Tool Wear Detection Using Machine Learning and AI

One of our recent projects was to apply novel computer vision techniques to come up with a scheme to detect tool wear during machining. Machining is an industrial manufacturing process that is applied to shape materials. Specialized materials are used as tools for machining. During the process, it is inevitable that the tools shall undergo wear and tear. Heat and abrasion are the usual causes of degradation.  Detecting tool wear in real-time is quite important for the process engineers.

In our approach we employed convolutional neural networks to train a model to predict wear and tear of machining tools. Our work was published in a very important journal related to manufacturing. The article can be found as follows.

Detection of tool wear during machining by designing a novel 12-way 2-shot learning model by applying L2-regularization and image augmentation – The International Journal of Advanced Manufacturing Technology

Tool wear monitoring is regarded as an incredibly important aspect of improving the surface integrity of machined components in the manufacturing sector. This research study performed operations using twelve different types of drilling and milling tools. The worn tools ranging from grade-1 to grade-5 were categorized based on tool wear severity by measuring the flank wear land width of each tool.

Imagine a world where your CNC machines warn you of a dying tool before it wrecks your expensive workpiece, grinds production to a halt, or sparks a safety incident. At OptimumT, we’re turning that vision into reality — today.

🔥 Meet Our Game-Changing 12-Way 2-Shot Learning Model 🔥

🔍 What It Does

• Instantly classifies tool health from “brand-new” all the way to “critically worn.”

• Works for both milling and drilling on high-performance alloys like Inconel 718.

• Tells you exactly when to replace or re-sharpen—saving hours of downtime and thousands in scrap costs.

🎯 Why It’s Revolutionary

• Only TWO images per wear-class needed. No massive data lakes.

• Smarter image augmentation jumps from 24 originals to 240 training samples—all without hefty manual labeling.

• Built on ResNet-18 to “see” force-signal spectrograms and scalograms like a seasoned machinist.

• L2 regularization ensures rock-solid accuracy (87.83% in rigorous tests) and zero overfitting.

🔧 How It Works in a Nutshell

1. 💥 Force Sensing: A dynamometer records real-time force signals as tools cut through tough metals.

2. 🖼️ Visual Transformation: We convert those raw signals into vibrant spectrograms and scalograms—think “sonar maps” of tool wear.

3. 🧠 Meta-Learning Magic: Our few-shot network compares a brand-new image against just two examples per wear level—then instantly knows if a tool is healthy or on its last breath.

4. 🚀 Actionable Insights: Alerts fire off to your CNC control system or maintenance dashboard, so you can swap that milling cutter before it gouges your part.

💪 Why Manufacturers Are Raving

• Zero retraining headaches. Deploy it on a forgotten production line; it learns on the spot.

• Ultra-low data requirements. Securely monitor every spindle without building giant data lakes.

• Real-time, shop-floor intelligence. No more guesswork—your team fixes only what needs fixing, when it needs fixing.

🔮 The Bottom Line
Our 12-way 2-shot learning model isn’t just another proof-of-concept. It’s a factory-floor game-changer that:

• Slashes unplanned downtime

• Drastically reduces scrap & rework

• Boosts throughput and quality

• Empowers maintenance teams with actionable alerts

Ready to supercharge your smart factory? Let’s make sure every tool in your arsenal works at peak performance—every single cut. 💥

👇 Reach out to see a live demo or talk integration—because in the world of precision machining, perfect timing is everything.

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Tool Wear Detection Using Machine Learning and AI by OptimumT is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.