Big Data Oriented Smart Tool Condition Monitoring System

摘要

The computer numerical control (CNC) machining is the technical foundation of modern high-end manufacturing. To satisfy the productivity and precision requirement, it is required to monitor and adaptively control the machining process in real time under varying working conditions. The current CNC machining system is limited by the data acquisition methods and modeling approaches, and it is difficult to make full use of monitoring information to smartly assess and optimize the cutting conditions online. This article proposes a new idea and a novel model to solve the problem, with a big data analytics framework for smart tool condition monitoring (TCM). Driven by the monitored big data, this article systematically investigates the key issues for TCM, such as machining dynamics, intelligent tool wear monitoring and compensation algorithms, heterogeneous big data fusion, and deep learning methods. Under this scheme, it develops the smart TCM system that could improve the CNC machining precision and productivity significantly.