Handcrafting Objects made with Machine Learning: An Object Design Approach with Computer Vision

Abstract

Many of today’s computational design systems based on explicit or graphic programming software require designers to determine relationships for morphogenesis based on computational thinking supported by the abstraction process. This computational thinking process can reduce the ability to generate analogies in design development and adverse vision related to computational tools. It also reduces the innovation capacity of small companies that produce handicrafts and design teaching in a customized way. This research promotes a computational model based on machine learning combined with an analog creation process. Machine learning engines determine the objects similarity percentage between students’ objects and master objects through a forecasting model. There is a proposal to combine parametric design systems, such as Grasshopper3D, with cloud computing and an edge computing device.

Keywords

• edge computing
• abstraction
• machine learning
• ceramics
• SDG 1
• SDG 9