Tool condition monitoring (TCM) in machining operations is crucial to maximise the useful
tool life while reducing the risks associated with tool breakage. Unlike progressive tool wear …

Abstract Machine learning (ML) has shown to be an effective alternative to physical models
for quality prediction and process optimization of metal additive manufacturing (AM) …

A situation often encountered in the condition monitoring (CM) and health management of
gearboxes is that a large volume of CM data (eg, vibration signal) collected from a healthy …

Fatigue life prediction is critical for ensuring the safe service and the structural integrity of
mechanical structures. Although data-driven approaches have been proven effective in …

Condition monitoring plays a vital role in ensuring the reliability and optimal performance of
various engineering systems. Traditional methods for condition monitoring rely on physics …

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 …

To overcome the limitations associated with purely physics-based and data-driven modeling
methods, hybrid, physics-based data-driven models have been developed, with improved …

The quality of the printed circuit board (PCB), an essential critical connection in
contemporary electronic information goods, directly influences the efficiency and …

Abstract Prognostics and Health Management (PHM), including monitoring, diagnosis,
prognosis, and health management, occupies an increasingly important position in reducing …

Solid polymer electrolytes (SPEs) have the potential to improve lithium-ion batteries by
enhancing safety and enabling higher energy densities. However, SPEs suffer from …