Breakthrough in Quality Control: AI-Powered Real-Time Monitoring of Surface Roughness and Tool Wear in CNC Treatment
In today’s manufacturing industry, where quality and efficiency are paramount, quality monitoring has become a critical link in the production process. The research team led by Professor Wenwen has recently made a significant breakthrough in quality control of CNC treatment, achieving synchronous monitoring of surface roughness and tool wear.
The Importance of Surface Roughness and Tool Wear
Surface roughness has a direct impact on the quality of the finished product, affecting not only its appearance but also its performance, such as wear resistance and sealing. Tool wear, on the other hand, can cause inaccurate treatment dimensions and damage to the integrity of parts, resulting in production stagnation and significant economic losses. Therefore, effective monitoring methods are crucial for optimizing manufacturing processes.
Advanced AI Technology in Quality Control
To address this challenge, the research team has developed an innovative AI-powered real-time monitoring system that combines advanced machine learning algorithms with sensor data collected during the CNC milling process. The system is designed to collect data on vibration, current, and cutting conditions, as well as surface roughness and tool wear, with a sampling frequency of 20 kHz.
Experimental Results: BestTLS Outperforms Traditional Methods
The new system, called BestTLS (Best Task Learning System), was tested in a controlled experiment using a VMC850B vertical machining center, with a milling depth of 1.2 mm, a molding width of 10 mm, and a cutting speed of 3800 RPM. A total of 816 tool paths were conducted, with 63 valid sample data groups obtained. The results showed that BestTLS achieved an average percentage error (MAPE) of only 5.75% in surface roughness prediction and 100% accuracy in tool wear monitoring, outperforming traditional methods such as BTTLS (Generalized Double Task Learning System) and FBTTLS (Fuzzy Generalized Double Task Learning System).
Advantages of BestTLS
The BestTLS system offers several advantages over traditional methods, including:
- Improved prediction accuracy: With a MAPE of 5.75%, BestTLS outperforms BTTLS and FBTTLS, indicating a more accurate prediction of surface roughness.
- Real-time monitoring: BestTLS enables real-time monitoring of surface roughness and tool wear, allowing for prompt intervention and adjustments.
- Adaptability: The system’s dynamically adaptive reservoir enables it to grasp the unique characteristics of each monitoring task, improving its performance.
Conclusion and Future Directions
The development of BestTLS marks a significant breakthrough in quality control during the CNC treatment process. This AI-powered system has the potential to revolutionize manufacturing by:
- Enhancing product quality
- Reducing production costs
- Shortening production time
- Increasing efficiency
As the manufacturing industry continues to evolve, the adoption of advanced AI technologies like BestTLS is essential for meeting the demands of quality, efficiency, and innovation. Further research and development are needed to explore the full potential of this technology and its applications in various manufacturing processes.
