This research presents a method of evaluating and optimizing the consolidation of parts in an assembly using metal additive manufacturing (MAM). The method generates candidates for consolidation, filters them for feasibility and structural redundancy, finds the optimal build layout of the parts, and optimizes which parts to consolidate using a genetic algorithm. Optimal results are presented for both minimal production time and minimal production costs, respectively. The production time and cost model considers each step of the manufacturing process, including MAM build, post-processing steps such as support-structure removal, and assembly. It accounts for costs affected by parts consolidation, including machine costs, material, scrap, energy consumption, and labor requirements. We find that developing a closed-loop filter that excludes consolidation candidates with structural redundancy dramatically reduces the number of candidates to consider, thereby significantly reducing convergence time. Results show that, when increasing the number of parts that are consolidated, the production cost and time at first decrease due to reduced assembly steps, and then increase due to additional support structures needed to uphold the larger, consolidated parts. We present a rationale and evidence justifying that this is an inherent tradeoff of parts consolidation that generalizes to most types of assemblies. Subsystems that can be oriented with very little support structures, or have low material costs or fast deposition rates can have an optimum at full consolidation; otherwise, the optimum is likely to be less than 100%. The presented method offers a promising pathway to minimize production time and cost by consolidating parts using MAM. In our test-bed results on an aircraft fairing produced with powder-bed electron-beam melting, the solution for minimizing time is to consolidate 48 components into three discrete parts, which leads to a 33% reduction in unit production time. The solution for minimizing production costs is to consolidate the components into five discrete parts, leading to a 28% reduction in unit costs.

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