Pan, YuhangZhou, PingYan, YingAgrawal, AnupamWang, YonghaoGuo, DongmingGoel, Saurav2020-12-112020-12-112020-11-06Pan Y, Zhou P, Yan Y, et al., (2021) New insights into the methods for predicting ground surface roughness in the age of digitalisation. Precision Engineering, Volume 67, January 2021, pp. 393-4180141-6359https://doi.org/10.1016/j.precisioneng.2020.11.001https://dspace.lib.cranfield.ac.uk/handle/1826/16084Grinding 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.enAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Industry-4.0DigitalisationQualityPredictionDigital manufacturingPrecision grindingArticle