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• We perform indentation of glasses using varying indenter sharpness and stiffness. • Indenter sharpness affects indentation size effect and crack initiation resistance. • Indenter stiffness affects indentation response when similar to that of the sample. • We discuss the implications for using indentation to evaluate damage resistance. In this study, the effects of both indenter sharpness and stiffness on the indentation response of oxide glasses have been investigated. Using five different indenters, the indentation response in terms of glass deformation mechanism, cracking behavior, and hardness has been examined for three glasses (sodium silicate, sodium borate, and sodium borosilicate). For right square-based pyramidal diamond indenters with varying tip angle, increased indenter sharpness reduces the magnitude of the indentation size effect. Additionally, increased indenter sharpness lowers the crack initiation resistance, which is linked to a reduction of densification. For indenters with the same geometry but made of different materials, the effect of the indenter material is insignificant when the indenter is substantially stiffer than the specimen. When the stiffness of the indenter is only slightly higher or similar to that of the sample, substantial deformation of the indenter occurs, reducing the effective indenter sharpness and hence affecting the indentation response. [ABSTRACT FROM AUTHOR]

Glass hardness varies in a non-linear fashion with the chemical composition and applied load, a phenomenon known as the indentation size effect (ISE), which is challenging to predict quantitatively. Here, using a curated dataset of over 3,000 inorganic glasses from the literature comprising the composition, indentation load, and hardness, we develop machine learning (ML) models to predict the composition and load dependence of Vickers hardness. Interestingly, when tested on new glass compositions unseen during the training, the standard data-driven ML model failed to capture the ISE. To address this gap, we combined an empirical expression (Bernhardt's equation) to describe the ISE with ML to develop a framework that incorporates the symbolic equation representing the domain reasoning in ML, namely Symbolic Reasoning-Informed ML Procedure (SRIMP). We show that the resulting SRIMP outperforms the data-driven ML model in predicting the ISE. Finally, we interpret the SRIMP model to understand the contribution of the glass network formers and modifiers toward composition and load-dependent (ISE) and load-independent hardness. The deconvolution of the hardness into load-dependent and load-independent terms paves the way toward a holistic understanding of the composition effect and ISE in glasses, enabling efficient and accelerated discovery of new glass compositions with targeted hardness. [Display omitted] [ABSTRACT FROM AUTHOR]