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Machine Learning-Based Parametric Design Workflow for Free-Form Surface Classification
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DOI：10.3390/app16104768.png)
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Abstract
En 中文
While the demand for free-form architecture (FFA) has increased with advancements in computer-aided design (CAD) technology, the rationalization of complex surfaces into fabricable panels remains a significant challenge due to high production costs and technical complexity. Practical pain points, such as the prohibitive cost of unique molds and the inefficiency of manual data processing during design iterations, pose substantial economic risks. This study proposes an intelligent surface rationalization framework that integrates parametric design with machine learning algorithms in AutodeskTM Dynamo Studio, a plug-in to Revit. A data-driven classification workflow was developed using four key geometric parameters-planarity, principal curvature (PC), Gaussian curvature (GC), and mean curvature (MC). Two unsupervised learning algorithms, a Gaussian mixture model and K-means clustering, were compared for their classification performance. As a result of two case studies, free-form surface classification by a Gaussian mixture model (CGMM) demonstrated flexibility in modeling complex surface data by probabilistically managing the uncertainty of the curvature distribution, and free-form surface classification by K-means clustering (CKC) was confirmed to be effective for the rapid classification of large-scale panel data. Optimizing the proportion of flat and single-curved panels through the proposed workflow contributes to deriving a reasonable balance between design intent and construction costs/constructability at the early design stage, and strengthening risk management capabilities for FFA.
Keywords:
free-form architecture
classification
machine learning
parametric design