Decoding Multidimensional Machining Loads: iKIT Wireless Extrasensory Toolholder and Parametric Analysis in Aluminum...

Area of Science:

• Manufacturing Engineering
• Sensor Technology
• Mechanical Engineering

Background:

• Smart manufacturing relies on real-time monitoring of forces in computer numerical control (CNC) machining.
• Existing methods often lack the fidelity or in-situ capabilities required for complex machining environments.

Purpose of the Study:

• To introduce an innovative wireless extrasensory toolholder (iKIT) for high-fidelity, in-situ sensing.
• To monitor cutting force, torque, and bending moments in real-time during CNC machining.
• To validate the system's performance and data accuracy for advanced manufacturing applications.

Main Methods:

• Designed and implemented a wireless toolholder system with miniature sensors.
• Utilized a high-bandwidth wireless transmission and noncontact power supply.
• Conducted comprehensive milling tests on aluminum alloys, varying process parameters.

Main Results:

• The iKIT system accurately detected changes in multidimensional mechanical loads due to process parameter variations.
• Torque and bending moments showed strong positive correlations with feed rate and depth of cut.
• These loads were negatively correlated with spindle speed, indicating thermal effects.

Conclusions:

• The developed smart toolholder provides a reliable hardware framework for integrated sensing in machining.
• The system delivers accurate data crucial for supporting digital twin models and adaptive control strategies.
• This technology enhances real-time process monitoring and optimization in smart manufacturing.