Summary of Data-driven Tool Wear Prediction in Milling, Based on a Process-integrated Single-sensor Approach, by Eric Hirsch and Christian Friedrich
Data-driven tool wear prediction in milling, based on a process-integrated single-sensor approach
by Eric Hirsch, Christian Friedrich
First submitted to arxiv on: 27 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores data-driven methods for accurate tool wear prediction in machining. It investigates deep learning models, such as transformer-inspired convolutional neural networks (CNN), long short-term memory networks (LSTM), support vector machines (SVM), and decision trees, to predict tool wear using minimal training data and a single acceleration sensor. The study evaluates the performance of these models on two machines with varying amounts of training data, including scenarios with significantly reduced datasets. The results show that specific models and configurations can effectively predict tool wear, contributing to the development of more adaptable and efficient predictive maintenance strategies in machining. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make tool wear prediction better by using special kinds of artificial intelligence called machine learning. They test different ways of doing this with minimal data from a single sensor, which is important because it’s not always possible or practical to get lots of data in real-world situations. The results show that some models work really well and can accurately predict when tools are worn out. This could help make manufacturing more efficient and cost-effective. |
Keywords
» Artificial intelligence » Cnn » Deep learning » Lstm » Machine learning » Transformer
