In today’s landscape, where smart manufacturing has become the core competitive advantage of the industry, machining simulation and verification have evolved from auxiliary tools to critical components ensuring the reliability and efficiency of digital manufacturing. With the rapid popularization of automation and AI, the design of toolpaths and machining parameters is shifting from manual expertise toward algorithmic generation. While this significantly enhances design efficiency and innovation speed, it introduces a pivotal challenge: how to verify that automatically generated data can be executed safely on actual production lines while maintaining quality.
This article explores the positioning accuracy of Tsudakoma’s cradle-type tilting axis, measured using an angular swing inspection instrument. It compares the differences between enabling and disabling the angle encoder. Results show that when the encoder is enabled, positioning accuracy and repeatability are excellent, with compensated error reduced to 4.7 arc-seconds. When the encoder is disabled, non-linear errors occur within the ±10° range, reaching up to 155 arc-seconds. The study indicates that 1° incremental measurement can reveal structural characteristics, providing valuable reference for five-axis machine development.
This study addresses preload monitoring for ball screws by introducing a diagnostic approach based on small-sample data augmentation. Prolonged operation reduces preload, compromising positioning accuracy, while traditional methods struggle to collect sufficient data. We employ Generative Adversarial Networks (GAN) to augment vibration signals and mitigate data imbalance, comparing CNN, MLP, and XGBoost models. Results show GAN-generated data significantly improves prediction accuracy and generalization, offering an effective solution for industrial machinery health monitoring.
