Cutting force detection can contribute to predicting the productivity and quality of end milling operations. Instantaneous cutting force prediction of digital twins in end milling operations should be near real-time and accurate. This paper proposes an image-based approach that can contain more useful information due to a higher dimension and simplify the complexity of computing geometric data. The cutter frame image (CFI) is utilized as one of the inputs of a convolutional neural network (CNN) to predict instantaneous cutting forces. Considering the convenience of capturing massive data, the approach uses cutting forces generated from a mechanistic force model instead of experimental cutting forces to train the CNN. The correlation coefficient R² value between predicted results and simulated results is 0.9999 and the average time cost per image is 0.057 s in a cutting condition, which validates the possibility to use the image-based method to predict instantaneous cutting forces accurately and efficiently in the digital twin.

切削力检测可以有助于预测立铣刀的生产率和质量。立铣操作中数字孪晶的瞬时切削力预测应接近实时且准确。本文提出了一种基于图像的方法，该方法由于具有较高的维数而可以包含更多有用的信息，并且可以简化计算几何数据的复杂性。刀具框架图像（CFI）被用作卷积神经网络（CNN）的输入之一，以预测瞬时切削力。考虑到捕获海量数据的便利性，该方法使用从机械力模型生成的切削力而不是实验切削力来训练CNN。相关系数R ² 预测结果与模拟结果之间的数值为0.9999，并且在切割条件下每张图像的平均时间成本为0.057 s，这证明了使用基于图像的方法准确，高效地预测数字孪生轮胎中瞬时切割力的可能性。