Tool Wear Prediction When Machining with Self-Propelled Rotary Tools

Area of Science:

• Materials Science
• Mechanical Engineering
• Manufacturing Processes

Background:

• Traditional tool wear models exist for fixed cutting tools.
• Predictive models for self-propelled rotary cutting tools are lacking in scientific literature.
• Understanding tool wear is crucial for optimizing machining operations.

Purpose of the Study:

• To develop and validate a flank wear model for self-propelled rotary carbide tools.
• To investigate the tool wear progression during the cutting of hardened steel.
• To address the absence of predictive models for rotary tool wear.

Main Methods:

• Developed a flank wear model using work-tool geometry and empirical functions.
• Conducted cutting experiments on AISI 4340 steel (54-56 HRC).
• Utilized genetic programming to determine model constants and measured wear rates over time.

Main Results:

• The proposed flank wear model demonstrated competence in estimating wear progression.
• Experimental data validated the model's predictions for rotary tool flank wear.
• The model effectively captures the relationship between cutting parameters and tool wear.

Conclusions:

• The developed model successfully predicts flank wear in self-propelled rotary tools.
• This research fills a critical gap in the understanding of rotary tool wear.
• The findings can aid in optimizing machining strategies for hardened steels.