Healthcare industry input parameters for a deterministic model that optimally locates additive manufacturing hubs
Ethan Sanekane et al.
What the paper says
Recent innovations in additive manufacturing (AM) have proven its efficacy for not only the manufacturing industry but also the healthcare industry. Researchers from Cal Poly, San Luis Obispo, and California State University Long Beach are developing a model that will determine the optimal locations for additive manufacturing hubs that can effectively serve both the manufacturing and healthcare industries. This paper will focus on providing an overview of the healthcare industry's unique needs for an AM hub and summarise the specific inputs for the model. The methods used to gather information include extensive literature research on current practices of AM models in healthcare and an inclusive survey of healthcare practitioners. This includes findings on AM's use for surgical planning and training models, the workflow to generate them, sourcing methods, and the AM techniques and materials used. This paper seeks to utilise the information gathered through literature research and surveys to provide guidance for the initial development of an AM hub location model that locates optimal service locations.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.00 × 0.4 = 0.00 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.