Abstract
Accurate tool wear prediction is essential for maintaining productivity and minimizing costs in machining. However, the complex nature of the tool wear process poses significant challenges to achieving reliable predictions. This study explores data-driven methods, in particular deep learning, for tool wear prediction. Traditional data-driven approaches often focus on a single process, relying on multi-sensor setups and extensive data generation, which limits generalization to new settings. Moreover, multi-sensor integration is often impractical in industrial environments. To address these limitations, this research investigates the transferability of predictive models using minimal training data, validated across two processes. Furthermore, it uses a simple setup with a single acceleration sensor to establish a low-cost data generation approach that facilitates the generalization of models to other processes via transfer learning. The study evaluates several machine learning models, including convolutional neural networks (CNN), long short-term memory networks (LSTM), support vector machines (SVM) and decision trees, trained on different input formats such as feature vectors and short-time Fourier transform (STFT). The performance of the models is evaluated on different amounts of training data, including scenarios with significantly reduced datasets, providing insight into their effectiveness under constrained data conditions. The results demonstrate the potential of specific models and configurations for effective tool wear prediction, contributing to the development of more adaptable and efficient predictive maintenance strategies in machining. Notably, the ConvNeXt model has an exceptional performance, achieving an 99.1% accuracy in identifying tool wear using data from only four milling tools operated until they are worn.
Abstract (translated)
准确的刀具磨损预测对于保持生产效率和降低成本至关重要。然而，刀具磨损过程的复杂性给实现可靠预测带来了重大挑战。本研究探索了基于数据驱动的方法，尤其是深度学习技术，在刀具磨损预测中的应用。传统数据驱动方法通常集中于单一加工过程，并依赖多传感器设置及大量数据生成，这限制了其在新环境下的泛化能力。此外，多传感器整合在工业环境中往往难以实现。为解决这些局限性，本研究探讨了使用少量训练数据的预测模型可移植性的验证，并通过两个不同加工过程进行了测试。同时，该研究采用了一种简单的单加速度传感器设置来建立低成本的数据生成方法，通过迁移学习促进模型向其他加工过程泛化。 在研究中，评估了几种机器学习模型的表现，包括卷积神经网络（CNN）、长短时记忆网络（LSTM）、支持向量机（SVM）和决策树，它们分别基于不同的输入格式训练，如特征向量和短时间傅里叶变换（STFT）。通过不同规模的训练数据集对这些模型进行了评估，其中包括大量减少的数据集场景，从而展示了其在受限数据条件下的有效性。研究结果表明了某些特定模型及其配置对于有效刀具磨损预测具有潜力，并促进了更灵活高效的预测性维护策略的发展。 值得注意的是，ConvNeXt模型表现出色，在仅使用四把铣刀操作直到完全磨损产生的少量数据的情况下，达到了99.1%的准确性，成功地识别出了刀具磨损。
URL
https://arxiv.org/abs/2412.19950
