Shared Additive Manufacturing Network for Metal Remanufacturing: Cost-aware Hub Distribution and Order Allocation

Abstract With an increasing emphasis on sustainability, remanufacturing has become essential in conserving resources and extending product lifecycles within the manufacturing sector. Additive manufacturing (AM) offers significant advantages for metal remanufacturing across industries such as aerospace, defense, and marine by providing flexible, precise repairs that surpass traditional methods. However, the high capital costs associated with in-house AM facilities pose substantial challenges for many small- and medium-sized enterprises (SMEs). Recent research highlights AM’s potential within a distributed manufacturing model, facilitating shared manufacturing capacity that allows multiple clients to access AM-based repair and restoration services without the financial burden of maintaining in-house facilities. Despite these benefits, research on designing and optimizing shared AM networks at a systems level remains limited. In this study, we develop a mathematical model to optimize AM hub locations and order allocation, considering variable repair orders from geographically distributed locations and associated transportation logistics. A case study of AM hubs serving the metal part remanufacturing needs of machine shops in the state of North Carolina in the U.S. is conducted to assess the effectiveness of the proposed method. The results demonstrate that strategically located AM hubs significantly enhance cost-efficiency within the shared network, providing SMEs affordable access to advanced AM resources and promoting a shift towards a circular economy.

Authors

Institutions

Publication Details

Published
2025-06-23
DOI
https://doi.org/10.1115/msec2025-155639
Primary Topic
Additive Manufacturing and 3D Printing Technologies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Shared Additive Manufacturing Network for Metal Remanufacturing: Cost-aware Hub Distribution and Order Allocation

Lingxiang Yun, Muyue Han, Jing Zhao, Yiran Yang et al.
Additive Manufacturing and 3D Printing Technologies
article

Shared Additive Manufacturing Network for Metal Remanufacturing: Cost-aware Hub Distribution and Order Allocation

Lingxiang Yun, Muyue Han, Jing Zhao, Yiran Yang, Anika Akther
article en

Abstract

Abstract With an increasing emphasis on sustainability, remanufacturing has become essential in conserving resources and extending product lifecycles within the manufacturing sector. Additive manufacturing (AM) offers significant advantages for metal remanufacturing across industries such as aerospace, defense, and marine by providing flexible, precise repairs that surpass traditional methods. However, the high capital costs associated with in-house AM facilities pose substantial challenges for many small- and medium-sized enterprises (SMEs). Recent research highlights AM’s potential within a distributed manufacturing model, facilitating shared manufacturing capacity that allows multiple clients to access AM-based repair and restoration services without the financial burden of maintaining in-house facilities. Despite these benefits, research on designing and optimizing shared AM networks at a systems level remains limited. In this study, we develop a mathematical model to optimize AM hub locations and order allocation, considering variable repair orders from geographically distributed locations and associated transportation logistics. A case study of AM hubs serving the metal part remanufacturing needs of machine shops in the state of North Carolina in the U.S. is conducted to assess the effectiveness of the proposed method. The results demonstrate that strategically located AM hubs significantly enhance cost-efficiency within the shared network, providing SMEs affordable access to advanced AM resources and promoting a shift towards a circular economy.

The University of Texas at Arlington (US), North Carolina Agricultural and Technical State University (US), Texas A&M University – Corpus Christi (US)
Openalex Percentile: Top 71%
Additive Manufacturing and 3D Printing Technologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Shared Additive Manufacturing Network for Metal Remanufacturing: Cost-aware Hub Distribution and Order Allocation — Lingxiang Yun, Muyue Han, et al. · (2025) | TGRS Research Map | TGRS