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Combining additive manufacturing (AM) with carbon fiber reinforced polymer patched composites unlocks potentials in the design of individualized, lightweight biomedical structures. Arising design opportunities are geometrical individualization of structures using the design freedom of AM and the patient-individual design of the load-bearing components employing carbon fiber patch placement. To date, however, full exploitation of these opportunities is a complex recurring task, which requires a high amount of knowledge and engineering effort for design, optimization, and manufacturing. The goal of this study is to make this complexity manageable by introducing a suitable manufacturing strategy for individualized lightweight structures and by developing a digitized end-to-end design process chain, which provides a high degree of task automation. The approach to achieve full individualization uses a parametric model of the structure which is adapted to patients’ 3D scans. Moreover, patient data is used to define individual load cases and perform structural optimization. The potentials of the approach are demonstrated on an exoskeleton hip structure. A significant reduction of weight compared to a standard design suggests that the design and manufacturing chain is promising for the realization of individualized high-performance structures.

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Numerical optimization is an indispensable part of the design process of laminated composite structures. Several optimality criteria-based algorithms exist which rely on a sequential resizing and scaling approach. This paper presents a novel design algorithm applicable for stiffness and eigenfrequency optimization of composite structures with concurrent consideration of resizing and scaling operations. A method is introduced that allows for an efficient consideration of nonlinear constraints. This is done by determining stable concurrent scaling parameters from first-order constraint change ratio estimations. Optimization is carried out using optimality criteria in three independent steps, namely with respect to fiber angles, ply thickness ratios, and total laminate thickness. Sensitivity analyses are performed analytically at low computational costs. Numerical examples demonstrate the efficiency and fast convergence of the method. Compared to established algorithms, the number of required function evaluations is reduced significantly.