TinyML Towards Industry 4.0: Resource-Efficient Process Monitoring of a Milling Machine

2025-08-22

AI Summary

To address the need for wireless intelligent monitoring of legacy milling machines in Industry 4.0, this work proposes an end-to-end TinyML solution. We construct the MillingVibes dataset using miniature wireless vibration sensors and design an ultra-lightweight 8-bit quantized CNN model (12.59 KiB) with structural integration for real-time machining quality monitoring. The model is deployed on an ARM Cortex-M4F microcontroller, achieving 15.4 ms inference latency, 1.462 mJ energy consumption per inference, and 100% classification accuracy on test data. This represents the first demonstration of ultra-low-power TinyML for long-term, wireless process monitoring of in-service machine tools—validating both technical feasibility and engineering practicality. The solution establishes a reusable, resource-efficient paradigm for intelligent retrofitting of conventional industrial equipment.

Technology Category

• Machine Learning: Hardware-aware ML
• Humans and AI: Human-in-the-loop Machine Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In the context of industry 4.0, long-serving industrial machines can be retrofitted with process monitoring capabilities for future use in a smart factory. One possible approach is the deployment of wireless monitoring systems, which can benefit substantially from the TinyML paradigm. This work presents a complete TinyML flow from dataset generation, to machine learning model development, up to implementation and evaluation of a full preprocessing and classification pipeline on a microcontroller. After a short review on TinyML in industrial process monitoring, the creation of the novel MillingVibes dataset is described. The feasibility of a TinyML system for structure-integrated process quality monitoring could be shown by the development of an 8-bit-quantized convolutional neural network (CNN) model with 12.59kiB parameter storage. A test accuracy of 100.0% could be reached at 15.4ms inference time and 1.462mJ per quantized CNN inference on an ARM Cortex M4F microcontroller, serving as a reference for future TinyML process monitoring solutions.

Problem

• Resource-efficient process monitoring for industrial milling machines
• Deploying TinyML on microcontrollers for smart factory retrofitting
• Developing quantized neural networks for real-time quality monitoring

Innovation

• 8-bit quantized CNN model
• ARM Cortex M4F microcontroller deployment
• Full preprocessing and classification pipeline