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Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes

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Published on: December 13, 2016

Efficient Bayesian Function Optimization of Evolving Material Manufacturing Processes.

David Rubín de Celis Leal^1,1, Dang Nguyen¹, Pratibha Vellanki¹

¹Applied Artificial Intelligence Institute (AI) and Institute for Frontier Materials (IFM), Deakin University, Waurn Ponds, Victoria 3216, Australia.

|December 21, 2019

Summary

This summary is machine-generated.

Adaptive Bayesian optimization enhances industrial production of short polymer fibers by improving yield and quality. This machine learning approach minimizes optimization costs for novel material scale-up.

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Area of Science:

Materials Science
Chemical Engineering
Machine Learning

Background:

Scaling laboratory procedures to industrial production faces challenges in maintaining quality and maximizing yield.
Optimizing batch processing for novel materials is complex due to interdependent and antagonistic experimental parameters.

Purpose of the Study:

To demonstrate Adaptive Bayesian Optimization (ABO) as a tool for improving per-batch yield and quality in short polymer fiber production.
To show that ABO can achieve targeted fiber length distributions with minimal optimization cost and prior knowledge.

Main Methods:

Utilized Adaptive Bayesian Optimization (ABO) for optimizing batch processing parameters.
Applied ABO to wet spinning and shear dispersion methods for producing short polymer fibers.
Validated ABO's effectiveness starting from sub-optimal conditions with limited initial data.

Main Results:

Achieved increased per-batch yield and improved quality of short polymer fibers.
Successfully obtained short fiber dispersions with a specified, targeted fiber length distribution.
Demonstrated minimal cost of optimization through the ABO approach.

Conclusions:

Adaptive Bayesian Optimization is a valuable tool for optimizing dynamic scale-up processes in materials manufacturing.
ABO can be applied to complex, high-dimensional optimization challenges beyond fiber production.
Synergies between industrial processing, material engineering, and machine learning offer significant potential for innovation.