Programs

Advanced Manufacturing Data Infrastructure and Analytics Program

Program Manager: Simon Frechette
The Advanced Manufacturing Data Infrastructure and Analytics (AMDIA) program will lay the groundwork for advanced data infrastructure to improve the productivity, resiliency, and sustainability of manufacturing operations and enterprises across the supply chain. As the manufacturing industry produces increasing volumes of diverse data, stakeholders need robust data infrastructure and trusted analytics to prepare, model, understand, and utilize their data effectively for improved control and better decision-making. Emerging technologies allow manufacturers to collect, structure, link, and analyze data in new ways. However, technologies are seldom one-size-fits-all solutions. Research into manufacturing data and the role of humans in the process is needed to effectively adopt and integrate solutions into existing operations.

The AMDIA program will develop methods, standards, tools, models, and datasets, to advance manufacturing data infrastructure, with a focus on data collection, transformation, traceability, and interoperability. AMDIA outputs will lower the barriers to incorporating new technologies and analysis methods into existing and emerging operations. These outcomes will enable trusted, understandable, and reproducible information workflows across engineered products, manufacturing enterprises, and supply chains to improve decision-making.

Measurement Science for Additive Manufacturing Program

Program Manager: Paul Witherell and Brandon Lane
The Measurement Science for Additive Manufacturing program aims to develop and deploy advances in measurement science that will enable rapid design-to-product transformation through: material characterization; in-process sensing, monitoring, and model-based optimal control; performance qualification of materials, machines, processes and parts; and end-to-end digital implementation and analysis of Additive Manufacturing (AM) processes and systems. Common challenges often faced when working towards the successful implementation of AM include: high levels of process variability; low part accuracy and surface quality; inconsistent material properties; and lack of process and part qualification and certification methods. To address these challenges, and reduce perceived risks in order to facilitate widespread AM adoption, the program will develop: methods for part and material characterization; exemplar data, datasets, and databases to accelerate the design, fabrication, and acceptance of AM parts; process metrology, sensing, and control methods to maximize part quality and production throughput in AM; test methods, protocols, and reference data to reduce the cost and time to qualify AM materials, processes, and parts; and an information systems architecture, including metrics, models, and validation methods to shorten the design-to-product cycle times in AM.  It is anticipated that this programmatic effort will result in: accelerated  proliferation of AM parts in high-performance applications benefiting from AM's unique capabilities; improved quality and throughput for AM;  rapid qualification of AM materials and processes leading to increased confidence in AM products used in industry; and streamlined design-to-product transformations leading towards more accessible AM technologies for small and medium-sized companies, increasing industrial competitiveness.