Novel Framework for Quality Control in Vibration Monitoring of CNC Machining

Area of Science:

• Manufacturing Engineering
• Artificial Intelligence
• Materials Science

Background:

• Spindle vibration in machining significantly degrades workpiece surface quality, leading to costly manual finishing and high scrap rates.
• Conventional solutions often treat vibration suppression and quality control as separate processes, limiting overall efficiency.
• Existing data-driven approaches struggle with adaptability across diverse CNC machine configurations.

Purpose of the Study:

• To develop a novel framework integrating advanced vibration monitoring with AI-driven quality prediction for machining processes.
• To enhance workpiece quality, productivity, and operational efficiency by addressing vibration and quality control concurrently.
• To create a generalized methodology for condition monitoring and quality control applicable to various CNC machines.

Main Methods:

• Implementation of advanced vibration-monitoring techniques to capture real-time machine dynamics.
• Development of an AI-driven model for predicting workpiece quality indicators based on vibration data.
• Integration of monitoring and prediction into a unified framework for proactive process control.

Main Results:

• Significant reduction in rejected parts, rework time, and manual finishing costs.
• Demonstrated improvement in overall process quality, productivity, and efficiency.
• Successful application of a generalized methodology across different CNC machine setups.

Conclusions:

• The integrated AI framework effectively mitigates the negative impacts of spindle vibration on machining quality.
• This approach offers a more holistic and adaptable solution compared to conventional, separate methods.
• The generalized methodology enhances the applicability of advanced monitoring and control in diverse manufacturing environments.