The design of a composite material structure is often challenging as it is driven by the trade-off between lightweight performance and production costs. In this paper, the boundaries of this design trade-off and its implications on material selection, geometrical design and manufacturability are analysed for a number of design strategies and composite material systems. The analysis is founded on a methodology that couples weight-optimization and technical cost modelling through an application-bound design cost. Each design strategy is evaluated for three levels of bending and torsional stiffness. The resulting stiffness-versus cost-range together constructs the design envelope and provides guidelines on the suitability and improvement potential of each case. Design strategies researched include monolithic, u-beam-, sandwich-insert- and sandwich-stiffened plates. Considered material systems include carbon-, glass, recycled carbon-, lignin- and hemp-fibre reinforced composites. Optimized sandwich designs are shown to have lowest design cost. Glass-, recycled carbon-, lignin- and hemp-fibre reinforced composite materials are all shown to reduce costs but at lower stiffness performance. Ultimately, the case study demonstrates the importance of early structural design trade-off studies and material selection and justifies introducing novel fibre systems in low-cost applications of moderate stiffness levels.

The intensified pursuit for lightweight solutions in the commercial vehicle industry increases the demand for method development of more advanced lightweight materials such as Carbon-Fiber-Reinforced Composites (CFRP). The behavior of these anisotropic materials is challenging to understand and manufacturing defects could dramatically change the mechanical properties. Voids are one of the most common manufacturing defects; they can affect mechanical properties and work as initiation sites for damage. It is essential to know the micromechanical composition of the material to understand the material behavior. Void characterization is commonly conducted using optical microscopy, which is a reliable technique. In the current study, an approach based on optical microscopy, statistically characterizing a CFRP laminate with regard to porosity, is proposed. A neural network is implemented to efficiently segment micrographs and label the constituents: void, matrix, and fiber. A neural network minimizes the manual labor automating the process and shows great potential to be implemented in repetitive tasks in a design process to save time. The constituent fractions are determined and they show that constituent characterization can be performed with high accuracy for a very low number of training images. The extracted data are statistically analyzed. If significant differences are found, they can reveal and explain differences in the material behavior. The global and local void fraction show significant differences for the material used in this study and are good candidates to explain differences in material behavior.

Fatigue strength dictates life and cost of welded structures and is often a direct result of initial manufacturing variations and defects. This paper addresses this coupling through proposing and applying the methodology of predictive life-cycle costing (PLCC) to evaluate a welded structure exhibiting manufacturing-induced variations in penetration depth. It is found that if a full-width crack is a fact, a 50% thicker design can result in life-cycle cost reductions of 60% due to reduced repair costs. The paper demonstrates the importance of incorporating manufacturing variations in an early design stage to ensure an overall minimized life-cycle cost.