Comparative Analysis of Additive and Subtractive Manufacturing Energetics in Aerospace Parts

Thesis Abstract

This study addresses the escalating energy intensity and environmental footprint of aerospace manufacturing by comparing the energetics of additive manufacturing (AM) and subtractive manufacturing (SM) processes for critical aerospace components. The problem centers on the lack of comprehensive, process-level energy analyses that integrate yield, process efficiency, and lifecycle energy consumption across diverse geometries and materials. The aim is to quantify and contrast the direct and indirect energy demands of AM and SM for a representative set of aerospace parts, elucidate the trade-offs between energy use and part performance, and identify opportunities for energy optimization. Specific objectives include (1) to measure and compare the embodied and operational energy consumption of AM and SM for a curated suite of titanium and aluminum aerospace components; (2) to evaluate energy intensity as a function of geometric complexity, material, and production scale; (3) to assess the influence of process parameters on energy efficiency using design of experiments (DOE); (4) to develop a predictive energy model incorporating machine efficiency, thermal management, and post-processing energy; and (5) to propose evidence-based recommendations for process selection and energy optimization. The methodological approach adopts a comparative, mixed-methods research design anchored in industrial case studies. The population comprises aerospace manufacturing facilities within a major aviation supply ecosystem. A purposive sample of six components with varying complexity (ranging from simple brackets to lattice-structured airfoil cores) will be selected, produced via both AM (selective laser melting and electron beam melting) and SM (subtractive milling and turning) under comparable material and heat-treatment regimes. Data collection instruments include (i) direct energy metering on machines and auxiliary systems, (ii) production records and process parameters (e.g., laser power, scan speed, material feed rate, spindle load, cutting depth), (iii) post-processing energy use (heat treatment, surface finishing, heat stamping), and (iv) performance metrics (weight, density, surface finish, mechanical properties). Validity and reliability will be ensured through instrument calibration, repeated trials (n=3 per configuration), and triangulation with utility bills and time-motion studies. Data analysis will proceed in three layers. First, descriptive statistics and energy intensity metrics (kWh per part, kWh per unit volume, and energy per mechanical property target) will be computed. Second, inferential analyses will use paired t-tests and one-way ANOVA to compare energy indicators across AM and SM for each component, supplemented by regression analysis to identify key determinants of energy use, including geometric complexity and material. Third, a life-cycle energy assessment (LCEA) will aggregate cradle-to-gate energy, incorporating material production, machine operation, and post-processing. A predictive energy model will be developed using multiple linear regression and validated by k-fold cross-validation, with model selection guided by Akaike Information Criterion (AIC). The study will also apply the Resource-Based View and Theory of Total Energy Efficiency to interpret results and discuss strategic implications for manufacturing decisions. Expected findings anticipate that AM will exhibit higher direct machine energy for complex geometries due to layer-by-layer energy demands but lower energy for material usage owing to near-net-shape capabilities, whereas SM may incur higher post-processing energy for surface finishing but lower early-stage energy for simple geometries. The energy gap is expected to narrow for high-volume production when lattice-structured AM parts demonstrate superior material optimization, while process parameter optimization (e.g., hatch strategy, cutting strategy) will significantly influence energy performance. The study contributes to knowledge by providing a robust, empirically grounded energy benchmarking framework for AM versus SM in aerospace, including a validated predictive model and a decision-support tool for energy-aware process selection. The conclusions will emphasize the conditions under which AM or SM is energetically favorable, and recommendations will address process parameter optimization, post-processing reduction, hybrid manufacturing strategies, and policy implications for energy efficiency in aerospace supply chains.