Tactile sensor transforms tool wear detection in machining

Researchers at the Department of Energy’s Oak Ridge National Laboratory (ORNL) have developed a tool condition monitoring system that can detect the wear of cutting tools used in machining. On-machine feedback helps manufacturers avoid premature tool wear to cut downtime.

Cutting tool performance is essential for machining productivity and part quality, as machining, the precise removal of material to shape parts, depends heavily on tool condition. Machining gradually wears down cutting tools, leading to process instability that causes diminished part quality and potential tool failure. 

To avoid these issues, manufacturers often replace cutting tools early, resulting in underused tooling, more frequent tool changes and higher costs. 

Current methods for tracking tool wear fall short because of unpredictable sensor noise and inconsistent imaging conditions. To address these challenges, ORNL researchers developed a novel monitoring system that delivers rapid, accurate wear measurements. The work is described in the journal Wear.

“This innovation bridges the gap between accurate sensing and decision-making on the factory floor,” said ORNL’s Ritin Mathews, lead researcher on the project. “It gives manufacturers a clear picture of tool health, so they can plan operations with confidence and improve efficiency.”

Tactile sensing delivers consistent on-machine tool wear insights

The team’s innovative monitoring system uses a flexible tactile sensor to capture detailed images of a tool’s cutting edge by pressing against it. As the sensor conforms to the tool’s shape, it records the fine surface details of the cutting edge. 

Advanced computer algorithms then analyze the images in real time to detect even subtle signs of wear or damage. These insights enable manufacturers to schedule replacement at the optimal time. The result is a cost-effective solution that enhances performance and product reliability by eliminating reliance on outdated estimation methods.

“By integrating this tactile sensor, manufacturers can reduce disruptions caused by tool failures and minimize labor costs tied to corrective maintenance,” Mathews said.

The breakthrough in manufacturing efficiency also offers a way for industry to reduce production costs on high-value components made from costly, difficult-to-machine materials, such as titanium alloys and nickel-based superalloys.

Compared to traditional tool condition monitoring systems, this method provides multiple advantages in tool wear detection. The sensor used in this approach is faster, taking only seconds, making it ideal for automated, real-time monitoring. It delivers 98% accuracy and consistent results that are unaffected by lighting conditions. The sensor is also relatively inexpensive and easy to integrate into existing manufacturing processes.

“This system delivers proactive analysis of tool wear progression, enabling manufacturers to detect and address potential issues before they compromise the machining of critical components,” said Chris Tyler, ORNL’s group leader for Advanced Machining and Machine Tools Research.

Additional ORNL researchers who contributed to this project include Greg Corson, Josh Harbin and Scott Smith.

This project is supported by the U.S. Department of War’s Industrial Base Analysis and Sustainment Program. The Manufacturing Demonstration Facility, where the research was conducted, is supported by DOE’s Advanced Materials and Manufacturing Technologies Office and acts as a nationwide consortium of collaborators focused on innovating, inspiring and catalyzing the transformation of U.S. manufacturing. 

UT-Battelle manages ORNL for DOE’s Office of Science, the single largest supporter of basic research in the physical sciences in the United States. The Office of Science is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science. — Tina M. Johnson 

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