Abstract:
In recent years, significant advancements have been achieved in the domain of machine monitoring as witnessed from the beginning of the new industrial revolution (IR) known as IR 4.0. This new revolution is characterized by complete automation, and an increase
in the technological deployments and advanced devices used by various systems. As a result, considerable advancement has been reported by researchers and academia around the world by adopting and adapting the new technology related to the machine
condition monitoring problem. For these reasons, it is important to highlight new findings and approaches in machine condition monitoring based on the wireless sensor solution and machine learning signal processing methodology that are relevant to assist in
advancing this new revolution. This article presents a comprehensive review on tool condition monitoring (TCM), tool wear, and chatter based on vibration, cutting force, temperature, surface image, and smart label monitoring parameters from signal acquisition, signal processing methodology, and decision-making, particularly for the milling process. The paper also provides a brief introduction to the manufacturing industries and computer numerical control (CNC) machine tool demand and a review of machine monitoring and countries involvement in machine monitoring, as well as the approaches and a survey of machine monitoring. The aim is to contribute to this rapidly growing field of machine condition monitoring research by exploring the latest research findings on the solution approach for milling machine process monitoring, to help expedite future research, and to give some future direction that needs to be considered as a path to produce a standardized CNC machine platform.
