Predictive Modelling for Mechanical Behaviour (PMMB) of Materials
A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Materials Simulation and Design".
Deadline for manuscript submissions: closed (20 April 2022) | Viewed by 8702
Special Issue Editor
Special Issue Information
Dear Colleagues,
Predictive modelling for mechanical behaviour (PMMB) of materials is a widely accepted method for virtual prototyping for materials design and application performance analysis for parts and components. The relatively cheap availability of computing power makes it possible for this method to provide inexpensive guidance and evaluation of performance prior to materials manufacture and testing. PMMB modelling covers considerations of fundamentals such as interatomic forces and dislocation effects, as well as constitutive and empirical relations to predict linear and nonlinear properties and response of materials under general thermomechanical loading. The methods of analysis include classical and quantum mechanics approaches. At micro and macro-levels, nonlinear response analyses are often carried out using finite difference, boundary element, finite volume and predominantly finite element methods. Artificial intelligence machine learning methods have found a lot of applications lately, especially for empirical description of materials behaviour and likely performance. Materials of interest in this collection include polymers, metal alloys, ceramics and composites made from these materials. Predictive response analysis of interest includes elastic, plastic and viscoplastic deformation under general thermomechanical loading. Measures of failure cover limits of ductility, creep, yield, strength, fatigue and fracture. Also of interest is quantitative characterization of the influence of composition, processing, heat treatment and mechanical working on properties and behaviour. The coverage of composite materials includes mean field theory and classical laminate analysis methods.
This Special Issue welcomes review and state-of-the-art predictive modelling articles covering the material types and methods of analysis highlighted. The issue will serve as a helpful reference to designers, engineers and researchers with interest in sustainable design and application of materials in general.
Prof. Dr. John Durodola
Guest Editor
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Keywords
- materials modelling
- machine learning
- elasticity
- creep
- strength
- ductility
- fracture
- classical and quantum mechanics
- processing
- treatment
- linear and nonlinear materials response plasticity
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