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.
Highlights
Many studies have attempted to build correlations in order to discover complicated non-linear relationships for monitoring surface roughness [49]
Plaza and Lopnez have employed singular spectrum analysis (SSA) to monitor surface finish in diamond turning [66], whereas Plaza of and colleagues monitored the signals in the time domain and frequency domain,12using predictive regression models with mean relative errors of 10% [67]
This paper aims to provide a comprehensive critical review and exploration of studies on approaches to the quantitative assessment and prediction of surface roughness generation in ultraprecision machining with a focus on diamond turning and applications
Summary
Due to its rapid development, AI is gaining humanlevel performance It provides a better outcome in areas such as the health monitoring of machines using Machine Learning (ML) [2]. Recent research suggests that the application of machine learning improves prediction performance and provides a digital solution. It provides a better outcome in areas such as the health moni ofthat. Recent research suggests application of machine learning improves prediction performance and provides a digital solution that can enable the manufacturing sector to implement current and future technologies forenable automation and digitization. The components fabricated microfabrication facilities are becoming very important [5]. The exponential improvements in machining the so-called Taniguchi curve [6].
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