This thesis investigates the exploration of advanced fabrication methodologies for complex, free-form shading elements in building facades. Emphasizing the integration of algorithmic programming, robotics, and augmented reality (AR), we propose a comprehensive digital workflow that unifies design, fabrication, and performance analysis. Our research focuses on utilizing state-of-the-art robotic fabrication techniques and AR to achieve precision and efficiency in the production of intricate facade components. Additionally, machine learning models are developed to optimize the daylight performance of these shading elements, enhancing their environmental impact. Through detailed case studies and practical implementations, we demonstrate that employing advanced technological methods in fabrication not only streamlines the design-to-fabrication pipeline but also significantly enhances the adaptability, sustainability, and performance of architectural facades. This work lays a robust foundation for future research and practice in the innovative and performance-driven design of complex architectural systems.
Questa tesi indaga l’esplorazione di metodologie avanzate di fabbricazione per elementi ombreggianti complessi e di forma libera nelle facciate degli edifici. Ponendo l’accento sull’integrazione della programmazione algoritmica, della robotica e della realtà aumentata (AR), proponiamo un workflow digitale completo che unifica progettazione, fabbricazione e analisi delle prestazioni. La nostra ricerca si concentra sull’utilizzo di tecniche avanzate di fabbricazione robotica e AR per ottenere precisione ed efficienza nella produzione di componenti intricati delle facciate. Inoltre, vengono sviluppati modelli di apprendimento automatico per ottimizzare le prestazioni di illuminazione diurna di questi elementi ombreggianti, migliorando il loro impatto ambientale. Attraverso studi di caso dettagliati e implementazioni pratiche, dimostriamo che l’impiego di metodi tecnologici avanzati nella fabbricazione non solo snellisce il processo dalla progettazione alla produzione, ma migliora significativamente anche l’adattabilità, la sostenibilità e le prestazioni delle facciate architettoniche. Questo lavoro pone una solida base per la ricerca futura e la pratica nella progettazione innovativa e orientata alle prestazioni di sistemi architettonici complessi.
Exploring Advanced Design and Fabrication Methodologies For a Freeform Shading Element Through Algorithmic Programming, Robotics and Machine Learning
Demissie, Mahlet Argaw;Oteuil, Alimzhan;Cheragh Nia, Zahra
2023/2024
Abstract
This thesis investigates the exploration of advanced fabrication methodologies for complex, free-form shading elements in building facades. Emphasizing the integration of algorithmic programming, robotics, and augmented reality (AR), we propose a comprehensive digital workflow that unifies design, fabrication, and performance analysis. Our research focuses on utilizing state-of-the-art robotic fabrication techniques and AR to achieve precision and efficiency in the production of intricate facade components. Additionally, machine learning models are developed to optimize the daylight performance of these shading elements, enhancing their environmental impact. Through detailed case studies and practical implementations, we demonstrate that employing advanced technological methods in fabrication not only streamlines the design-to-fabrication pipeline but also significantly enhances the adaptability, sustainability, and performance of architectural facades. This work lays a robust foundation for future research and practice in the innovative and performance-driven design of complex architectural systems.File | Dimensione | Formato | |
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2024_07_Cheragh Nia_Demissie_Oteuil.pdf
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Descrizione: This thesis investigates the exploration of advanced fabrication methodologies for complex, free-form shading elements in building facades. Emphasizing the integration of algorithmic programming, robotics, and augmented reality (AR), we propose a comprehensive digital workflow that unifies design, fabrication, and performance analysis. Our research focuses on utilizing state-of-the-art robotic fabrication techniques and AR to achieve precision and efficiency in the production of intricate facade components. Additionally, machine learning models are developed to optimize the daylight performance of these shading elements, enhancing their environmental impact. Through detailed case studies and practical implementations, we demonstrate that employing advanced technological methods in fabrication not only streamlines the design-to-fabrication pipeline but also significantly enhances the adaptability, sustainability, and performance of architectural facades. This work lays a robust foundation for future research and practice in the innovative and performance-driven design of complex architectural systems.
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https://hdl.handle.net/10589/223796