Polycrystalline materials are extensively employed in industry. Its surface roughness significantly affects the working performance. Material defects, particularly grain boundaries, have a great impact on the achieved surface roughness of polycrystalline materials. However, it is difficult to establish a purely theoretical model for surface roughness with consideration of the grain boundary effect using conventional analytical methods. In this work, a theoretical and deep learning hybrid model for predicting the surface roughness of diamond-turned polycrystalline materials is proposed. The kinematic–dynamic roughness component in relation to the tool profile duplication effect, work material plastic side flow, relative vibration between the diamond tool and workpiece, etc, is theoretically calculated. The material-defect roughness component is modeled with a cascade forward neural network. In the neural network, the ratio of maximum undeformed chip thickness to cutting edge radius R_TS, work material properties (misorientation angle θ_g and grain size d_g), and spindle rotation speed n_s are configured as input variables. The material-defect roughness component is set as the output variable. To validate the developed model, polycrystalline copper with a gradient distribution of grains prepared by friction stir processing is machined with various processing parameters and different diamond tools. Compared with the previously developed model, obvious improvement in the prediction accuracy is observed with this hybrid prediction model. Based on this model, the influences of different factors on the surface roughness of polycrystalline materials are discussed. The influencing mechanism of the misorientation angle and grain size is quantitatively analyzed. Two fracture modes, including transcrystalline and intercrystalline fractures at different R_TS values, are observed. Meanwhile, optimal processing parameters are obtained with a simulated annealing algorithm. Cutting experiments are performed with the optimal parameters, and a flat surface finish with Sa 1.314 nm is finally achieved. The developed model and corresponding new findings in this work are beneficial for accurately predicting the surface roughness of polycrystalline materials and understanding the impacting mechanism of material defects in diamond turning.

Brittle materials are widely used for producing important components in the industry of optics, optoelectronics, and semiconductors. Ultraprecision machining of brittle materials with high surface quality and surface integrity helps improve the functional performance and lifespan of the components. According to their hardness, brittle materials can be roughly divided into hard-brittle and soft-brittle. Although there have been some literature reviews for ultraprecision machining of hard-brittle materials, up to date, very few review papers are available that focus on the processing of soft-brittle materials. Due to the 'soft' and 'brittle' properties, this group of materials has unique machining characteristics. This paper presents a comprehensive overview of recent advances in ultraprecision machining of soft-brittle materials. Critical aspects of machining mechanisms, such as chip formation, surface topography, and subsurface damage for different machining methods, including diamond turning, micro end milling, ultraprecision grinding, and micro/nano burnishing, are compared in terms of tool-workpiece interaction. The effects of tool geometries on the machining characteristics of soft-brittle materials are systematically analyzed, and dominating factors are sorted out. Problems and challenges in the engineering applications are identified, and solutions/guidelines for future R&D are provided.

Diamond turning based on a fast tool servo (FTS) is widely used in freeform optics fabrication due to its high accuracy and machining efficiency. As a new trend, recently developed high-frequency and long-stroke FTS units are independently driven by a separate control system from the machine tool controller. However, the tool path generation strategy for the independently controlled FTS is far from complete. This study aims to establish methods for optimizing tool path for the independent control FTS to reduce form errors in a single step of machining. Different from the conventional integrated FTS control system, where control points are distributed in a spiral pattern, in this study, the tool path for the independent FTS controller is generated by the ring method and the mesh method, respectively. The machined surface profile is predicted by simulation and the parameters for the control point generation are optimized by minimizing the deviation between the predicted and the designed surfaces. To demonstrate the feasibility of the proposed tool path generation strategies, cutting tests of a two-dimensional sinewave and a micro-lens array were conducted and the results were compared. As a result, after tool path optimization, the peak-to-valley form error of the machined surface was reduced from 429 nm to 56 nm for the two-dimensional sinewave by using the ring method, and from 191 nm to 103 nm for the micro-lens array by using the mesh method, respectively.

Hard coatings are extensively required in industry for protecting mechanical/structural parts that withstand extremely high temperature, stress, chemical corrosion, and other hostile environments. Electrical discharge coating (EDC) is an emerging surface modification technology to produce such hard coatings by using electrical discharges to coat a layer of material on workpiece surface to modify and enhance the surface characteristics or create new surface functions. This paper presents a comprehensive overview of EDC technologies for various materials, and summarises the types and key parameters of EDC processes as well as the characteristics of resulting coatings. It provides a systematic summary of the fundamentals and key features of the EDC processes, as well as its applications and future trends.

Precision is one of the most important aspects of manufacturing. High precision creates high quality, high performance, exchangeability, reliability, and added value for industrial products. Over the past decades, remarkable advances have been achieved in the area of high-precision manufacturing technologies, where the form accuracy approaches the nanometer level and surface roughness the atomic level. These extremely high precision manufacturing technologies enable the development of high-performance optical elements, semiconductor substrates, biomedical parts, and so on, thereby enhancing the ability of human beings to explore the macro- and microscopic mysteries and potentialities of the natural world. In this paper, state-of-the-art high-precision material removal manufacturing technologies, especially ultraprecision cutting, grinding, deterministic form correction polishing, and supersmooth polishing, are reviewed and compared with insights into their principles, methodologies, and applications. The key issues in extreme precision manufacturing that should be considered for future R&D are discussed.