Intelligent material and design correlation methodology towards wheel caster systems under realistic operational conditions

拟议的研究的目的是了解现实的负载和边界条件下的弹性体的基本力学。这包括材料、功能和设计与弹性体性能和耐久性之间的相关性，这在文献和行业中是独一无二的，而且非常有限。这将通过制定一个全面的测试计划和数据的弹性体行为在几个负载和环境的情况下实现。将开发统计和机器学习方法和算法，以合理选择建模的有效设计变量，并预测弹性体系统的性能，同时考虑材料性能、工艺处理和几何规格的影响。这种智能材料和设计相关方法将作为工程设计的支柱，并有助于开发，选择和优化新的弹性体材料和针对特定任务调整的设计，从而使广泛的应用受益。因此，这一工具将是知识体系的一个附加值，并为加拿大工业的创新工程提供一个有效的设计工具。

The objective of the proposed study is to understand the underlying mechanics of elastomers under realistic loading and boundary conditions. This includes the correlation between material, functionality and design with elastomers performance and durability which is unique and very limited in the literature and industry. This will be achieved through developing a comprehensive test plan and data of elastomer behaviour under several loading and environmental scenarios. Statistical and machine learning methodologies and algorithms will be developed to rationally select the effective design variables for modeling and to predict the performance of elastomer systems, considering the effect of material properties, process treatment and geometrical specifications. This intelligent material and design correlation methodology would serve as a backbone of the engineering design and aid in the development, selection and optimization of new elastomer materials and designs tuned towards specific tasks, benefiting a wide range of applications. This tool would, therefore, be an added value to the body of knowledge and offer an effective design tool towards innovative engineering to the Canadian industry.