Multidisciplinary Optimization of Composite Parts

Sonaca and Cenaero have joined their expertise to develop a numerical tool for multidisciplinary optimization of composite parts. The tool consists in a fully automatic optimization chain integrating high-fidelity... [ view full abstract ]

Sonaca and Cenaero have joined their expertise to develop a numerical tool for multidisciplinary optimization of composite parts. The tool consists in a fully automatic optimization chain integrating high-fidelity computational functions (such as Finite Element Analysis). Two levels of optimization are possible: on one hand, a design optimization minimizing the weight while respecting all design rules (stacking, geometry, etc) and structural integrity (BVID, fasteners and buckling) ; on the other hand, a manufacturing optimization with optimal cutting and nesting of the plies to minimize material attrition rate, while respecting all manufacturing rules (plies overlaps, stagger, etc). In addition to existing tools, such as Sonaca in-house FEA chain (SONIA) and Cenaero in-house optimizer (Minamo), several specific numerical tools have been developed and integrated in the computational chain, such as a stacking sequence generator, a ply cutting tool and a cost model. The optimization tool is based on a hierarchical parameterization approach and surrogate models that enable to converge rapidly to optimized solutions. It can manage a very large design space with the combination of discrete and continuous parameters.

The tool has been validated with an in-production composite flap structure. Two consecutive optimizations were performed. First, a design optimization with 42 variables (number of plies and percentage of ±45° plies in 21 zones) has led to a weight reduction of 9 % while respecting all design rules and structural integrity criteria. Second, a cost optimization with 162 variables (locations of plies cut) has led to a material cost reduction of 7% and a total cost reduction, including material and labor, of 2%. If combined with the weight optimization, the total cost reduction would be 11%. This first complex realistic benchmark has proved the tool efficiency. A combination of both design and manufacturing optimization loops could yield even higher benefits.