Integrating Materials and Manufacturing Innovation (IMMI) is committed to building a seamless and dynamic model-based framework supporting the accelerated discovery, development, and application of materials and manufacturing processes. The journal explores innovations from the discovery of materials through their manufacture that support the practice of Integrated Computational Materials Engineering (ICME). IMMI focuses on presenting new experimental and computational tools, data analysis and management methods, and valuable multiscale datasets, as well as the application and the impact using of an ICME approach to advance materials and manufacturing technologies.
IMMI provides a platform for the presentation of novel research and engineering efforts seeking to solve pervasive or recurring needs in materials and manufacturing that adhere to the discipline of ICME. IMMI supports research seeking to build model-based definitions of materials and manufacturing processes that incorporate the processing-structure-properties-performance paradigm. The journal provides a venue for presenting innovative approaches to overcoming key technical challenges in integrating experiment, computation, and data that support creation of a materials innovation infrastructure for ICME. These challenges include description and representation of complex material structure, application of artificial intelligence approaches, data management, model verification and validation, as well as seamlessly linking simulations and data. Tools and methods that integrate and manage information and knowledge across length and time scales as well as discipline boundaries are extraordinarily complex and at the forefront of scientific and engineering progress.
The journal will publish:

Research results that support building model-based definitions of materials and manufacturing processes that are compatible with model-based engineering design processes and multidisciplinary design optimization
Descriptions of novel experimental or computational tools or data analysis techniques, and their application, that are intended for use in ICME applications
Reports on efforts to develop and apply artificial intelligence and machine learning techniques that advance the development and deployment of materials and manufacturing processes within an ICME paradigm.
Best practices in verification and validation of computational tools, sensitivity analysis, uncertainty quantification, and data management, as well as standards and protocols for software integration and data exchange of importance to ICME
In-depth descriptions of data, databases, and database tools of high value to the materials and manufacturing communities
Detailed case studies on efforts, and their impact, that integrate experiment and computation to solve an enduring engineering problem in materials and manufacturing following the ICME discipline
IMMI highly encourages linking the key digital data supporting published articles to a publicly accessible data repository. Sufficient metadata must be included with datasets to adequately describe their provenance and enable reuse by others. Data associated with published articles should strive to meet FAIR data principles (https://www.go-fair.org/fair-principles/)

Support the publication of research on establishing model-based material and process definitions, new experiments or computational tools or data analysis techniques and their applications, detailed case studies, and in-depth descriptions of data, databases and database tools. Integrated materials and manufacturing innovations are highly encouraged to submit key data supporting published articles to a publicly accessible data repository. The data set must contain sufficient metadata, including uncertainty quantification, to fully describe its source.

Editor-in-Chief:

Charles Ward,University of Dayton, OH, USA

Board of Review:

John Allison, University of Michigan, Ann Arbor, MI, USA

Raymundo Arróyave, Texas A&M University, College Station, TX, USA

Rick Barto, Lockheed Martin, Cupertino, CA, USA

Bradford Cowles, Cowles Consulting LLC, East Hartford, CT, USA

Dennis Dimiduk, BlueQuartz Software LLC, Springboro, OH, USA

Steve Engelstad, Engelstad Consulting LLC, Atlanta, GA, USA

David Furrer, Pratt & Whitney, East Hartford, CT, USA

Somnath Ghosh, Johns Hopkins University, Baltimore, MD, USA

Gail Hahn, Boeing, Berkeley, MO, USA

Elizabeth Holm, Carnegie Mellon University, Pittsburgh, PA, USA

Surya Kalidindi, Georgia Institute of Technology, Atlanta, GA, USA

Paul Krajewski, General Motors Company, Warren, MI, USA

Peter Lee, University College London, London, UK

Mei Li, Ford Motor Company, Dearborn, MI, USA

Javier LLorca, IMDEA Materials Institute and Technical University of Madrid, Madrid, Spain

Paul Mason, Thermo-Calc Software Inc., McMurray, PA USA

David McDowell, Georgia Institute of Technology, Atlanta, GA, USA

Matthew Miller, Cornell University, Ithaca, NY, USA

Tresa Pollock, University of California Santa Barbara, Santa Barbara, CA, USA

Ulrich Prahl, TU Bergakademie Freiberg, Freiberg, DE

Dierk Raabe, Max-Planck Institut für Eisenforschung, Dusseldorf, DE

Georg Schmitz,Access e.V. at RWTH Aachen Univ, Aachen, DE

Taylor Sparks, University of Utah, Salt Lake City, UT, USA

Katsuyo Thornton, University of Michigan, Ann Arbor, MI, USA

James Warren, National Institute of Standards & Technology, Gaithersburg, MD, USA