A feature-based cost model framework for 3D woven composites

3D woven fabrics face many barriers to acceptance as composite materials, with lack of cost information a key reason. A potential application is wind turbine spar caps. Renewable energy generation is increasing while prices for wind turbine generated electricity are decreasing. To reduce Levelised Cost of Electricity (LCOE), either the entire turbine is replaced at end of life or new blade spar caps are retrofitted. Utilising the principle that cost increases with complexity, two parametric, resource-based Technical Cost Models were developed, one for 3D woven preforms and another for assessing the feasibility of retrofitting glass and carbon fibre spar caps. A relationship equating manufacturing time and 3D woven preform complexity, defined as a function of fibre tow number and preform shape, was introduced. Manufacturing time and therefore cost was found to scale with preform complexity for seventeen bespoke manufactured 3D woven preforms. There was good agreement between the cost of a preform estimated by the model and by a 3D woven fabric manufacturer. Two 3D preform sub-Models were derived for a Weavebird loom and a Jacquard loom. Organizational Learning can reduce preform cost. A spar cap cost curve from 35m to 75m length was derived from 35m spar cap cost data, with good agreement between model and manufacturer-estimated costs. LCOE decreased more for carbon fibre than for glass fibre 5-year life extended retrofits compared to a 20-year glass fibre baseline. To aid cost-effective 3D woven fabric design for various composite applications, an outline framework linking both models is proposed for assessing the economic feasibility of a 3D woven fabric for a given composite application. The model framework could incorporate appropriate learning curves with multivariate analysis so that the cost per preform of bespoke 3D woven fabrics manufactured at required production rates for mass customisation may be predicted with greater accuracy.