Toward resilient energy assets: comparative evaluation of additive manufacturing technologies for critical spare parts
Fatima Ghassan Alabtah, Manel Chihi, Abdalla Mohammed, Numan Khan, Yasser Al Hamidi, Mohammad Albakri, Marwan KhraishehPurpose
Ensuring continued operation of industrial assets increasingly depends on replacing legacy components whose original tooling, suppliers and manufacturing routes are no longer available. Additive manufacturing (AM) offers a route to restore supply of such critical parts, but practical guidance on process selection and validation is limited. This study aims to establish and validate an end-to-end workflow for qualifying AM routes for legacy metal components in the energy sector, using an eccentric chemical pump gear as a representative case study.
Design/methodology/approach
A cast iron gear was reverse engineered using high-resolution 3D scanning and the geometry was refined through generative design to improve printability and reduce material usage. The part was reproduced using CoCr alloy by Laser Powder Bed Fusion (LPBF) and 4140 chromoly steel by sinter-based Bound Metal Deposition (BMD). Finite element analysis and LPBF process simulation were used to verify load-bearing performance and screen build orientations. Printed parts were postprocessed, inspected for dimensional accuracy, mechanically characterized and validated using API 677 gear contact testing and in-field operation.
Findings
Both AM routes achieved dimensional compliance and acceptable gear contact patterns, while exhibiting distinct trade-offs in mechanical response, accuracy, material consumption and workflow complexity. Successful in-field operation of the AM gear confirmed that the design-simulation-manufacture-test workflow can deliver functionally equivalent replacement parts and reduce dependence on obsolete casting routes.
Originality/value
A comparative, field-validated assessment is provided of LPBF and BMD for legacy component replacement in the energy sector. The study links reverse engineering, generative design, process simulation and experimental validation into an evidence-based process-selection template for qualifying AM routes for critical spare parts.