Many-objective design optimisation of a plain weave fabric composite

Plain weave fabrics provide low-cost composites used in many applications. Their mechanical properties are dependent on the weave and the yarn dimensions, which provides a complex design space to ensure optimal properties for a given application. Genetic Algorithms are commonly used in the literature to optimise the performance of composite materials but are currently limited to two or three objectives, where the optimisation may improve the specified properties but degrade others. In this paper 9 top performing Genetic Algorithms are benchmarked to find designs that respectively satisfy five-objective, three-objective and bi-objective formulations. The results show that the consideration of the five-objective problem is important, since the designs for the five-objective formulation give a wider range of results. These results do not include designs from the optimisation with the more limited objectives, meaning that these designs would need to be redesigned to be practical and demonstrating the benefits of optimisation with more objectives. cMLSGA is shown to be the strongest solver for these problems, contradicting the findings from the Evolutionary Computation literature. When compared with a current weave pattern, the five-objective optimisation provides 101 designs which improve all 5 material properties, with up to 76.61% improvements on the four mechanical properties and a maximum 37.73% reduction on areal density; there are weave patterns with designs that are specific to each of the properties individually.

Automated summary

Plain weave fabrics provide low-cost composites with mechanical properties dependent on weave and yarn dimensions. This paper benchmarks 9 top performing Genetic Algorithms to find designs that satisfy five-objective, three-objective and bi-objective formulations. The results highlight the importance of considering five-objective optimization and demonstrate the benefits of optimization with more objectives.