Mariana Mutis

Mrs. Mariana Mutis

orcid

Research Focus & Metrics

Academic Career Length

2Years (2025-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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28 October 2025 in Journal of Composites Science
Verified
Journal Article
Open access
PDF file
MMMariana Mutis
Mariana Mutis
MTMario andres Guerrero Torres
Mario andres Guerrero Torres
Sylvia María Villarreal-ArchilaSylvia María Villarreal-Archila
Sylvia María Villarreal-Archila
JNJairo Nuñez
Jairo Nuñez
Additive manufacturing (AM) is rapidly transforming clinical workflows in dentistry by enabling the customized, efficient, and digitally integrated production of dental devices. However, the existing literature lacks a process-oriented perspective on its technical and operational impact. This study aims to address this gap through a dual-phase analysis using the Input–Transformation–Output (ITO) framework, providing practical insights into the operational reconfiguration enabled by AM. The first phase examined materials, image acquisition methods, design and lamination software, printing technologies, and key parameters across each stage of the AM workflow. The second phase analyzed four clinical applications (dental models, crowns and bridges, occlusal splints, and surgical guides) supported by a structured fabrication protocol and scanning electron microscopy (SEM) of 18 resin samples to assess surface quality and process-related defects. In addition, for each application, a comparative process analysis with traditional workflows was conducted using ASME diagramming. The findings indicate that AM reduces cycle times, manual intervention, and supply chain reliance while enabling production models such as Make-to-Order (MTO) and Engineer-to-Order (ETO). Its integration also fosters decentralized, in-clinic manufacturing with enhanced autonomy, flexibility, and reduced lead times. Nonetheless, this study highlights persisting challenges, including post-processing quality control, training requirements, and cost-efficiency concerns in low-volume settings. A hybrid model combining AM with conventional methods emerges as a pragmatic strategy for clinical adoption.