As additive manufacturing (AM) transitions from prototyping to industrial production, achieving consistency and efficiency at scale becomes a critical challenge—especially for customized medical devices. A custom part, by nature, is not standard, so standardizing production for custom components presents a unique conundrum.
This presentation details how iOrthotics USA standardized both digital and physical AM workflows to scale the production of patient-specific orthotics from roughly 5,000 pairs per year to more than 5,000 pairs per month. The company’s growth from a single HP Multi Jet Fusion machine to a fleet of four, supported by two post-processing stations and eight build units, required structured process engineering and data-driven workflow standardization.
The team implemented batch scheduling, digital job tracking, and process parameter control to optimize throughput while reducing reject rates and lead times. Dedicated production space and trained operators further enhanced repeatability and traceability across three materials.
The presentation will share specific metrics on cycle-time reduction, workflow efficiency, and labor optimization—demonstrating how standardized additive workflows can transform small-scale prototyping into validated, high-volume production.
Attendees will gain actionable insights into scaling AM for customized medical products, with emphasis on workflow design, data feedback loops, and quality-control automation. These strategies provide a transferable framework for organizations seeking to industrialize AM processes and achieve production efficiency at scale.
Learning Objectives:
describe workflow standardization strategies that improve repeatability, throughput, and traceability in multi-printer additive manufacturing environments.
explain how digital job tracking, batch scheduling, and process parameter control enhance production efficiency and quality consistency in additive manufacturing.
apply key lessons from scaling custom orthotic production to other industries seeking to industrialize and optimize additive manufacturing processes.
