Work

Real-time autonomous control of a continuous macroscopic process as demonstrated by plastic forming

Year: 2024

Type: article

Abstract: values of 0.87 and 0.90 for film width and thickness, respectively. Moreover, the active learning algorithm enabled the system to begin exploration with zero training data, effectively addressing the complex and interdependent relationships between control factors (material supply rate, applied force, material viscosity) in the continuous plastic forming process. Given that the core concept of this autonomous process can, in principle, be transferred to other continuous material processing systems, these results have implications for accelerating progress in both research and industry.

Source: Materials Horizons

DOI: 10.1039/d4mh00051j

Authors: Shun Muroga, Takashi Honda, Yasuaki Miki, Hideaki Nakajima, Don N. Futaba, Kenji Hata

Institutions: National Institute of Advanced Industrial Science and Technology, Research Association of High-Throughput Design and Development for Advanced Functional Materials

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: New Energy and Industrial Technology Development Organization

Award: G45242689