Worn tools might lead to substantial detrimental implications on the surface integrity of workpieces for precision/ultra-precision machining. Most previous research has heavily relied on singular information, which might not be appropriate enough to ascertain tool conditions and guarantee the accuracy of workpieces. This paper proposes a CNN-LSTM hybrid model directly utilizing tool images to predict surface roughness on machined parts for tool condition assessment. This work first performs pruning based on UNet3+ architecture to eliminate redundant structures while integrating attention mechanisms to enhance the model's focus on the target region. On this basis, tool wear region information is intensely mined and heterogeneous data is optimized using Spearman correlation analysis. Subsequently, we innovatively proposed a hybrid model that integrates CNN and RNN, endowing the model with the ability to process spatial and sequential information. The effectiveness of the proposed methodology is validated using the practical data obtained from cutting experiments. The results indicate that the proposed tool condition assessment methodology significantly improves the segmentation accuracy of the tool wear region to 94.52 % (Dice coefficient) and predicts the surface roughness of machined parts with an accuracy exceeding 93.1 % (R). It can be observed that the developed methodology may provide an effective solution for accurate tool condition assessment and the implementation of tool health management.

中文翻译：

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混合 CNN-LSTM 模型驱动的图像分割和粗糙度预测，用于利用异构数据评估工具状况


磨损的刀具可能会对精密/超精密加工工件的表面完整性产生重大不利影响。以前的大多数研究都严重依赖单一信息，这些信息可能不足以确定刀具状况并保证工件的精度。本文提出了一种直接利用刀具图像来预测加工零件表面粗糙度以进行刀具状态评估的 CNN-LSTM 混合模型。这项工作首先基于UNet3+架构进行剪枝，消除冗余结构，同时整合注意力机制，增强模型对目标区域的关注。在此基础上，深入挖掘刀具磨损区域信息，并利用Spearman相关分析对异构数据进行优化。随后，我们创新性地提出了一种融合CNN和RNN的混合模型，赋予模型处理空间和序列信息的能力。使用切割实验获得的实际数据验证了所提出方法的有效性。结果表明，所提出的刀具状态评估方法显着地将刀具磨损区域的分割精度提高到 94.52%（Dice 系数），并以超过 93.1%（R）的精度预测加工零件的表面粗糙度。可以看出，所开发的方法可以为准确的工具状态评估和工具健康管理的实施提供有效的解决方案。