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Nicolas Sbrugnera Sotomayor
Department of Industrial Engineering, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano, Italy

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Journal article
Published: 19 July 2021 in Applied Sciences
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In the last few decades, complex light-weight designs have been successfully produced via additive manufacturing (AM), launching a new era in the thinking–design process. In addition, current software platforms provide design tools combined with multi-scale simulations to exploit all the technology benefits. However, the literature highlights that several stages must be considered in the design for additive manufacturing (DfAM) process, and therefore, performing holistic guided-design frameworks become crucial to efficiently manage the process. In this frame, this paper aims at providing the main optimization, design, and simulation tools to minimize the number of design evaluations generated through the different workflow assessments. Furthermore, DfAM phases are described focusing on the implementation of design optimization strategies as topology optimization, lattice infill optimization, and generative design in earlier phases to maximize AM capabilities. In conclusion, the current challenges for the implementation of the workflow are hence described.

ACS Style

Nicolas Sbrugnera Sotomayor; Fabrizia Caiazzo; Vittorio Alfieri. Enhancing Design for Additive Manufacturing Workflow: Optimization, Design and Simulation Tools. Applied Sciences 2021, 11, 6628 .

AMA Style

Nicolas Sbrugnera Sotomayor, Fabrizia Caiazzo, Vittorio Alfieri. Enhancing Design for Additive Manufacturing Workflow: Optimization, Design and Simulation Tools. Applied Sciences. 2021; 11 (14):6628.

Chicago/Turabian Style

Nicolas Sbrugnera Sotomayor; Fabrizia Caiazzo; Vittorio Alfieri. 2021. "Enhancing Design for Additive Manufacturing Workflow: Optimization, Design and Simulation Tools." Applied Sciences 11, no. 14: 6628.