Engineering glasses and glass-ceramics: A processability-to-application readiness selection framework

Glass and glass-ceramics are often introduced by family name—soda-lime silicate, borosilicate, aluminosilicate, fused silica, LAS, MAS, or bioactive glass—yet engineering selection is controlled less by family labels than by processability, microstructure control, reliability, and application readiness. This review reframes common glasses and glass-ceramics through a practical selection framework linking viscosity-temperature behavior, crystallization windows, flaw-controlled strength, thermal expansion, chemical durability, and scale-up barriers. The central contribution is the Glass Selection Triangle—a unified theory stating that commercial success for any glass product requires simultaneous satisfaction of three constraints: processability (Glass Processability Index, GPI ≥ 55), reliability (Flaw Tolerance Score, FTS ≥ 40), and sustainability (Glass Sustainability Index, GSI ≥ 35). The theory is validated against a database of 20 commercial glass products and five historical failure cases. Six quantitative frameworks are introduced: GPI (0–100 metric integrating working temperature window, critical cooling rate, liquidus temperature, cord sensitivity, and mould compatibility), Glass Maturity Level (GML, a nine-stage scale from laboratory discovery to full bankability), Crystallization Risk Number (CRN, quantifying devitrification probability during forming), FTS (integrating critical flaw size, fracture toughness, and Weibull modulus), Glass-Ceramic Interface Stability Index (GCISI, for solid-state battery electrolytes), and GSI (combining carbon footprint, cullet recyclability, batch melting energy, and raw material criticality). A twelve-mode Failure Mode Library and a seven-node Selection Decision Tree are provided. The Glass Processability Paradox is articulated: glasses with the highest functional performance systematically have the lowest processability, and vice versa. The Glass Selection Grand Unified Theory (GSP = (GPI × FTS × GSI)^(1/3)) provides a single dimensionless parameter predicting commercial viability. A sensitivity analysis demonstrates that the ranking of commercial glasses by GPI is robust against factor weighting variations of ±50%. Machine learning feature importance analysis independently confirms the dominance of working window and critical cooling rate—the two GPI factors weighted at 0.30 each. The review concludes with a research roadmap through 2040 identifying five priority directions: decarbonized melting, cullet circularity, glass-ceramic interface engineering for solid-state batteries, flaw tolerance prediction, and bioactive glass scaffold manufacturing.