# Advanced Manufacturing Configuration by Sample-efficient Batch Bayesian Optimization

arXiv: https://arxiv.org/abs/2205.11827 · HTML: https://mlsanity.com/paper/2205.11827/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 30 references that are in the map.
- First posted on arXiv 24 May 2022.

## Builds on (most cited first, 8 of 8)

- [Constrained Bayesian Optimization with Noisy Experiments](https://mlsanity.com/paper/1706.07094/) (2017)
- [Safe Controller Optimization for Quadrotors with Gaussian Processes](https://mlsanity.com/paper/1509.01066/) (2015)
- [Parallel Bayesian Global Optimization of Expensive Functions](https://mlsanity.com/paper/1602.05149/) (2016)
- [Safety-Aware Cascade Controller Tuning Using Constrained Bayesian Optimization](https://mlsanity.com/paper/2010.15211/) (2020)
- [Parallel Predictive Entropy Search for Multi-objective Bayesian Optimization with Constraints](https://mlsanity.com/paper/2004.00601/) (2020)
- [Pareto-efficient Acquisition Functions for Cost-Aware Bayesian Optimization](https://mlsanity.com/paper/2011.11456/) (2020)
- [Self-Optimizing Grinding Machines using Gaussian Process Models and Constrained Bayesian Optimization](https://mlsanity.com/paper/2006.05360/) (2020)
- [Plasma Spray Process Parameters Configuration using Sample-efficient Batch Bayesian Optimization](https://mlsanity.com/paper/2103.13881/) (2021)

## Abstract (arXiv)

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. cmtbThe novel acquisition function is demonstrated, analyzed and compared on state-of-the-art benchmarking problems. We apply the optimization approach to atmospheric plasma spraying and fused deposition modeling. Our results demonstrate that the proposed framework can efficiently find input parameters that produce the desired outcome and minimize the process cost.