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 transformer-inspired 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 two machines and 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 99.1\% accuracy in identifying tool wear using data from only four milling tools operated until they are worn.

翻译：精确的刀具磨损预测对于维持加工生产率和最小化成本至关重要。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。本研究探索了数据驱动方法，特别是深度学习，用于刀具磨损预测。传统的数据驱动方法通常专注于单一过程，依赖多传感器设置和大量数据生成，这限制了其对新场景的泛化能力。此外，多传感器集成在工业环境中往往不切实际。为解决这些局限性，本研究调查了预测模型在最小训练数据下的可迁移性，并在两个加工过程中进行了验证。此外，研究采用仅配备单个加速度传感器的简单设置，建立了一种低成本数据生成方法，该方法通过迁移学习促进了模型向其他过程的泛化。研究评估了多种机器学习模型，包括受Transformer启发的卷积神经网络（CNN）、长短期记忆网络（LSTM）、支持向量机（SVM）和决策树，这些模型在不同输入格式（如特征向量和短时傅里叶变换（STFT））上进行训练。模型性能在两台机床上以及不同数量的训练数据（包括数据集显著减少的场景）上进行评估，从而揭示了其在数据受限条件下的有效性。结果表明，特定模型和配置在有效预测刀具磨损方面具有潜力，有助于开发更具适应性和高效的加工预测性维护策略。值得注意的是，ConvNeXt模型表现卓越，仅使用四把运行至磨损的铣刀数据，在识别刀具磨损方面达到了99.1%的准确率。