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    Integrating Robotic Fabrication into the Basic Design Studio
    (Kim Williams Books, 2023) Yazar, Tugrul; Oral-Karakoc, Hulya; Gunduz, Gamze; Yabanigul, Meryem N.
    Digital fabrication technologies are revealing new ways of dealing with design processes. Robotic fabrication technologies are generally dismissed at the undergraduate level, especially in first-year design education. This is due to the common belief that novice design students have insufficient skills for designing with a robotic arm. This paper presents an experiment that took place in a first-year basic design studio at a faculty of architecture. The studio investigates the utilization of a robotic arm as a design and production tool. Students without robot programming and operation skills were encouraged to transfer a priori skills of hand tools and techniques learned in successive assignments to utilize the robotic arm. This experiment revealed the educational potential strategies for the integration of robot technology in first-year design studios.
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    Production of Gyroid-like modular systems with non-linear robotic hotwire cutting
    (Elsevier, 2021) Yabanigul, Meryem N.; Yazar, Tugrul
    Robotic arms are being used by construction firms and schools of architecture around the world in design/build researches and material studies. Robotic hotwire cutting (RHC) is a commonly used technique in these studies. This technique has limitations on the production of free-form objects. This paper addresses this limitation by proposing an alternative non-linear robotic hotwire cutting (NL-RHC) workflow. The original contribution of this workflow is the usage of a shape memory alloy (SMA) as a cutting wire, that enables the production of curved forms. The workflow was tested on the production of a Gyroid-like modular system to reveal its advantages and limits. Several attempts were made to find the most accurate approximation of the Gyroid. The result suggested that NL-RHC would be an efficient solution for the production of curved surfaces if the SMA is trained and the robot path is calculated correctly