Machine operators play an important role in monitoring machining performance and identifying behaviour anomalies of CNC machines. Experienced operators can infer the working status and performance based on machining sounds. The nature of machine sounds can be used to determine critical machining events such as collision, tool wear, crash, air-cutting and other behavioral anomalies, etc. With rapid development of IoT, data acquisition capabilities and intelligent acoustic signal processing technologies, there is a growing interest for the development of AI-driven self-awareness system for CNC machines. It will equip CNC machines with ears which can replace the role of machine operators to perform real-time monitoring of the machining job to identify anomalies. It will also play an important role in predictive maintenance and operational efficiency assessment for CNC machines, which is critical to the realization of smart manufacturing in the field of Industry 4.0.
In this project, the team will develop an AI-driven machine self-awareness system based on acoustic pattern recognition to automate human intuitive skills to the realization of real-time self-awareness of CNC machines. Critical machining events can then be detected in real-time, i.e. tool crash, tool break and normal cutting. SAS AI edge and toolboxes will be applied to build the pattern recognition neural network for machining events identification based on multi-dimensional acoustic patterns. Supervised machine-learning will be applied to train the pattern recognition neural network with a set of labelled machining pattern data. With the proposed system, CNC machines will be equipped with an intelligent engine with the ability to aware their real-time working conditions. These obtained awareness indicators are helpful in predictive maintenance, operational efficiency assessment and accident prevention of CNC machines.
| Index |
Name |
Role |
Organization |
| 1 |
Zhao Zhiqiang |
Leader |
Nanyang Polytechnic |
| 2 |
Zheng Xinhua |
Data Analyst |
Nanyang Polytechnic |
| 3 |
Wayne Wong Chee Weng |
Application specialist |
Nanyang Polytechnic |
