Security Risks in Machining Process Monitoring: Sequence-to-Sequence Learning for Reconstruction of CNC Axis Positions

2026-03-02

AI Summary

This study addresses the security risks associated with reconstructing CNC machine tool axis positions from accelerometer signals, a task where conventional double integration methods struggle to accurately recover trajectories under noisy conditions and complex operational scenarios. To overcome these limitations, this work proposes a novel sequence-to-sequence learning model based on Long Short-Term Memory (LSTM) networks that directly reconstructs high-precision axis and tool positions from broadband acceleration signals collected during industrial machining processes. The proposed approach significantly outperforms traditional methods, reducing reconstruction errors by 98% in low-complexity motions and by 85% in complex machining sequences, while preserving critical geometric features of the toolpath. These results demonstrate the feasibility of inferring sensitive positional information from readily available condition monitoring data, thereby revealing a previously underappreciated security vulnerability in industrial control systems.

Technology Category

• Machine Learning: Learning with Manifolds
• Natural Language Processing: Safety and Robustness
• Intelligent Robots: State Estimation

Application Category

• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Accelerometer-based process monitoring is widely deployed in modern machining systems. When mounted on moving machine components, such sensors implicitly capture kinematic information related to machine motion and tool trajectories. If this information can be reconstructed, condition monitoring data constitutes a severe security threat, particularly for retrofitted or weakly protected sensor systems. Classical signal processing approaches are infeasible for position reconstruction from broadband accelerometer signals due to sensor- and process-specific non-idealities, like noise or sensor placement effects. In this work, we demonstrate that sequence-to-sequence machine learning models can overcome these non-idealities and enable reconstruction of CNC axis and tool positions. Our approach employs LSTM-based sequence-to-sequence models and is evaluated on an industrial milling dataset. We show that learning-based models reduce the reconstruction error by up to 98% for low complexity motion profiles and by up to 85% for complex machining sequences compared to double integration. Furthermore, key geometric characteristics of tool trajectories and workpiece-related motion features are preserved. To the best of our knowledge, this is the first study demonstrating learning-based CNC position reconstruction from industrial condition monitoring accelerometer data.

Problem

• Security Risks
• CNC Position Reconstruction
• Accelerometer-based Monitoring
• Machining Process Monitoring
• Kinematic Information Leakage

Innovation

• sequence-to-sequence learning
• CNC position reconstruction
• accelerometer-based monitoring
• LSTM
• machining security