Objective To improve the precision of the micro-electrical discharge machining (micro-EDM) of micro-holes, a pioneering approach is undertaken in this study. Deep learning is applied innovatively to micro-hole micro-EDM, weaving together orthogonal experiments and deep learning to present an intricate methodology, i.e., an integrated fusion of orthogonal experiments and convolutional neural networks (CNNs). This synthesis strives to secure a robust dataset for subsequent predictive analyses of experimental outcomes via a CNN while concurrently minimizing the number of experiments. Methods The initial phase involves a meticulous L27 (313) orthogonal experiment, featuring four factors and three levels. The intricate impact patterns and optimal processing parameters for feed rate, spindle speed, pulse duty cycle, and pulse frequency regarding entrance overcut (EnOV), exit overcut (ExOV), and taper angle (TA) in the micro-EDM of H62 brass micro-holes are investigated meticulously in this study. Rigorous range and variance analyses are conducted on the experimental results, complemented by the deployment of scanning electron microscopy (SEM) to scrutinize the machined morphology for result validation. Subsequently, to validate the precision and applicability of the model predictions, 81 groups of data from orthogonal experiments on entrance overcut, exit overcut, and taper angle serve as predictive data for a CNN based on the PyTorch framework. Eleven groups of data are chosen meticulously as prediction samples, leaving the remaining 70 groups for training samples to predict experimental results. Results and Discussions The results show that feed rate, spindle speed, pulse duty cycle, and pulse frequency wield significant influence over entrance overcut, exit overcut, and taper angle in the micro-EDM of H62 brass micro-holes. For the entrance overcut in micro-hole machining, the hierarchy of impact intensity from high to low is pulse duty cycle, pulse frequency, feed rate, and spindle speed, and the identified optimal combination of parameters is a feed rate of 0.08 mm/s, a spindle speed of 2 000 r/min, a pulse duty cycle of 60%, and a pulse frequency of 3 000 Hz. For the exit overcut in micro-hole machining, the determined order is feed rate, spindle speed, pulse duty cycle, and pulse frequency, and the optimal combination of parameters is a feed rate of 0.08 mm/s, a spindle speed of 1 000 r/min, a pulse duty cycle of 60%, and a pulse frequency of 3 000 Hz. For the taper angle in micro-hole machining, the hierarchy is feed rate, pulse duty cycle, spindle speed, and pulse frequency, and the optimal combination parameters is a feed rate of 0.05 mm/s, a spindle speed of 1 500 r/min, a pulse duty cycle of 60%, and a pulse frequency of 3 000 Hz. A holistic consideration and analysis of the relationships between various factors pinpoints the optimal combination of parameters for micro-hole machining precision through validation experiments, i.e., a feed rate of 0.02 mm/s, a spindle speed of 1 000 r/min, a pulse duty cycle of 60%, and a pulse frequency of 3 000 Hz. The PyTorch-based CNN demonstrates remarkable predictive accuracy while underscoring the resilience of its results. The predicted values of entrance overcut, exit overcut, and taper angle for micro-hole machining align closely with the true experimental values, exhibiting minimum and maximum relative errors of 1.60% and 10.89% for entrance overcut, 2.44% and 11.10% for exit overcut, and 2.75% and 11.82% for taper angle. All the predicted values have relative errors below 12%, affirming a resilient fit of the PyTorch-based CNN and demonstrating exceptional predictive performance. The model’s applicability extends impressively, showcasing the capability to utilize orthogonal-experiment data for predicting any parameter combination, thereby effectively meeting practical production and machining requirements. A novel and advanced method for predicting the precision of micro-EDM for micro-holes is introduced in this study. Additionally, it serves as a valuable guide for actual production and machining processes, providing insights and directions for future advancements in the field. As a result, the research contributes significantly to the ongoing efforts in enhancing micro-hole machining precision, providing a new methodology and serving as a foundational basis for guiding practical applications in the field of micro-electrical discharge machining. Expanding on the implications of these findings, it is evident that the integration of deep learning with traditional experimental approaches presents a paradigm shift in micro-EDM research. Conclusions The synergistic combination of orthogonal experiments and CNNs optimizes the utilization of experimental data while minimizing the resources required for conducting a vast number of experiments. The meticulous analysis of the impact patterns and optimal parameters for micro-EDM of H62 brass micro-holes offers a comprehensive understanding of the intricate relationships between various machining factors. The utilization of SEM further validates the experimental results by providing a detailed analysis of the machined morphology. The subsequent validation of the model predictions using a PyTorch-based CNN demonstrates the accuracy and applicability of the proposed approach. The model’s ability to predict entrance overcut, exit overcut, and taper angle with high precision, as evidenced by the minimal relative errors, establishes its reliability and effectiveness. The findings contribute to the theoretical understanding of micro-EDM processes while also providing practical insights for optimizing machining parameters in real-world applications. The recommended optimal combination of parameters provides a clear guide for achieving high precision in micro-hole machining, addressing a critical need in industries where micro-EDM is employed. The study’s emphasis on the applicability of the model to diverse parameter combinations ensures its relevance in various manufacturing scenarios. The high predictive accuracy of the CNN suggests its potential for broader applications beyond the specific parameters investigated in this study. In conclusion, this research advances the theoretical understanding of micro-EDM processes and introduces a practical and efficient methodology for optimizing machining parameters. The integration of deep learning with traditional experimental approaches opens new avenues for research in precision machining. The model’s applicability and accuracy position it as a valuable tool for industries relying on micro-EDM, offering a systematic and data-driven approach to enhance precision and efficiency in micro-hole machining. As industries continue to embrace advanced technologies, the findings of this study pave the way for the integration of deep learning in manufacturing processes, marking a significant contribution to the field of micro-electrical discharge machining. © 2024 Sichuan University. All rights reserved.
