Optimization of Parts Consolidation for Minimum Production Costs and Time Using Additive Manufacturing Zhenguo Nie † , Sangjin Jung † , Levant Burak Kara † , Kate S. Whitefoot *†‡ † Mechanical Engineering, Carnegie Mellon University ‡ Engineering and Public Policy, Carnegie Mellon University Pittsburgh, PA, USA ABSTRACT 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 * Corresponding author: kwhitefoot@cmu.edu components into five discrete parts, leading to a 28% reduction in unit costs. 1. INTRODUCTION Parts consolidation is a design change in which multiple components that were formerly discrete and assembled together are fabricated as a single part. Through parts consolidation, it is possible to reduce weight and size, minimize assembly operations, improve performance, and prolong service life [1]. Recent research shows that parts consolidation (referred to as consolidation hereafter) has a great potential to improve product or system performance, reduce weight and material usage, and reduce costs. Multiple demonstrations of consolidation in the industry have realized substantial reductions of production or lifecycle costs, weight reductions of up to 60%, and improved reliability [2]. Currently, it is difficult for researchers and manufacturers to identify promising opportunities to redesign products for consolidation using additive manufacturing (AM). Redesign for consolidation is done on an ad-hoc basis without systematically characterizing the effects of consolidating particular parts on assembly operations, production costs and time, or other manufacturer objectives. Complicating matters, determining which parts to consolidate is a combinatorial problem that explodes to large numbers of possible candidates even for assemblies with relatively few parts. This research develops the first method that optimizes which parts to consolidate in an assembly using AM. Given a user-provided assembly design, the method seeks to minimize costs or time across the full production process consisting of AM setup and build; finishing steps, including support structure removal; and assembly (if needed). Production costs are estimated using a process-based cost model that considers machine, material, and energy inputs; labor; and rejected parts. The method includes six stages to find the optimally consolidated design: generating candidates for consolidation using a connectivity matrix, filtering the candidates based on 1 Copyright © 2019 ASME Proceedings of the ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC/CIE2019 August 18-21, 2019, Anaheim, CA, USA DETC2019-97649 Attendee Read-Only Copy