Monitoring and Predicting the Surface Generation and Surface Roughness in Ultraprecision Machining: A Critical Review

Abstract

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.

Experimental Investigation on Roughness Parameters of 42CrMo4 Steel Surface During Nitrocarburizing Treatment

“…Surface roughness in the manufacturing industry must be compatible with both job requirements and quality standards [1]. Surface roughness evaluation is required for describing functional behavior and monitoring product quality [2]. Furthermore, it is a crucial characteristic in many fundamental industrial concerns, such as friction [3], adhesion [4], electrochemical potential [5][6], aesthetic appearance [7], as well as positional accuracy [8].…”

Surface Characteristics Measurement Using Computer Vision: A Review

“…If the Euclidean value is higher, then higher would be the value of dissimilarity. 'The circular shaft-based matching' removes the possibility of a 'simple shift' within the image, which could affect the Euclidean distance [147]. The steps of measuring surface roughness using image processing are shown in Fig.…”

“…Therefore, it is understood that Ga, GLCM comparison, GLCM energy, GLCM Max likelihood, and Fractal dimensions have a similar pattern when correlating to R. Inconsistency in the quantification of surfaces with visual ruggedness parameters can be reduced to a large degree by considering the transition and properly collecting images from machined surfaces. Manjunath et al [147] show clearly that the Machine Vision approach is used to approximate the surface roughness of the machined components and that the results show a strong linear relation between stylus Ra values and optical Parameters. The standard deviation strongly correlates with Ra values of all given optical parameters, as determined by traditional and commonly agreed type instruments on machined surfaces produced by electric discharge processes, milling, and grinding processes.…”

Advances in Ultraprecision Diamond Turning: Techniques, Applications, and Future Trends

“…It emphasizes the need for further development of ultraprecision machining technology to reduce surface fluctuation by considering the dynamics of machine tools in tool path planning (Wang et al, 2018). Moreover, it discusses the integration of Industry 4.0 and machine learning for the optimization of process parameters, particularly through in-process monitoring and prediction, thus avoiding the conventional trial-and-error approach (Manjunath et al, 2021). The paper also highlights the qualitative change brought about by ultraprecision turning, which mainly employs diamond tools, and the remarkable progress made in enabling the machining of ultra-fine optical surfaces with complex geometries (Kim et al, 2004).…”