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.

It is well known that diamond wears out rapidly (within several metres of cutting length) when machining low carbon ferrous alloys and pure iron. The past few years have seen a growing interest in the field of elliptical vibration-assisted machining (EVAM) due to it being successful in the micromachining of difficult-to-cut materials including steel. During EVAM, a cutting tool is prescribed an oscillatory motion perpendicular to the direction of cutting, thereby causing the tool to be relieved intermittently from chemical and physical contact with the workpiece. This phenomenon serves as a guideline to develop the simulation test bed for studying EVAM in this work to compare it with conventional cutting. The pilot implementation of the EVAM came as a quasi-3-dimensional (Q3D) elliptical cutting model of body-centred cubic (BCC) iron with a diamond cutting tool using molecular dynamics (MD) simulation. The developed MD model supplemented by the advanced visualization techniques was used to probe the material removal behaviour, the development of the peak stress in the workpiece and the way the cutting force evolves during the cutting process. One of the key observations was that the cutting chips of BCC iron during conventional cutting underwent crystal twinning and became polycrystalline, while EVAM resulted in cutting chips becoming highly disordered, leading to better viscous flow compared to conventional cutting.

Single point diamond turning is one of the ultraprecision methods to generate high quality surfaces with highest possible profile accuracies. However, diamond turning process also introduces some unique errors which affect the surface finish. Some of these errors are controllable, while some are noncontrollable. Controllable parameters need proper selection, whereas uncontrollable parameters require adequate understanding of the process behavior. In this study only the effect of uncontrollable parameters on surface finish is explored. Tool overhang results tool–workpiece vibration whereas dynamic unbalance of vacuum responsible for workpiece wobble and both affect the surface finish considerably. Chipping of tool edge, improper extraction of chips, and slipping of workpiece on vacuum chuck are the main reasons of random scratch marks on surface and are contributing factors for degradation of surface finish.

This paper reports the performance enhancement benefits in diamond turning of the silicon wafer by incorporation of the surface defect machining (SDM) method. The hybrid micromachining methods usually require additional hardware to leverage the added advantage of hybrid technologies such as laser heating, cryogenic cooling, electric pulse or ultrasonic elliptical vibration. The SDM method tested in this paper does not require any such additional baggage and is easy to implement in a sequential micro-machining mode. This paper made use of Raman spectroscopy data, average surface roughness data and imaging data of the cutting chips of silicon for drawing a comparison between conventional single-point diamond turning (SPDT) and SDM while incorporating surface defects in the (i) circumferential and (ii) radial directions. Complementary 3D finite element analysis (FEA) was performed to analyse the cutting forces and the evolution of residual stress on the machined wafer. It was found that the surface defects generated in the circumferential direction with an interspacing of 1 mm revealed the lowest average surface roughness (Ra) of 3.2 nm as opposed to 8 nm Ra obtained through conventional SPDT using the same cutting parameters. The observation of the Raman spectroscopy performed on the cutting chips showed remnants of phase transformation during the micromachining process in all cases. FEA was used to extract quantifiable information about the residual stress as well as the sub-surface integrity and it was discovered that the grooves made in the circumferential direction gave the best machining performance. The information being reported here is expected to provide an avalanche of opportunities in the SPDT area for low-cost machining solution for a range of other nominal hard, brittle materials such as SiC, ZnSe and GaAs as well as hard steels.