A D V A N C E D M A T E R I A L S & P R O C E S S E S | J U L Y / A U G U S T 2 0 2 1 1 8 The network of experts, adjacent organizations, working groups, and project teams that will be part of advancing FAIR principles and mitigating PEST challenges highlights the most important driver of real advancement in Materials 4.0 and democratizing digital capabilities—people! Many forms of structured and nonstructured collaboration will enable the movement toward FAIR, including focused project teams, consortia, and communities of practice. The challenges are real, and big, but not insurmountable. Creating such an environment for productive collaboration will require close partnerships between industry, government, academia, SDOs, and technical professional societies. SUMMARY The potential significance of FAIR data is hard to overstate, but equally hard to prove in totality. FAIR data can change the paradigm for research and development, qualfication/certification, and acquisition, sustainment. We can predict the benefits by extrapolating the needs of today, but we expect the real benefit to be unimaginable. The internet has taught us that the availability of knowledge leads to impactful but unpredictable advancements. However, these advancements rely on network effects and require a critical number of users. A concentrated effort is needed to prime the AM FAIR data pump. It needs to begin addressing the PEST challenges by proving a tangible value of FAIR data while demonstrating a model for rapid technical development of the AM FAIR data infrastructure. This must be accomplished in a way that builds community acceptance among industrial users, government users, and database management providers. Most importantly, this progress needs to attract influential early adopters of FAIR AM data. ~AM&P For more information: William E. Frazier, FASM, Pilgrim Consulting LLC, Lusby, MD, frazierwe@pilgrim- consulting.com, https://www.pilgrim- consulting.com/. References 1. J. Soldatos, et al., Industrie 4.0 Deep Dive, (Wevolver, Amsterdam), https://wevolver-project-images. s3-us-west-1.amazonaws.com/ Indust r ie_4.0_deep_dive.pdf. [Accessed March 17, 2021]. 2. H. van Vlijmen, et al., The Need of Industry to Go FAIR, Data Intelligence, 2, p 276-284, 2020, Doi: 10.1162/dint_a_00050. 3. R. Jose and S. Ramakrishna, Materials 4.0: Materials Big Data Enabled Materials Discovery, Applied Materials Today, 10, p 127-132, 2018. 4. Vision 2040: A Roadmap for Integrated Modeling of Materials and Fig. 5 — ASM International envisions Materials Data Management Ecosystem. Companies listed are examples only. TABLE 2 – LIST OF ACRONYMS Acronym Description AM Additive manufacturing ANOVA Analysis of variance CAD Computer-aided design CAM Computer-aided manufacturing CDD Common data dictionary CDEF Common data exchange format FAIR Findable, accessible, interoperable, and reusable FEM Finite element methodology ICME Integrated computational materials engineering JASON JavaScript object notation KSA Knowledge, skills, and abilities MatML Materials extensible markup language NoSQL Nonstructured query language OWL Web ontology language PEST Political, economic, social, and technological PLM Product lifecycle management RDF Resource description framework SDO Standards development organizations WWW World wide web XML Extensible markup language
RkJQdWJsaXNoZXIy MTYyMzk3NQ==