Abstract
Motivated by the ever-increasing wealth of data boosted by national strategies in terms of data-driven Integrated Computational Materials Engineering (ICME), Materials Genome Engineering, Materials Genome Infrastructures, Industry 4.0, Materials 4.0 and so on, materials informatics represents a unique strategy in revealing the fundamental relationships in the development and manufacturing of advanced materials. Materials developments are becoming ever more integrated with robust data-driven and data-intensive technologies. In the present review, big data-assisted digital twins (DTs) for the smart design and manufacturing of advanced materials are presented from the perspective of the digital thread. In the introduction of the DT design paradigm in the ICME era, the simulation aspects of DT and the data and design infrastructures are discussed. Referring to the simulation and theoretical factors of DTs, high-throughput simulation and automation and artificial intelligence-assisted multiscale atomistic modeling are detailed through several cases studies. With respect to data and data mining technologies, entropy and its application for attribute selection in decision trees are discussed to emphasize knowledge-based modeling, simulation and data analysis in machine learning coherently. Guided by the perspectives and case studies of the digital thread, we present our recent work on the design, manufacturing and product service via big data-assisted DTs for smart design and manufacturing by integrating some of these advanced concepts and technologies. It is believed that big data-assisted DTs for smart design and manufacturing effectively support better products with the application of novel materials by reducing the time and cost of materials design and deployment.
Highlights
With the dramatic development of advanced technologies, the ever-increasing wealth of data from computations and experiments is considered an essential component in the modern innovation ecosystem of materials[1,2,3,4,5,6,7]
Guided by the perspectives and case studies of the digital thread, we present our recent work on the design, manufacturing and product service via big data-assisted digital twins (DTs) for smart design and manufacturing by integrating some of these advanced concepts and technologies
The human in the HCPs highlights the significance of the knowledge base while the CPS indicates the important role of datadriven and data-intensive technologies, such as artificial intelligence, machine learning, data mining and so on
Summary
With the dramatic development of advanced technologies, the ever-increasing wealth of data from computations and experiments is considered an essential component in the modern innovation ecosystem of materials[1,2,3,4,5,6,7]. With respect to small data sets, decision strategies are developing into data-driven and computation-enabled approaches, the latter of which always combine robust and reliable codes and the availability of computing power to enable the application of pioneering technologies and support an alternative strategy for the discovery of advanced materials[12,75] Machine learning models, such as neural networks, excel at modeling complex data relationships but generally do not directly provide fundamental scientific insights, thereby motivating more efforts to analyze the models that identify the fundamental composition-property and composition-structure-property relationships. This digital twin process was used to systematically explore the microstructural evolution rules, establish constitutive and microstructural models of hot deformation and predict several fundamental properties, including average grain size, phase volume fraction, dislocation density and macro physical fields (i.e., stain and stress fields) during hot deformation[29]. It is noteworthy that both the time and the cost will be significantly reduced in the development of new types of rails from the China Railway High-speed to subways through the big data-assisted digital twin for the smart design and manufacturing routine
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