Resumen
Implementation of a machine learning procedure to model the temperature drop in the cryogenic cooling of Inconel 718. Machine learning is performed by an adaptive neuro-fuzzy inference system (ANFIS). Based on a representative set of cryogenic cooling experiments, the formed response surfaces (of temperature drop in Inconel 718) are represented as a set of fuzzy logic rules. Since we are dealing with a small set of experiments (input?output datapoints), the leave one subject out (LOSO) version of k-fold cross-validation is adopted. LOSO offers to exploit the small datasets and by them assess and tune the overall ANFIS modeling performance. A machine learning-based model is designed to minimize the RMSE prediction error by finding the optimal set of hyper-parameters (external ANFIS input parameters). The optimization is performed with the particle swarm optimization (PSO) algorithm.