The Master in Robotics and Advanced Construction (MRAC) seeks to train a new generation of interdisciplinary professionals who are capable of facing our growing need for a more sustainable and optimised construction ecosystem. The Master is focused on the emerging design and market opportunities arising from novel robotic and advanced manufacturing systems.
Through a mixture of seminars, workshops, and studio projects, the master programme challenges the traditional processes in the Construction Sector. It investigates how advances in robotics and digital fabrication tools change the way we build and develop processes and design tools for such new production methods.
Limitless Project
The industry of construction creates most of the waste in the world. By 2025 we expect to reach 2.2 billion tons globally. As a solution of this problem, industry 4.0 proposed the Additive manufacturing process, which is looking for the optimization of fabrication and construction adding layer by layer different materials; this process started being … Read more
PRINT POINT:
The reference marking system for Additive Manufacturing Context Additive manufacturing (AM) technologies have established and grown significantly over the past few decades, solidifying themselves as a mature and competitive alternative across various sectors of fabrication and construction. This development has been observed across multiple levels and applications of production, primarily due to its remarkable properties … Read more
Print – Scan – Cut
Preliminary steps towards a hybrid workflow for smooth 3D printed surfaces. Aim To develop a hybrid workflow to obtain smooth surfaces on 3D printed clay for improved alignment and surface finish. State of the Art Additive manufacturing enables the precise and cost-effective creation of complex designs while minimizing waste. One of the shortcomings common to … Read more
Machine Learning for the Prediction of Clay Deformation
Optimizing 3D Printing with AI prediction Introduction In this seminar we applied the concepts of machine learning to estimate the deformation during drying of simplified 3D printed clay objects. To do this, we trained an artificial neural network (ANN) to predict the movement of discrete points within the printed object. This project is very relevant … Read more
