Integrating Materials and Manufacturing Innovation (IMMI)

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.

Integrating Materials and Manufacturing Innovation (IMMI) is :-

• International, Peer-Reviewed, Open Access, Refereed, Surfaces, Coatings and Films, Surfaces and Interfaces, Industrial and Manufacturing Engineering, Materials Science, Manufacturing Engineering, Materials Informatics, Digital Manufacturing, Process Innovation, materials informatics, integrated computational materials engineering, additive manufacturing, process modelling, data driven materials development, digital twins, advanced characterization techniques , Online , Continuously Journal

• UGC Approved, ISSN Approved: P-ISSN E-ISSN: 2193-9772, Established: 2012, Impact Factor: 2.5
• Does Not Provide Crossref DOI

• Not indexed in Scopus, WoS, DOAJ, PubMed, UGC CARE