This study aims to present an overall review of the recent research status regarding Machine
Learning (ML) applications in machining processes. In the current industrial systems …

Intelligent manufacturing is considered among the most important elements of the modern
industrial revolution, which includes digitalization, networking, and the development of the …

In this paper the idea of Self-Optimizing Machining Systems (SOMS) is introduced and
discussed. Against the background of Industry 4.0, here the focus is the technological level …

Advanced modelling and optimisation techniques have been widely used in recent years to
enable intelligent manufacturing and digitalisation of manufacturing processes. In this …

The general trend towards lightweight components and stronger but difficult to machine
materials leads to a higher probability of vibrations in machining systems. Amongst them …

Along with nanoscience advances, nanoarchitectonics have emerged as novel
nanomaterials, with self-assembled arrangement of atoms and interesting properties …

Accurate milling stability prediction is necessary for selecting chatter-free machining
parameters to ensure the machining quality. With the development of machine learning …

Robotic machining systems have been widely implemented in the assembly sites of large
components of aircraft, such as wings, aircraft engine rooms, and wing boxes. Milling is the …

Machine learning (ML) and artificial intelligence (AI) have experienced an increased degree
of applications associated with Industry 4.0. Their effective utilisation is elevated with readily …

Efficient and accurate chatter prediction is critical for selection of chatter-free process
parameters to improve machining productivity and surface quality of the workpiece …