Spatial accuracy volumetric error compensation technology is regarded as a core solution for improving machining quality. This technique systematically analyzes and corrects the comprehensive errors of machine tools within a three-dimensional workspace, significantly reducing deviations caused by structural defects, environmental interference, or machining loads. It enables strict tolerance requirements for high-end workpieces such as aerospace components, medical instruments, and optical molds. In practice, spatial accuracy compensation can reduce volumetric errors by 50% to 90%.

With the advancement of modern manufacturing technology and the increasing demand for high-precision and high-efficiency machining, five-axis machine tools have been widely applied in aerospace, automotive manufacturing, and other industries. However, high-precision machining also introduces complex challenges in error control, which directly affect product quality and machine tool lifespan. Therefore, the measurement of errors in five-axis machine tools has become an important research topic.

台灣與日本同為地震頻繁之區域，地震對工具機與精密機械之水平精度監測則相形重要，本研究開發一套長時間水平量測與監測系統，如圖一所示。透過持續且高精度的數據蒐集與分析，評估地震活動是否造成機台水平偏移，進而影響加工與量測精度。並且了解與分析地震對精密機械穩定性的潛在影響，並為未來的地震補償技術與精密加工設備設計提供參考依據。本研究證實地震與CNC工具機之水平精度關係時，其影響程度甚是輕微，其水平精度皆於0.02mm/m，消除地震發生後對於機台可靠度下降之疑慮產生。

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 investigates the accuracy of domestic five-axis machine tools, measuring linear axis six degrees of freedom, rotary axis positioning, circular interpolation, spatial diagonal accuracy, and tool-tip synchronous motion. Results indicate most machines meet ISO 10791 standards, with some achieving half the tolerance limits. Rotary axis errors are linked to geometric accuracy, rotary center alignment, and control parameters. Compensation and parameter adjustments are recommended to optimize precision and enhance machining quality.

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.