Abstract
Thanks to the application of the Industry 4.0 paradigm, contemporary factories consist of flexible production lines that can generate countless product variations without substantial increases in production costs. This study highlighted a scientific and technological gap between the flexible manufacturing system and the design system adopted to make products. In fact, commonly used CAD design technologies are static and do not allow the generation of dynamic and variable designs, causing the need to redesign models in whole or in part in order to realize variations in the generated shapes. In this paper, an algorithm-based generative design methodology oriented to the flexible manufacturing paradigm is proposed. This design approach, based on parametric modeling in Grasshopper, allows countless geometric variations of a product to be automatically generated while returning input CAD files for CNC machines. Specifically, the proposed design approach was tested by making two applications for the wood furniture industry; the output obtained in the case studies consists of a generative and parametric algorithm. The generative system provides a file for advanced manufacturing systems; in the case study, a numerically controlled laser cutter, one of the most popular machines for making flat panels from wood and metal, was chosen. The results obtained showed how the algorithmic design approach is of great importance in order to ensure customized production without substantial cost increases. This is made possible through algorithmic design automation and contemporary manufacturing technologies.
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This research was granted by Next Generation UE - PNRR Tech4You Project funds assigned to University of Calabria (PP1.4.2 - Process of chaotic design, Scientifc Coordinator: Professor Pietro Pantano).
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Demarco, F., Bertacchini, F., Bilotta, E., Scuro, C., Pantano, P. (2025). Algorithms for Design with CNC Machines: The Case Study of Wood Furniture. In: Sergeyev, Y.D., Kvasov, D.E., Astorino, A. (eds) Numerical Computations: Theory and Algorithms. NUMTA 2023. Lecture Notes in Computer Science, vol 14478. Springer, Cham. https://doi.org/10.1007/978-3-031-81247-7_22
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