Tool wear conditions impact the final quality of the workpiece. In this
study, we propose a deep learning approach based on a convolutional neural
network that incorporates cutting conditions as extra model inputs, aiming to
improve tool wear estimation accuracy and fulfill industrial demands for
zero-shot transferability. Through a series of milling experiments under
various cutting parameters, we evaluate the model's performance in terms of
tool wear estimation accuracy and its transferability to new fixed or variable
cutting parameters. The results consistently highlight our approach's advantage
over conventional models that omit cutting conditions, maintaining superior
performance irrespective of the stability of the wear development or the
limitation of the training dataset. This finding underscores its potential
applicability in industrial scenarios.

通过在卷积神经网络中引入切削条件作为额外的模型输入，本研究提出了一种基于深度学习的方法，旨在提高刀具磨损估计的准确性，并满足工业对零射击传递能力的需求，通过一系列铣削实验评估模型的性能，结果一致强调我们方法的优势，无论磨损发展的稳定性还是训练数据集的限制如何，其性能都优于省略切削条件的传统模型，这一发现凸显了其在工业场景中的潜在应用性。