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Recent Advances in Integrated Computational and

Experimental Methods for Additive Manufacturing


Golden, Colorado
September 6-8, 2017

 

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Contact Information

For administrative information about the workshop, contact Ruth Hengst at ruth@usacm.org.

Important Dates and Information

Financial support application is now closed.

Deadline to book room at conference hotel has been extended to August 15.  See Accommodations for more information.

Early registration deadline is August 15.

A preliminary program is now available for downloading.  See Program for more details.

September 6-8, 2017: Conference

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Recent Advances in Integrated Computational and Experimental Methods for Additive Manufacturing

Welcome to Recent Advances in Integrated Computational and Experimental Methods for Additive Manufacturing

Additive manufacturing (AM) holds the promise of revolutionizing current paradigms in engineering design, manufacturing, and material and structural performance.  To fully realize the possibilities of AM requires significant advances in both the process and performance modeling of additively manufactured materials and structures.  The modeling and simulation of AM processes is multidisciplinary in nature, involving multiple physics as well as multiple time and space scales, thus requiring interdisciplinary research efforts.  The unique manufacturing process of AM affords the possibility of tightly integrating in-situ measurements and computational modeling.

As a thematic conference held as part of the USACM Technical Thrust Area on Manufacturing and Materials Processing and co-sponsored by the Mineral, Metals, and Materials Society (TMS), the goal of this conference is to bring together researchers from both the computational mechanics and materials science research communities involved in advancing the state-of-the-art in process modeling of additively manufactured (AM) materials and structures.  A theme of the conference will be integration of computational modelingand experiment, leveraging the unique manufacturing aspects of AM, to improve overall modeling predictivity and to enable process optimization.

 

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