Comparative Analysis of Additive vs. Subtractive Machining Efficiency in Automotive Components

Thesis Abstract

This study investigates the comparative efficiency of additive manufacturing versus subtractive machining in producing automotive components, addressing the persistent challenge of balancing production speed, material utilization, dimensional accuracy, and cost across diverse engineering applications. The problem centers on the lack of comprehensive, component-level evidence comparing lifecycle performance, energy consumption, and manufacturing flexibility between the two fabrication paradigms within a unified automotive context. The aim is to quantify efficiency differentials and determine the conditions under which additive or subtractive processes yield superior outcomes for specific component geometries and material systems. Specific objectives include (1) evaluating production time, material waste, energy usage, and cost per part for a representative set of automotive components manufactured by selective laser melting (SLM) and traditional CNC milling; (2) comparing dimensional tolerances, surface roughness, and mechanical properties (taylored modular tests for tensile, fatigue, and hardness) between the two approaches; (3) identifying process-induced variability and its impact on functional performance; (4) developing a decision framework that links component geometry, material selection, and performance requirements to the preferred manufacturing route. Methodologically, the study adopts a mixed-methods design grounded in the Theory of Technology Readiness and the Resource-Based View to capture both quantitative performance metrics and qualitative insights from industry practitioners. The population comprises automotive components with varying complexity, assessed through a purposive sample of 40 parts drawn from mid- to high-volume suppliers and a benchmark set of 15 parts manufactured both additively and subtractively in controlled laboratory conditions. Data collection instruments include (i) high-precision coordinate measuring machines (CMM) for geometric accuracy, (ii) surface profilometry for roughness, (iii) thermographic cameras and power meters for energy monitoring, (iv) thermal-mechanical test rigs for strength and fatigue evaluation, (v) standardized cost sheets for direct and indirect manufacturing costs, and (vi) structured interviews with process engineers to extract qualitative efficiency drivers. Validity and reliability are ensured through calibration protocols, repeatability trials (n=5 per part per process), and triangulation of quantitative results with expert interviews. Data analysis employs a combination of descriptive statistics, analysis of variance (ANOVA) to compare means across processes, multiple regression to quantify drivers of efficiency, and Monte Carlo simulations to assess sensitivity under variability in material properties and machine settings. A paired-sample comparison is conducted for components manufactured by both methods to isolate process effects, while Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique is used to map interdependencies among process parameters. The theoretical framework integrates the Technology Acceptance and Diffusion of Innovation theories to interpret adoption barriers, complemented by Activity-Based Costing (ABC) to allocate overhead across manufacturing routes. The study also leverages the DOE (Design of Experiments) approach to systematically explore the interaction effects of material type (aluminum alloys, Ti-6Al-4V), geometry (simple through complex internal features), and process parameters on key performance indicators. Ethical considerations include data confidentiality, vendor nondisclosure agreements, and compliance with laboratory safety standards. Expected findings indicate that subtractive machining demonstrates superior dimensional accuracy and surface finish for simple to moderate geometries, with lower variability in mechanical properties, whereas additive manufacturing offers advantages in design freedom, reduced tooling costs, and lower waste for complex geometries, albeit with higher energy consumption and post-processing requirements. The results are anticipated to reveal that component-specific efficiency is strongly influenced by geometry complexity, material compatibility, and post-processing efficiency. The study’s contribution to knowledge lies in providing a robust, empirically validated framework for selecting manufacturing routes in automotive production, integrating lifecycle efficiency metrics with practical decision criteria, and highlighting the trade-offs associated with process-induced variability. It is expected to inform both practitioners and policymakers about optimizing manufacturing portfolios, fostering more sustainable and cost-effective practices. The main conclusion envisaged is that a hybridized decision framework, guided by component geometry and performance requirements, optimizes overall efficiency by leveraging additive manufacturing for complex, low-volume parts and subtractive machining for components demanding high precision and surface integrity. Recommendations include developing standardized post-processing protocols, investing in integrated metrology for routine in-line inspection, and promoting collaborative design for manufacturability to maximize the benefits of each fabrication route.