DeepAI AI Chat
Log In Sign Up

Advanced Manufacturing Configuration by Sample-efficient Batch Bayesian Optimization

by   Xavier Guidetti, et al.

We propose a framework for the configuration and operation of expensive-to-evaluate advanced manufacturing methods, based on Bayesian optimization. The framework unifies a tailored acquisition function, a parallel acquisition procedure, and the integration of process information providing context to the optimization procedure. The novel acquisition function is demonstrated and analyzed on benchmark illustrative problems. We apply the optimization approach to atmospheric plasma spraying in simulation and experiments. Our results demonstrate that the proposed framework can efficiently find input parameters that produce the desired outcome and minimize the process cost.


Sample-efficient Plasma Spray Process Configuration with Constrained Bayesian Optimization

Recent work has shown constrained Bayesian optimization to be a powerful...

Neighbor Regularized Bayesian Optimization for Hyperparameter Optimization

Bayesian Optimization (BO) is a common solution to search optimal hyperp...

Batch Bayesian Optimization on Permutations using Acquisition Weighted Kernels

In this work we propose a batch Bayesian optimization method for combina...

LinEasyBO: Scalable Bayesian Optimization Approach for Analog Circuit Synthesis via One-Dimensional Subspaces

A large body of literature has proved that the Bayesian optimization fra...

Preferential Batch Bayesian Optimization

Most research in Bayesian optimization (BO) has focused on direct feedba...

An efficient application of Bayesian optimization to an industrial MDO framework for aircraft design

The multi-level, multi-disciplinary and multi-fidelity optimization fram...