The wear of cutting tools, cutting force determination, surface roughness variations and other
machining responses are of keen interest to latest researchers. The variations of these …

Intelligent spindles are core components of the next-generation of intelligent/smart machine
tools in the Industry 4.0 Era. The purpose of this paper is to clarify the concept of intelligent …

To integrate the complete degradation information of machinery, deep learning-based
prognostics approaches usually use monitoring data acquired by different sensors as the …

In modern manufacturing industry, tool wear monitoring plays a significant role in ensuring
product quality and machining efficiency. Numerous data-driven models based on deep …

In this work, a unified AI-framework named Hierarchical Deep Learning Neural Network
(HiDeNN) is proposed to solve challenging computational science and engineering …

Accurate tool condition monitoring (TCM) is essential for the development of fully automated
milling processes. However, while considerable research has been conducted in industrial …

This paper introduces a hybrid model that incorporates a convolutional neural network
(CNN) with a stacked bi-directional and uni-directional LSTM (SBULSTM) network, named …

On-machine monitoring of tool wear in machining processes has found its importance to
reduce equipment downtime and reduce tooling costs. As the tool wears out gradually, the …

Tool wear prediction was significant for improving processing efficiency, ensuring product
quality and reducing tool costs in manufacturing. In this paper, a novel deep learning …

Heteroatoms-rich activated carbon (AC) can effectively promote the pseudo-capacitance of
AC-based electrodes used in supercapacitors. The well-known microstructural properties of …