Self-Driving Laboratories for Photopolymer Formulation: Closed-Loop Experimentation, Measurement Quality and Decision Governance

Self-driving laboratories combine automated experimentation with algorithmic experiment selection, but the evidence base is dominated by chemistry and materials platforms outside photopolymer formulation. This review therefore does not assume that general autonomous-laboratory success transfers directly to photocurable systems. It defines the additional state variables required when formulation composition, optical absorption, inhibition, rheology, cure history, washing and post-cure jointly determine the measured outcome. The central engineering proposition is that an autonomous loop is only as credible as its measurement traceability, state definition, feasible-domain controls and decision governance. A closed loop that optimises a convenient proxy while losing formulation identity, specimen history or measurement uncertainty can converge reproducibly on the wrong engineering decision. The review separates exploration, optimisation and qualification; specifies a minimum state record for photopolymer experiments; and proposes stop, escalation and human-review gates for autonomous campaigns. ER-401.