Here we introduce a new evolutionary algorithm called the Lotus Effect Algorithm, which
combines efficient operators from the dragonfly algorithm, such as the movement of …

This paper develops a steps-ahead tool wear prediction method based on particle filtering
and support vector regression. A degradation phase classification method is presented …

The accurate prediction of the remaining useful life (RUL) of Li-ion batteries holds significant
importance in the field of predictive maintenance, as it ensures the reliability and long-term …

The roughness of the part surface is one of the most crucial standards for evaluating
machining quality due to its relationship with service performance. For a preferable …

The die steel NAK80 is used in specular optical molds, deep drawing forming dies, and cold
extrusion dies in large quantities; high strength and hardness often induce tool wear during …

Milling tool wear, a ubiquitous challenge in industrial automation and manufacturing, leads
to diminished equipment utilization, escalating costs, and a decline in product quality. The …

Feature selection (FS) represents an essential step for many machine learning-based
predictive maintenance (PdM) applications, including various industrial processes …

Accurate prediction of tool wear status plays a critical role in the digital manufacturing
industry, and its health level directly affects machining quality, production costs, and overall …

Timely prediction of bearing faults is essential for minimizing unexpected machine downtime
and improving industrial equipment's operational dependability. The Q transform was …

In intelligent manufacturing, accurate prediction of grinding wheel wear is essential to
reduce maintenance costs and improve production efficiency. To achieve accurate forecasts …