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IN-MaC

About IN-MaC

IN-MaC is Purdue University’s response, in partnership with Ivy Tech Community College and Vincennes University, to the economic challenge facing the United States over the next decade: How do we create growth and sustain the American Dream for generations to come? Economic and political instability around the world mean that the time is right to rebuild America’s manufacturing capacity. Many US based companies face a shortage of trained workers capable of filling open positions. Technology is moving quickly, and must be transferred to industry quickly in order to remain competitive. Investment in new knowledge creation today will ensure future competitiveness for US industry. IN-MaC answers these needs with an integrated partnership among industry, academia, and government.

Documents:

IN-MaC Overview

INVESTING IN THE FUTURE
Competitiveness and sustainability of the manufacturing sector are essential to ensure job growth and economic prosperity in Indiana. There is renewed national interest in manufacturing research and education. The call for action to renew the country’s leadership in manufacturing creates a once in a generation opportunity for the State to lead the nation in the revitalization of the manufacturing sector. The Indiana Next Generation Manufacturing Competitiveness Center (IN-MaC) is a bold effort led by Purdue University to transform manufacturing in the State of Indiana....

Download: IN-MaC Overview (PDF)

IN-MaC Briefing

IN-MaC: The Next Generation Indiana Manufacturing Competitiveness Center

Connecting Statewide Resources for Manufacturing Knowledge Creation and Delivery

Download: IN-MaC Briefing (PowerPoint Presentation as a PDF)

IN-MaC Research Threads

1) Digital Manufacturing Enterprise:
Companies need more efficient and predictable methods to virtually design, test, build, and support their products, while minimizing costs for those activities. The digital manufacturing enterprise forms the digital mirror to the physical product, including its geometric, behavioral, and contextual definitions. It leverages a model-based product definition to support the production, supply chain, and sustainment environments in a model-based enterprise. The digital manufacturing enterprise also includes the software, hardware, and network architectures necessary to deliver product information across the lifecycle, as well as the methodologies to enable data authors and consumers inside and outside the enterprise.

2) Personalization
Advances in the connectivity and availability of computational resources have the potential to fundamentally change manufacturing and delivery of products and services, by allowing personalized products and services with the efficiency of mass production. The ability to sense-and-respond in real- time to the various product lifecycle stages can allow manufacturing enterprises to personalize products to meet individual customer needs. Personalized manufacturing will focus on the new tools and processes for capturing multiple inputs to design, production and sourcing of products; the massive data analysis and storage mechanisms necessary to handle the amounts of data generated in creating products this way; and the social and economic impacts that result from developing products in this manner.

3) Market Viable Manufacturing Processes
Current and future manufactured products are more than just discrete parts combined into higher-level assemblies to form a finished good. They often incorporate newly invented materials and complex, electro-mechanical systems with embedded software to give them contextual intelligence and a personalized connection to the consumer. These types of products can be developed in research labs, but it is necessary to translate laboratory scales and techniques for manufacturing to commercially viable manufacturing approaches that meet market volume and pricing needs. To do this, it will be necessary to combine the variability of personalization with techniques to model and simulate product performance, supply networks, and sustainment resources, while considering product complexity (precision and volume) and production capacity.