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Development of 18 Quality Control Gates for Additive Manufacturing of Error Free Patient-Specific Implants.

ABSTRACT: Unlike subtractive manufacturing technologies, additive manufacturing (AM) can fabricate complex shapes from the macro to the micro scale, thereby allowing the design of patient-specific implants following a biomimetic approach for the reconstruction of complex bone configurations. Nevertheless, factors such as high design variability and changeable customer needs are re-shaping current medical standards and quality control strategies in this sector. Such factors necessitate the urgent formulation of comprehensive AM quality control procedures. To address this need, this study explored and reported on a variety of aspects related to the production and the quality control of additively manufactured patient-specific implants in three different AM companies. The research goal was to develop an integrated quality control procedure based on the synthesis and the adaptation of the best quality control practices with the three examined companies and/or reported in literature. The study resulted in the development of an integrated quality control procedure consisting of 18 distinct gates based on the best identified industry practices and reported literature such as the Food and Drug Administration (FDA) guideline for AM medical devices and American Society for Testing and Materials (ASTM) standards, to name a few. This integrated quality control procedure for patient-specific implants seeks to prepare the AM industry for the inevitable future tightening in related medical regulations. Moreover, this study revealed some critical success factors for companies developing additively manufactured patient-specific implants, including ongoing research and development (R&D) investment, investment in advanced technologies for controlling quality, and fostering a quality improvement organizational culture.

SUBMITTER: Martinez-Marquez D

PROVIDER: S-EPMC6803939 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Quality is a key determinant in deploying new processes, products, or services and influences the adoption of emerging manufacturing technologies. The advent of additive manufacturing (AM) as a manufacturing process has the potential to revolutionize a host of enterprise-related functions from production to the supply chain. The unprecedented level of design flexibility and expanded functionality offered by AM, coupled with greatly reduced lead times, can potentially pave the way for mass customization. However, widespread application of AM is currently hampered by technical challenges in process repeatability and quality management. The breakthrough effect of six sigma (6S) has been demonstrated in traditional manufacturing industries (e.g., semiconductor and automotive industries) in the context of quality planning, control, and improvement through the intensive use of data, statistics, and optimization. 6S entails a data-driven DMAIC methodology of five steps-define, measure, analyze, improve, and control. Notwithstanding the sustained successes of the 6S knowledge body in a variety of established industries ranging from manufacturing, healthcare, logistics, and beyond, there is a dearth of concentrated application of 6S quality management approaches in the context of AM. In this article, we propose to design, develop, and implement the new DMAIC methodology for the 6S quality management of AM. First, we define the specific quality challenges arising from AM layerwise fabrication and mass customization (even one-of-a-kind production). Second, we present a review of AM metrology and sensing techniques, from materials through design, process, and environment, to postbuild inspection. Third, we contextualize a framework for realizing the full potential of data from AM systems and emphasize the need for analytical methods and tools. We propose and delineate the utility of new data-driven analytical methods, including deep learning, machine learning, and network science, to characterize and model the interrelationships between engineering design, machine setting, process variability, and final build quality. Fourth, we present the methodologies of ontology analytics, design of experiments (DOE), and simulation analysis for AM system improvements. In closing, new process control approaches are discussed to optimize the action plans, once an anomaly is detected, with specific consideration of lead time and energy consumption. We posit that this work will catalyze more in-depth investigations and multidisciplinary research efforts to accelerate the application of 6S quality management in AM.

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Development of 18 Quality Control Gates for Additive Manufacturing of Error Free Patient-Specific Implants.

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