Enhancing and Supporting Integrated Computational Material Science Engineering Education

In this paper we describe a novel approach for teaching a multi-disciplinary course “Integrated Computational Materials Engineering (ICME) for Metals” aimed to support the generation of future taskforce of engineers. By combining traditional teaching of the theoretical concepts of the ICME paradigm (based on a textbook) with in-class practical training sessions using the resources accessible online through ICME Cyberinfrastructure (CI), the students are motivated to work in dynamic, shared, and collaborative learning environment while learning and utilizing the state-of-art, high-performance computational tools. This course was taught as a part of Fall 2012 and 2013 graduate coursework of Mechanical Engineering Department at Mississippi State University. The paper discusses the rationale for the course, the course description, the grading procedures, and survey-based course assessments. The surveys showed that the students’ reaction to the class was very positive. The impact of this course was evident in students learning outcomes that were published online on ICME Wiki. The majority of the students were awarded the top grade for the class, reflecting their performance, interest and effort.

[1]  Hussein M. Zbib,et al.  3D dislocation dynamics: stress–strain behavior and hardening mechanisms in fcc and bcc metals , 2000 .

[2]  A. Gokhale Collaborative Learning Enhances Critical Thinking , 1995 .

[3]  M. Baskes,et al.  Embedded-atom method: Derivation and application to impurities, surfaces, and other defects in metals , 1984 .

[4]  Ghodrat Karami,et al.  MULTISCALE DISLOCATION DYNAMICS PLASTICITY , 2003 .

[5]  Mark F. Horstemeyer,et al.  Integrated Computational Materials Engineering (ICME) for Metals: Using Multiscale Modeling to Invigorate Engineering Design with Science , 2012 .

[6]  Modeling of Anisotropic Damage for Ductile Materials in Metal Forming Processes , 2004 .

[7]  R. Dreizler,et al.  Density-Functional Theory , 1990 .

[8]  Charles H. Ward Materials Genome Initiative for Global Competitiveness , 2012 .

[9]  E. Kaxiras,et al.  Generalized-stacking-fault energy surface and dislocation properties of aluminum , 1999, cond-mat/9903440.

[10]  Mark F. Horstemeyer,et al.  Historical review of internal state variable theory for inelasticity , 2010 .

[11]  A. Becke,et al.  Density-functional exchange-energy approximation with correct asymptotic behavior. , 1988, Physical review. A, General physics.

[12]  Mark F. Horstemeyer,et al.  Cradle-to-grave simulation-based design incorporating multiscale microstructure-property modeling: Reinvigorating design with science , 2003 .

[13]  Mark F. Horstemeyer,et al.  Multiscale Modeling: A Review , 2009 .

[14]  John E. Allison,et al.  Integrated computational materials engineering: A perspective on progress and future steps , 2011 .

[15]  M. Baskes,et al.  Modified embedded-atom potentials for cubic materials and impurities. , 1992, Physical review. B, Condensed matter.

[16]  Nitin Sukhija,et al.  Cyberinfrastructure Support for Engineering Virtual Organization for CyberDesign , 2011, PPAM.

[17]  Mark F. Horstemeyer,et al.  A general inelastic internal state variable model for amorphous glassy polymers , 2010 .

[18]  M. Shukla,et al.  Practical Aspects of Computational Chemistry III , 2014 .

[19]  W. T. Read,et al.  Multiplication Processes for Slow Moving Dislocations , 1950 .

[20]  Sherri L. Jackson Research Methods: A Modular Approach , 2007 .

[21]  David L. McDowell,et al.  Microstructure-based fatigue modeling of cast A356-T6 alloy , 2003 .

[22]  Curtis J. Bonk,et al.  The Handbook of Blended Learning: Global Perspectives, Local Designs , 2005 .