The application of deep learning methods in Real-Time tool wear monitoring in intelligent CNC machining systems
智能数控加工系统中深度学习方法在刀具磨损实时监测的应用

摘要

智能数控加工系统中，刀具磨损监测对保障加工质量和效率至关重要。传统监测方法存在精度低、实时性差等局限，难以满足现代高效加工需求。深度学习技术凭借强大的特征提取和模式识别能力，为刀具磨损监测提供了新思路。通过构建深度学习模型，结合多源数据训练与验证，可实现高精度实时监测，显著提升加工过程的智能化水平。未来，随着技术的不断发展，深度学习在刀具磨损监测中的应用将更加广泛，有望进一步推动制造业的智能化转型。

Abstract

In intelligent CNC machining systems, tool wear monitoring is crucial for ensuring machining quality and efficiency. Traditional monitoring methods suffer from limitations such as low accuracy and poor real-time performance, which fail to meet the demands of modern high-efficiency machining. Deep learning technology, with its powerful feature extraction and pattern recognition capabilities, offers new approaches for tool wear monitoring. By constructing deep learning models and validating them with multi-source data training, high-precision real-time monitoring can be achieved, significantly enhancing the intelligence level of the machining process. In the future, as technology continues to advance, the application of deep learning in tool wear monitoring will become more widespread, and is expected to further promote the intelligent transformation of the manufacturing industry.