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On the predictive modelling of machined surface topography in abrasive air jet polishing of quartz glass

DOI:10.1016/j.ijmecsci.2018.12.041 期刊:International Journal of Mechanical Sciences 出版年份:2019 更新时间:2025-09-23 15:22:29
摘要: Abrasive Air Jet Polishing (AAJP) process can be considered as one of the most promising ultra-high-precision finishing methods of quartz glass products due to the superior machined surface integrity, and the high machining feasibility for free-form surfaces. Although many academic and engineering efforts have been paid so far to AAJP process from the theoretical, numerical and experimental aspects, most of the available studies have considered neither (i) the stochastic nature of the abrasive sizes nor (ii) the elastic springback after the impacting abrasives bounced back from the workpiece surface. To fill this gap, this paper proposes the predictive model of the machined surface topography in the AAJP process of quartz glass. A series of experimental trials are performed as well which to a large extent proved the model feasibility and accuracy, and, more importantly, the necessity to consider the normally-distributed abrasive sizes, the stochastic abrasive distribution within the spray airflow, and the elastic workpiece deformation recovery after the AAJP process. Both the experimental and theoretical results also conclude that the small-sized abrasives and the low jet airflow pressure were more effective to achieve the smooth surfaces. The proposed model in this work is expected to be not only helpful to provide the theoretical foundation to study more in-depth mechanism of the AAJP process of brittle materials, but also meaningful to guide the industrial manufacturing in terms of machining parameter optimisation and machined surface quality prediction.
作者: Zhao Wang,Hao Nan Li,Tian Biao Yu,Hao Chen,Ji Zhao
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To propose a predictive model of the machined surface topography in the AAJP process of quartz glass considering the stochastic abrasive size, elastic springback, and plastic deformation, and to validate it through experiments.

The proposed predictive model accurately describes the AAJP process, with experimental validation showing relative errors less than 15%. Key findings include the effectiveness of small-sized abrasives and low jet pressure for smoother surfaces, and the necessity of considering stochastic abrasive sizes and elastic springback. The model provides a foundation for further study and industrial application in parameter optimization and surface quality prediction.

The model assumes spherical abrasive shapes and ignores relative movement between nozzle and workpiece due to low velocity. Computational efforts may be high for large numbers of abrasives. Experimental validation is limited to specific parameters and materials.

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