Integrated Computational Materials Engineering: A Multi-Scale Approach

Computational materials science and engineering has a strong potential for developing robust simulation-based designs for optimizing materials, components, and engineering structures. Integrated computational materials engineering (ICME) provides practical solutions for engineering design tasks and has been recognized by the National Academy of Engineering (NAE Report 2008). The vital role of materials in design as well as the importance of computation in materials engineering necessitate understanding structure, properties, processing, and performance of materials and designed components. Thus, multiscale modeling has extended into solid mechanics, fluid mechanics, materials science, physics, mathematics, biology, and chemistry. ICME couples quantum scales with macroscales. It also allows the integration of materials processing and product performance for simulation-based manufacturing. These advances are analogous to transistor models that have led to complex logic operations and concurrent multilevel simulations. ICME can reduce product development time and costs by minimizing design cycles and facilitating cost-effective design optimization. These benefits have the potential to drive the utilization of multiscale modeling in various industrial sectors. The ‘‘integrated’’ part of ICME strongly emphasizes synergistic systems and interdisciplinary thinking accompanied by multiscale modeling methods. However, a key challenge is to link the various disciplines. Mathematics and statistics play a critical role and form a strong basis for modeling strategies. The terms ‘‘bottom-up’’ and ‘‘top-down’’ are of particular interest. The following characteristics are critical for these approaches: (I) length scales, (II) degrees of freedom and boundary conditions, (III) geometric and energy constraints, (IV) suitable numerical model implementation, (V) determining right sequence of calculations to be performed, (VI) determining variables and equations describing structure–property function, (VII) analyzing the cause–effect relationships using numerical methods, (VIII) verifying effects by experimental approaches before using the developed theory for next higher scales, and (IX) estimating the uncertainty in simulations and experiments to help develop a concrete multiscale modeling approach. This issue of JOM presents nine invited research and review articles. These articles discuss the importance of the ICME approach in refining conventional material and component processing methods as well as advanced materials processing. The article by Ghosh et al. discusses multiscale modeling of heterogeneous metals and alloys. They comprehensively address the problems related to hierarchical-concurrent multilevel or multiscale modeling methods for ductile fracture and nested dual-stage homogenization method for microstructure-based models for cast Al alloys. The two articles by Horstemeyer and colleagues detail the development and deployment of the modified embedded atom method (MEAM). The authors focus on the MATLAB-based MEAM potential calibration tool and interface with LAMMPS. The technique is used for studying aluminum system. To further define the ICME in ‘‘completeness,’’ the article by Liu et al. discusses case studies focused on three examples, where experiments are integrated with computation aspects. The modeling methods described include artificial neural networks, dislocation dynamics, and CALPHAD approach. Asadi et al. discuss the phase-field crystal model and multiscale analysis of solid–liquid structures. The article also compares various phase-field models for calculating solid–liquid interfaces. Bryan et al. demonstrate alloy design using the ICME-based Nitin Chopra is the Guest Editor for the ICME Committee of the TMS Materials Processing & Manufacturing Division, and coordinator of the topic Multi-Scale Modeling: Concurrent and Hierarchical Methods in this issue. JOM, Vol. 67, No. 1, 2015