Computational design methodology for additive manufacturing to enhance customised products and process management efficiency

■ Design automation to eliminate bottlenecks derived from noncontinuous data flows. It is especially useful in personalized products and enables efficient mass customization scenarios. ■ Integration of multidisciplinary algorithms such as decision support, machine learning or finite element analysis for open design efficient systems for optimized solutions for AM. ■ Direct machine code generation from the design process without the need for mesh export to AM formats, such as Stereolithographic (STL) or Additive Manufacturing Format (AMF). ■ Metadata that can be generated and available throughout the design, manufacturing and inspection process are integrated. Consequently, part manufacturing can be optimized with multidisciplinary analysis through the integration of both machine code generation and design processes in an automated workflow. ■ Integration of the different phases involved in a knowledge-based framework in a unique algorithm that enables multidisciplinary optimizations for enhanced AM designs that are automatically generated and adapted to personal needs. The emergence of Additive Manufacturing (AM) provides new horizons for innovation and optimization of designs. However, the limited knowledge of Design for Additive Manufacturing (DfAM) and the constraints imposed by conventional DfAM frameworks are hindering the potential of AM. The scope of recent studies, focused on algorithmic approaches, is limited to certain aspects of the problem and does not offer a comprehensive solution to generate efficient custom designs. To overcome these limitations, the role of interdisciplinary and transversal DfAM methodologies is essential. This work presents a DfAM methodology based on computational design with a continuous dataflow for the integration of all the design phases of a knowledge-based DfAM framework in a single algorithm, from initial design stages to machine code generation, what fully exploits the potential of additive technologies in customized products. This facilitates the integration of key data relationships and connections, promoting the development of intelligent and efficient open systems. Firstly, a bibliographic review on actual knowledge-based DfAM framework is exposed. Then, the proposed methodology is detailed and discussed. Furthermore, its potential applied to customized parts with specific mechanical requirements is validated through a splint design case study. Finally, the proposed methodology advantages and limitations are discussed.