SCOUT: What to Print and What to Test in Two-Budget Closed-Loop Design

Materials design under additive manufacturing (AM) couples three oracles whose costs differ by orders of magnitude: a structurally biased finite-element simulator, an in-situ infrared (IR) video that every print records at no marginal cost, and a destructive mechanical test that only a few printed parts can receive. We formalize this as two-budget closed-loop design with a free intermediate observation, a relative of Bayesian optimization of function networks in which the intermediate observation comes only with a print and each test consumes a printed part from its own budget. We introduce SCOUT, whose residual Gaussian process corrects the simulator from design and IR features and whose two-stage acquisition chooses what to print and then, once the IR is observed, what to test. On a literature-anchored Nickel Aluminum Bronze generator, SCOUT attains lower simple regret than thirteen baselines, including design-of-experiments practice, at every budget, and at the smallest budget 43% lower regret than EPIG on the identical IR-equipped surrogate, 37% with IR noise raised to the dispersion of a real recording, a margin that narrows as the test budget grows. We show that a budget-fraction test threshold read against a part's own prediction tests every design at the same rate under a calibrated model and can discard the best part, and we derive a value-of-testing rule from the two budgets. In a pre-registered study on 150 random instances of public battery and concrete data, the new rule keeps the best battery cell that the old threshold discards in 39 of 53 runs with labelled history and lowers SCOUT's regret there by 8.6 and 12.8 points after Holm's correction, to the lowest mean of 21 arms, tied in one family. A post hoc analysis traces the effect to five cells whose recorded life merges two test batches, and without history a Gaussian process on the design alone is better. A physical AM campaign is left to future work.