Engineering (Jun 2020)

DID Code: A Bridge Connecting the Materials Genome Engineering Database with Inheritable Integrated Intelligent Manufacturing

  • William Yi Wang,
  • Peixuan Li,
  • Deye Lin,
  • Bin Tang,
  • Jun Wang,
  • Quanmei Guan,
  • Qian Ye,
  • Haixing Dai,
  • Jun Gao,
  • Xiaoli Fan,
  • Hongchao Kou,
  • Haifeng Song,
  • Feng Zhou,
  • Jijun Ma,
  • Zi-Kui Liu,
  • Jinshan Li,
  • Weimin Liu

Journal volume & issue
Vol. 6, no. 6
pp. 612 – 620

Abstract

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A data identifier (DID) is an essential tag or label in all kinds of databases—particularly those related to integrated computational materials engineering (ICME), inheritable integrated intelligent manufacturing (I3M), and the Industrial Internet of Things. With the guidance and quick acceleration of the development of advanced materials, as envisioned by official documents worldwide, more investigations are required to construct relative numerical standards for material informatics. This work proposes a universal DID format consisting of a set of build chains, which aligns with the classical form of identifier in both international and national standards, such as ISO/IEC 29168-1:2000, GB/T 27766–2011, GA/T 543.2–2011, GM/T 0006–2012, GJB 7365–2011, SL 325–2014, SL 607–2018, WS 363.2–2011, and QX/T 39–2005. Each build chain is made up of capital letters and numbers, with no symbols. Moreover, the total length of each build chain is not restricted, which follows the formation of the Universal Coded Character Set in the international standard of ISO/IEC 10646. Based on these rules, the proposed DID is flexible and convenient for extending and sharing in and between various cloud-based platforms. Accordingly, classical two-dimensional (2D) codes, including the Hanxin Code, Lots Perception Matrix (LP) Code, Quick Response (QR) code, Grid Matrix (GM) code, and Data Matrix (DM) Code, can be constructed and precisely recognized and/or decoded by either smart phones or specific machines. By utilizing these 2D codes as the fingerprints of a set of data linked with its cloud-based platforms, progress and updates in the composition–processing–structure–property–performance workflow process can be tracked spontaneously, paving a path to accelerate the discovery and manufacture of advanced materials and enhance research productivity, performance, and collaboration.

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