We propose efficient multiple machine learning (ML) models using specifically polynomial and logistic regression ML methods to predict the optimal design of proton exchange membrane (PEM) electrolyzer cells. The models predict eleven different parameters of the cell components for four different input parameters such as hydrogen production rate, cathode area, anode area, and the type of cell design (e.g., single or bipolar). The models fit well as we trained multiple machine learning models on 148 samples and validated the model performance on a test set of 16 samples. The average accuracy of the classification model and the mean absolute error is 83.6% and 6.825, respectively, which indicates that the proposed technique performs very well. We also measured the hydrogen production rate using a custom-made PEM electrolyzer cell fabricated based on the predicted parameters and compared it to the simulation result. Both results are in excellent agreement and within a negligible experimental uncertainty (i.e., a mean absolute error of 0.615). Finally, optimal PEM electrolyzer cells for commercial-scaled hydrogen production rates ranging from 500 to 5000 mL/min were designed using the machine learning models. To the best of our knowledge, we are the first group to model the PEM design problem with such large parameter predictions using machine learning with those specific input parameters. This study opens the route for providing a form of technology that can greatly save the cost and time required to develop water electrolyzer cells for future hydrogen production.

Threaded component fatigue failure is a severe issue in cyclically loaded mechanical systems, and the service life in these systems is controlled primarily by stress concentration at the thread root, especially in loading regimes dominated by bending. Rounded thread profiles such as knuckle threads have been thought to improve fatigue performance, although this is mostly due to the assumption being made on the basis of axial loading, the numerical stress analysis, and/or isolated stress-concentration analyses. This paper presents an experimental study on the fatigue behavior of knuckle-thread and conventional screw-thread specimens manufactured from AISI 1045 steel under rotating bending loading to determine the effects of thread geometry on fatigue life and damage mechanisms. Fatigue testing was conducted at varying stress levels to develop comparative stress–life (S–N) curves, the analytical relation being used in determining the stress-concentration factor, and standard literature techniques have been used in the analysis of fracture-surface in order to investigate the behavior of crack initiation and propagation. Results indicate that knuckle threads exhibit a lower stress concentration factor (K_t ≈ 1.59) than screw threads (K_t ≈ 2.11), resulting in longer fatigue life at the same nominal stress level, particularly in the high-cycle life regime. Fractographic research also indicates that knuckle threads enhance delayed crack initiation and more evenly distributed circumferential crack propagation, but screw threads show highly localized crack initiation and rapid radial propagation of cracks, resulting in earlier unstable fracture. These findings provide new experimental evidence that the improved fatigue performance of knuckle threads during rotating bending is linked to fundamental change in fatigue damage mechanism rather than to stress alleviation alone, thereby offering quantitative supporting guidance in designing fatigue-sensitive threaded components to experience cyclic bending.