimental evaluation using industrial motor drives and controllers demonstrates that the proposed system offers comparable performance. Compared with the legacy native HMI, while the web-based HMI exhibits slightly higher input latency due to network overhead, it significantly reduces output latency while improving responsiveness. To address security concerns, the paper introduces an AI-driven framework that integrates LSTM (long short-term memory) based anomaly detection and interaction biometrics for implicit user authentication. A customized design is proposed to ensure secure operation within web-based motion control environments.