The multi-sensor configuration enables a comprehensive description of the machining
processes and thus improves the capability of quality prediction model. However, the …

In the process of milling, accurate and reliable monitoring of tool condition monitoring (TCM)
is essential to ensure machining quality and efficiency. Among the current data-driven …

The quality prediction of machining processes is essential for maintaining process stability
and improving component quality. The prediction accuracy of conventional methods relies …

The rapid development of information and communication technologies has facilitated
machining condition monitoring toward a data-driven paradigm, of which the Industrial …

This paper proposes an estimation approach for tool wear and surface roughness using
deep learning and sensor fusion. The one-dimensional convolutional neural network (1D …

Traditional tool condition monitoring methods developed in an ideal environment are not
universal in multiple working conditions considering different signal sources and recognition …

Surface roughness has played a crucial role in determining the quality and performance in
service of the machined workpiece. To enhance the performance of the final product, it is …

Tool wear monitoring has been increasingly important in intelligent manufacturing to
increase machining efficiency. Multi-domain features can effectively characterize tool wear …

Cutting tool is one of the major components to largely influence the overall machining
process efficiency. There has been a significant amount of research conducted in …

Ground surface roughness is regarded as one of the most crucial indicators of machining
quality and is hard to be predicted due to the random distribution of abrasive grits and …