This course will be an opportunity to experiment with various machine learning tools and algorithms available in Owl. It will start with a lecture explaining the current and the past problems of AI in design. The presentation will introduce the notions of supervised, unsupervised and reinforcement learning. Using Owl, the participants will then learn the Grasshopper-based workflow for neural networks (supervised learning). Introduction will end with an overview of Owl components used for data pre- and post-processing.
After the introduction, the focus will be put on the group projects and the ways to utilise ML to aid the designs either in sensing/responding, controlling and predicting. Neural networks will be used for both regression and classification problems. The students will learn how to understand and control the learning process through hyperparameters.
Over the course of the semester this knowledge shall be extended to application of ANN in the students’ projects.
During this course the students will be challenged to incorporate artificial neural network models either for sensing/response, control or prediction.
To be able to work efficiently with the NN models, the students will have to:
Aim: To predict the number of seating spaces based on various types of seating layout, number of corridors and dimensions of generative enclosed rectangular spaces such as an auditorium. Data Structure: Prediction Comparison between normal (linear) regression and XGB regression Prediction Comparison between Kernel Ridge regression and XGB regression Conclusion:
The project’s objective was to create a machine-learning model capable of classifying repurposed timber components within an assembly process as either structural or non-structural, using factors such as defect quantity, age, and exposure to weather conditions as input. Dataset Generation and Analysis The dataset was produced using Roboflow by utilizing scans of the timber elements … Read more
Our aim is to develop a machine learning model that accurately predicts the color outcome of ceramic underglazes based on their ingredient compositions and firing conditions. In the world of ceramic art, the process of underglazing involves applying colors to pottery which are then sealed under a transparent glaze before firing. However, predicting the final … Read more
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
Our project involves the fabrication of a curved surface using cork panels that have been discretized into unique four-sided shapes. Our previous approach involved cutting each panel individually, but we aim to streamline the process by using machine learning to classify the panels into five distinct groups. We will then design and create a mold … Read more
