Machining data have been increasingly crucial with the development of modern manufacturing strategies, and the explosive growth of data amount revolutionizes how to collect and analyze data. In machining process, anomalies such as machining chatter and tool wear occur frequently, which strongly affect the process by reducing accuracy and quality as well as increasing the time and cost. As a typical type of machining data, signals acquired in real time by advanced sensor techniques are widely embraced to detect those anomalies. This paper reviews the recent development and applications of process monitoring technologies in machining processes, and typical application scenarios in machining processes are discussed with the latest literatures and current research issues. Potential future trends of process data monitoring and analysis for intelligent machining are put forward at the end of the paper.

Accurate tool condition monitoring is necessary for the development of automatic milling technology. In order to improve the accuracy and real-time of online monitoring of tool wear state in machining process, an online monitoring system of milling cutter state based on LabVIEW software development is proposed. Firstly, the modern monitoring technology is introduced into the online monitoring of tool state in principle. The vibration signal is analyzed by wavelet packet in time-frequency domain, and the online monitoring of tool state is realized by machine learning algorithm model. The system can be used for real-time monitoring of tool status, timely alarm to facilitate tool replacement, and ensure high efficiency and high quality of processing. The effectiveness and feasibility of the online monitoring system for milling cutter wear state are verified by experiments, and the purpose of online monitoring tool wear state is preliminarily realized.

The in-situ TiB₂ particle reinforced Al-based metal matrix composites have become a series of promising aeronautical materials due to the advanced properties such as finer evenly-distributed grains, cleaner particle-matrix interface, improved mechanical performance and strength when compared with ex-situ SiC particle reinforced Al-based metal matrix composites. However, over the last 50 years, a significant body of research has been carried out on ex-situ SiC particle reinforced Al-based metal matrix composites from material fabrication process, material property improvement, material mechanical test to machining performance such as machined surface integrity, cutting process simulation and modeling, parameter optimization and fatigue characteristics. For in-situ TiB₂ particle reinforced Al-based metal matrix composites, studies in recent years were mainly focused on the material preparation process and property development and few published works was found on the machining performance of this new kind material. Hence, this article aims to provide a general overview of recent achievement on machining performance of in-situ TiB₂ particle reinforced Al-based metal matrix composites.

Structured light method is one of the best methods for automated 3D measurement in industrial production due to its stability and speed. However, when the surface of industrial parts has high dynamic range (HDR) areas, e.g. rust, oil stains, or shiny surfaces, phase calculation errors may happen due to low modulation and pixel over-saturation in the image, making it difficult to obtain accurate 3D data. This paper classifies and summarizes the existing high dynamic range structured light 3D measurement technologies, compares the advantages and analyzes the future development trends. The existing methods are classified into multiple measurement fusion (MMF) and single best measurement (SBM) based on the measurement principle. Then, the advantages of the various methods in the two categories are discussed in detail, and the applicable scenarios are analyzed. Finally, the development trend of high dynamic range 3D measurement based on structed light is proposed.

Crystalline material is commonly used in human society, crystal plasticity finite element (CPFE) method is an effective way to explore the grain scale thermodynamics behaviors of crystalline materials. In order to promote the development and application of CPFE method, this article briefly reviews grain scale microstructure modelling methods, grain scale constitutive modelling theories and grain scale constitutive parameter calibration methods used in recent CPFE works. Existing grain geometry modelling, polycrystalline microstructure modelling and multiphase microstructure modelling methods for crystalline materials were critically reviewed. Basic grain scale constitutive theories including single crystal elastic, single crystal plastic, grain boundary, damage and thermo-mechanic models were listed. Frequently-used grain scale constitutive parameter calibration methods were summarized.