In machining operations, dynamometers are typically used to directly measure the forces acting on cutting tools. However, their high cost and complex setup restrict their use to laboratory environments, making them unsuitable for real-time monitoring in general production settings. To overcome this limitation, this study proposes an autoencoder-based learning model for estimating cutting forces using only spindle vibration signals acquired during milling. The model features a deep neural network (DNN) that takes processed spindle vibration signals as input and predicts latent features derived from cutting force signals through an autoencoder. These predicted latent features are then fed into a pretrained decoder to reconstruct the corresponding cutting force signals. To enhance the model's accuracy and robustness, the raw vibration signals sampled at 20 kHz were filtered with a bandpass filter that spans the effective frequency range of 20–2500 Hz, effectively removing irrelevant noise. For validation, an accelerometer was mounted on the spindle head of a milling machine, and vibration data were collected during cutting. The estimated cutting forces were compared to ground truth measurements obtained from a dynamometer. The model achieved a Pearson correlation coefficient of 0.943, demonstrating that reliable cutting force estimation is achievable using only low-cost vibration sensors.

The objective of this study is to suggest the form rolling technology to produce high precision worm on the base of three dimensional finite element simulation and experiment. It is important to determine the initial workpiece diameter in form rolling because it affects the quality of tooth profile. The calculation method of the initial workpiece diameter in form rolling is suggested and it is verified by finite element simulation. The form rolling processes of worm shaft used as automotive part using both the rack dies of counter flow type and the roll dies are considered and simulated with the same numerical model as actual process by the commercial finite element code, DEFORM-3D. Deformation modes of workpiece between the form rolling by the rack dies of counter flow type and the roll dies are investigated from the result of simulation. The experiments using rack dies and roll dies are performed under the same conditions as those of simulation. The surface roughness, the straightness and the profile of worm are measured precisely using the worm shafts obtained from experiment. The results of simulation and experiment in this study show that the form rolling process of worm shaft using the rack dies is decidedly superior to that using roll dies from the aspect of the precision of worm such as the surface roughness, the straightness and the profile of worm.