Mathematical Strategies for Design Optimization of Multiphase Materials

This work addresses various mathematical solution strategies adapted for design optimization of multiphase materials. The goal is to improve the structural performance by optimizing the distribution of multiple phases that constitute the material. Examples include the optimization of multiphase mate...

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Bibliographic Details
Published in:Mathematical problems in engineering 2019-01, Vol.2019 (2019), p.1-10
Main Authors: Tagle, Armani, Maier, Dominic, Diraz, Abdalla, Catania, Rick, Acar, Pınar
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Basis functions
Composite materials
Computational efficiency
Computing time
Design optimization
Design parameters
Engineering
Finite element method
Machine learning
Manufacturing
Mathematical models
Multiphase
Parameter sensitivity
Phase distribution
Radial basis function
Representations
Sensitivity analysis
Studies
Two phase materials
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Summary:This work addresses various mathematical solution strategies adapted for design optimization of multiphase materials. The goal is to improve the structural performance by optimizing the distribution of multiple phases that constitute the material. Examples include the optimization of multiphase materials and composites with spatially varying fiber paths using a finite element analysis scheme. In the first application, the phase distribution of a two-phase material is optimized to improve the structural performance. A radial basis function (RBF) based machine learning algorithm is utilized to perform a computationally efficient design optimization and it is found to provide equivalent results with the physical model. The second application concentrates on the optimization of spatially varying fiber paths of a composite material. The fiber paths are described by the Non-Uniform Rational Bezier (B)-Spline Surface (NURBS) using a bidirectional control point representation including 25 parameters. The optimum fiber path is obtained for various loading configurations by optimizing the NURBS parameters that control the overall distribution of fibers. Next, a direct sensitivity analysis is conducted to choose the critical set of parameters from the design point to improve the computational time efficiency. The optimized fiber path obtained with the reduced number of NURBS parameters is found to provide similar structural properties compared to the optimized fiber path that is modeled with a full NURBS representation with 25 parameters.
ISSN:1024-123X
1563-5147
DOI:10.1155/2019/4024637