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This research presents a method of 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. Results are presented for both minimal production time and minimal production costs, respectively. The production time and cost models consider 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 part consolidation, including machine costs, material, scrap, energy consumption, and labor requirements. We find that developing a closed-loop filter that excludes consolidation candidates that are structurally redundant with others dramatically reduces the number of candidates, 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 important tradeoff of part consolidation that generalizes to many types of assemblies. Subsystems that are smaller, or can be oriented with very little support structures or have low material costs or fast deposition rates can have an optimum at full consolidation; for other subsystems, the optimum is 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 for an aircraft fairing produced with powder-bed electron beam melting, the solution for minimizing production cost (time) is to consolidate 17 components into four (two) discrete parts, which leads to a 20% (25%) reduction in unit production cost (time).

Many practical design problems are multidisciplinary and typically involve the transfer of complex information between analysis modules. In solving such problems, the method for performing multidisciplinary analyses greatly affects the speed of the total design time. Thus, it is very important to group and order a multidisciplinary analysis (MDA) process so as to minimize the total computational time and cost by decomposing a large multidisciplinary problem into several subsystems and then processing them in parallel. This study proposes a decomposition method that exploits parallel computing, including the determination of an optimal number of subsystems by using a multi-objective optimization formulation and a messy genetic algorithm (GA) modified to handle discrete design variables. In the suggested method, an MDA process is decomposed and sequenced for simultaneously minimizing the feedback couplings within each subsystem, the total couplings between subsystems, the variation of computation times among subsystems, and the computation time of each subsystem. The proposed method is applied to the decomposition of an artificial complex system example and a multidisciplinary design problem of a rotorcraft with 17 analysis modules; promising results are presented using this proposed method.