Self-evolving experimental platform for 3D sand printing

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ID: 316455
2026
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Abstract
Abstract 3D sand printing revolutionizes casting mold fabrication while confronting nonlinear high-dimensional optimization challenges stemming from variable combinations and objective conflicts. Here, we develop Ex3DSP, a self-evolving experimental platform combining robotic ‘can-do’ capabilities with artificial intelligence ‘can-think’ cognition to establish a ‘do-while-thinking’ self-evolving experimentation paradigm. Addressing a 3-objective, 3-variable high-dimensional optimization problem, our Ex3DSP discovers the Pareto front with a 1148-fold reduction in experimental workload. The platform provides two solution types (objective-optimal and balanced) with interpretability and reveals underlying mechanisms (variable-objective interaction and sand mold fracture behavior). Moreover, these solutions are applied to guide real-world casting parameter selection, yielding castings with up to 385% improved physicochemical and mechanical performances. This work demonstrates a self-evolving experimentation paradigm for 3D sand printing, offering both an intelligent additive manufacturing roadmap and pre-industrial screening potential.
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Authors Songtao Hu, Xiantong Zhang, Kaiming Tang, Haoran Li, Wenhui Lu, Zi Wang, Yinjun Deng, Bo Zhang, Xiaobao Cao, Xi Shi, Zhike Peng
Journal national science review
Year 2026
DOI
10.1093/nsr/nwag352
URL
https://doi.org/10.1093/nsr/nwag352
Keywords Keywords not found

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