.png){kind=icon}
返回
Efficient Bayesian Function Optimization of Evolving Material Manufacturing Processes
.png){kind=icon}
OA
AI
DOI：10.1021/acsomega.9b02439.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The scale-up of laboratory procedures to industrial production is the main challenge standing between ideation and the successful introduction of novel materials into commercial products. Retaining quality while ensuring high per-batch production yields is the main challenge. Batch processing and other dynamic strategies that preserve product quality can be applied, but they typically involve a variety of experimental parameters and functions that are difficult to optimize because of interdependencies that are often antagonistic. Adaptive Bayesian optimization is demonstrated here as a valuable support tool in increasing both the per-batch yield and quality of short polymer fibers, produced by wet spinning and shear dispersion methods. Through this approach, it is shown that short fiber dispersions with high yield and a specified, targeted fiber length distribution can be obtained with minimal cost of optimization, starting from sub-optimal processing conditions and minimal prior knowledge. The Bayesian function optimization demonstrated here for batch processing could be applied to other dynamic scale-up methods as well as to cases presenting higher dimensional challenges such as shape and structure optimization. This work shows the great potential of synergies between industrial processing, material engineering, and machine learning perspectives.
Keyword:
SCALE-UP
CHALLENGES
GRAPHENE
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.3
论文数:
3.4W
被引数:
9.8W
机构
引用论文
分享收藏Challenges and opportunities in graphene commercialization石墨烯商业化的挑战与机遇.png)
NATURE NANOTECHNOLOGY
NATURE NANOTECHNOLOGYIF34.9
PREAI
分享收藏edgeR: a Bioconductor package for differential expression analysis of digital gene expression dataedgeR: 用于数字基因表达数据差异表达分析的Bioconductor软件包OAAI
OAAI分享收藏A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robotAUTONOMOUS ROBOTS
AUTONOMOUS ROBOTSIF4.3
PREAI
分享收藏
查看更多内容