Browsing by Author "Yan, Ying"

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    Elastic recovery of monocrystalline silicon during ultra-fine rotational grinding
    (Elsevier, 2020-05-21) Huang, Ning; Yan, Ying; Zhou, Ping; Kang, Renke; Guo, Dongming; Goel, Saurav
    Micromachining of brittle materials like monocrystalline silicon to obtain deterministic surface topography is a 21st Century challenge. As the scale of machining has shrunk down to sub-micrometre dimensions, the undulations in the machined topography start to overlap with the extent of elastic recovery (spring back) of the workpiece, posing challenges in the accurate estimation of the material's elastic recovery effect. The quantification of elastic recovery is rather complex in the grinding operation due to (i) randomness in the engagement of various grit sizes with the workpiece as well as (ii) the high strain rate employed during grinding as opposed to single grit scratch tests employed in the past at low strain rates. Here in this work, a method employing inclination of workpiece surface was proposed to quantify elastic recovery of silicon in ultra-fine rotational grinding. The method uniquely enables experimental extraction of the elastic recovery and tip radius of the grits actively engaged with the workpiece at the end of the ultra-fine grinding operation. The proposed experimental method paves the way to enable a number of experimental and simulation endeavours to develop more accurate material constitutive models and grinding models targeted towards precision processing of materials. It can also be shown that using this method if the tip radius distribution of active grits is measured at different time instances, then this data can be used to assess the state of the grinding wheel to monitor its wear rate which will be a useful testbed to create a digital twin in the general framework of digital manufacturing processes.
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    New insights into the methods for predicting ground surface roughness in the age of digitalisation
    (Elsevier, 2020-11-06) Pan, Yuhang; Zhou, Ping; Yan, Ying; Agrawal, Anupam; Wang, Yonghao; Guo, Dongming; Goel, Saurav
    Grinding is a multi-length scale material removal process that is widely employed to machine a wide variety of materials in almost every industrial sector. Surface roughness induced by a grinding operation can affect corrosion resistance, wear resistance, and contact stiffness of the ground components. Prediction of surface roughness is useful for describing the quality of ground surfaces, evaluate the efficiency of the grinding process and guide the feedback control of the grinding parameters in real-time to help reduce the cost of production. This paper reviews extant research and discusses advances in the realm of machining theory, experimental design and Artificial Intelligence related to ground surface roughness prediction. The advantages and disadvantages of various grinding methods, current challenges and evolving future trends considering Industry-4.0 ready new generation machine tools are also discussed.