Bayesian experimental design in production engineering: a comprehensive performance and robustness study - Leyendecker, Lars; Gonzalez Degetau, Ana Maria; Bata, Katharina; Emonts, Jessica; Schmitz, Angela; Schmitt, Robert H. (Frontiers in Manufacturing Technology, 2026)
In production engineering, the identification of optimal process parameters is essential to advance product quality and overall equipment effectiveness. Optimizing and adapting process parameters through experimental design is relevant for different phases of the life cycle of a production process: (i) design and development of new processes, (ii) failure analysis and optimization, and (iii) adaptation and calibration in series production. Existing experimental design approaches tend to be inefficient because they comprise static, non-adaptive methodologies that separate experiment design from execution and analysis. Instead, Bayesian Optimization (BO) offers an adaptive and data-efficient methodology for experimental design termed Bayesian experimental design (BED). In BED, the selection of an experiment is re-evaluated in each iteration based on previous experiment results according to an acquisition function that aims to maximize the informational content of each experiment. However, the configuration of BO algorithms for specific optimization problems requires extensive knowledge of both BO and process characteristics. The mean and covariance functions of the surrogate model, the acquisition function, and initial data sampling must be individually configured and significantly influence overall optimization performance, preventing widespread adoption in production engineering practice. To guide the configuration of BO algorithms for optimizing production processes, in this paper, we perform an extensive benchmark study with a total of 15,360 experiments. We evaluate the performance of a variety of BO algorithm configurations (including kernels, acquisition functions, and initial sampling sizes) on a total of eight optimization problems with a noiseless and a noisy variant each. The performance and robustness analysis reveals significant performance differences between individual BO algorithm configurations. The results of our benchmarking serve as empirical references based on which we derive actionable guidelines for the application of BED in production engineering.