The aim of manufacturing can be described as achieving the predefined high quality product in a short delivery time and at a competitive cost. However, it is unfortunately quite challenging and often difficult to ensure that certain quality characteristics of the products are met following the contemporary manufacturing paradigm, such as surface roughness, surface texture, and topographical requirements. Ultraprecision machining (UPM) requirements are quite common and essential for products and components with optical finishing, including larger and highly accurate mirrors, infrared optics, laser devices, varifocal lenses, and other freeform optics that can satisfy the technical specifications of precision optical components and devices without further post-polishing. Ultraprecision machining can provide high precision, complex components and devices with a nanometric level of surface finishing. Nevertheless, the process requires an in-depth and comprehensive understanding of the machining system, such as diamond turning with various input parameters, tool features that are able to alter the machining efficiency, the machine working environment and conditions, and even workpiece and tooling materials. The non-linear and complex nature of the UPM process poses a major challenge for the prediction of surface generation and finishing. Recent advances in Industry 4.0 and machine learning are providing an effective means for the optimization of process parameters, particularly through in-process monitoring and prediction while avoiding the conventional trial-and-error approach. This paper attempts to provide a comprehensive and critical review on state-of-the-art in-surfaces monitoring and prediction in UPM processes, as well as a discussion and exploration on the future research in the field through Artificial Intelligence (AI) and digital solutions for harnessing the practical UPM issues in the process, particularly in real-time. In the paper, the implementation and application perspectives are also presented, particularly focusing on future industrial-scale applications with the aid of advanced in-process monitoring and prediction models, algorithms, and digital-enabling technologies.

Ultra-precision machining (UPM) of Ti-6Al-4V alloy is widely regarded as a challenging material processing due to excessive tool wear and chemical reactivity of the tool and workpiece. Tool wear has a significant influence on the surface quality and also causes damage to the substrate. Therefore, it is critical to consider the tool condition during diamond turning, especially as precision machining moves toward intelligent systems. Consequently, there is a need for effective ways for in-process tool wear monitoring in UPM. This study aims to monitor the diamond tool wear using time-frequency-based wavelet analysis on vibrational signals acquired during the machining of Additively Manufactured (AM) Ti6Al4V alloy. The analysis employed Daubechies wavelet (db4, level 8) to establish a correlation between the Standard Deviation (SD) of the magnitude in the decomposed vibrational signal obtained from both the fresh and used tools. The analysis revealed that at a feed rate of 1 mm/min, the change in SD is 32.3% whereas at a feed rate of 5 mm/min, the change in SD is 8.4%. Furthermore, the flank wear and microfractures are observed using a scanning electron microscope on the respective flank and rake face of the diamond tool.

Additive Manufacturing (AM) has shown excellent research potential for biomedical implants, complex aerofoil structures, military applications, high-pressure cryogenic vessels, and automobile components. The mechanical properties exhibited by the additively manufactured Ti6Al4V ELI alloy components are significantly different from traditionally manufactured Ti6Al4V alloy due to the material anisotropy, orientation of the microstructure, and variation in heat treatments. Among AM techniques, SLM (Selective laser melting) offers unparalleled design flexibility with minimum impurities and high reproducibility. However, post-processing of additive manufactured components is essential due to their poor surface finish and lack of dimensional accuracy. Ti6Al4V ELI (Extra Low Interstitials) alloy is a difficult-to-cut material in Ultra-Precision Machining (UPM) due to its excessive tool wear, hardness, and chemical reactivity. Optimizing machining parameters for better surface finish by UPM is time-consuming and often not economical. Modeling and simulation provide a low-cost approach for the investigation of machining. In this work, a series of cutting experiments have been carried out on additively manufactured Ti6Al4V ELI alloy to study the cutting mechanism during UPM. In addition, Finite Element Model (FEM) is employed to understand the chip formation and cutting forces in UPM with a built-in Johnson-Cook (JC) model and a Johnson-Cook-TANH (JC-TANH) vectorized user-defined material subroutine (VUMAT) model. Cutting forces corresponding to the JC model and JC-TANH are examined with the experimental forces in literature, and the results are found to be fairly close.