Hybrid Additive and Subtractive Manufacturing Framework for High-Precision Mechanical Component Production
Abstract
Hybrid additive and subtractive manufacturing is increasingly being used to produce high-value metal components that require both geometric complexity and functional precision. Additive routes such as directed energy deposition and wire-arc additive manufacturing can create near-net-shape structures, internal passages, and material-efficient preforms, whereas subtractive machining remains necessary for datum recovery, tolerance closure, sealing surfaces, bores, and final roughness control. This manuscript revises and extends a hybrid manufacturing framework for high-precision mechanical component production by treating geometry partitioning, allowance allocation, deposition, in-situ metrology, semi-finish machining, finish machining, and final inspection as one coordinated process chain. The proposed method includes analytical expressions for part zoning, machining allowance, deposition time, material removal time, total route duration, dimensional error propagation, surface-finish improvement, material utilization, and multi-objective route selection. An illustrative Ti-6Al-4V precision housing case is used to demonstrate how staged hybrid processing can reduce dimensional deviation from 0.42 mm in the as-deposited state to approximately 0.025 mm after finishing, while improving roughness from Ra = 14.2 µm to Ra = 1.4 µm and maintaining a much lower buy-to-fly ratio than a billet-based subtractive route. The revised framework is positioned for aerospace, tooling, energy, repair, and high-performance mechanical applications where process planning, metrology feedback, and finish-machining strategy must be considered together rather than as isolated post-processing decisions.
How to Cite This Article
Margaret P Jones, Matthew M Garrett, Milton D Zepeda, Sandra K Hudson, Rosella J Martineau (2024). Hybrid Additive and Subtractive Manufacturing Framework for High-Precision Mechanical Component Production . International Journal of Artificial Intelligence Engineering and Transformation (IJAIEAT), 5(1), 75-86.