Optimization of Composite Laminates for Robust and Predictable Progressive Failure Response

Abstract

Increasing robustness of structures with nonlinear history-dependent behavior requires their response to be very predictable. Predictability is made difficult by lack -of good physical models (e.g., material failure), inaccurate analysis models, and insufficient resolution used in the discretized solutions. In complex structures, the problem is compounded by the complex interactions between the various individual failure events that happen at different scales. The systems that are highly nonlinear and exhibit competing failure paths are sensitive to small design variations and exhibit poor failure predictability. Small design variations significantly alter the failure paths in designs having competing failure modes, reducing predictability. Progressive failure of a composite laminate resembles complex systems with many failing components (plies) and multiple failure modes (plies can fail by shear, matrix, and/or fiber failure) that exhibit problems in failure predictability. In this paper, we investigate the robustness of energy absorption and predictability of the failure sequence of composite laminates in progressive failure response. This investigation demonstrates that deterministic optimization makes predictability poor due to the coalescence in failure modes. A traditional reliability optimization was performed to improve failure predictability. Analyzing designs obtained revealed that robust and predictable progressive failure requires the elimination of competing modes. Strain separation between successive failure modes was identified as a surrogate deterministic measure to eliminate competing failures. A deterministic optimization for maximizing energy absorption with a constraint for strain separation between successive failures in different modes was performed. The deterministic design obtained with the surrogate measure for predictability was comparable to the nondeterministic design in performance and predictability, indicating that this is a sufficient condition for improving predictability. The paper demonstrates an approach for investigating the mechanics that affect progressive failure predictability and developing simple and efficient surrogate measures to use in deterministic optimization to maximize performance and predictability.