## 1 Introduction and Literature Review
Therefore, this study aims to develop an automated real-time online measurement system capable of precisely capturing dimensional deviations at critical manufacturing stages--including wax patterns, rough castings, and final products. This system integrates displacement sensing, PLC-based control, and cloud-based data analysis to enhance quality monitoring, reduce human error, and support smart manufacturing in propeller production. With the acceleration of smart manufacturing and digital transformation, conventional propeller producers are facing increasing challenges in achieving precision, efficiency, and quality consistency. Therefore, the integration of automated measurement and monitoring technologies to enhance process controllability and real-time feedback has become a central focus of recent research. Therefore, the objective of this study is to develop a real-time, online, and automated measurement system capable of high-precision inspection of marine propeller blades across different stages of manufacturing. This system aims to reduce labor cost, shorten measurement time, and enable real-time monitoring to improve overall manufacturing quality and support smart production environments. Compared with existing systems that rely on coordinate measuring machines (CMMs) or manual caliper-based inspections, the proposed system offers significantly enhanced capabilities in terms of real-time data acquisition, automation, and measurement repeatability. Unlike previous solutions limited by offline data processing or restricted to post-machining inspection, our platform supports real-time, in-process quality monitoring across the wax pattern, rough blank, and final product stages. These advantages reinforce the system's innovation and practical contribution to smart manufacturing applications. [PERSON] and [PERSON] [15] developed a reconfigurable five-axis machining system based on the OPEN CNC architecture. The core structure of this system is a three-degree-of-freedom parallel mechanism combined with an xy platform. Through a custom postprocessing module developed in LabVIEW, the five-axis tool paths generated with PowerMill 2020 are converted using inverse kinematics, thereby enabling precise tool alignment and machining along each axis. This system supports remachining and can be used for experiments involving the surface machining and calibration of machining points. Its machining precision has been validated through a concave circle engraving test on wax material, confirming its potential for future applications involving automated reworking and finishing of complex surfaces by using 3D scan data. Because of the complex three-dimensional curvature and geometry of propeller blades, challenges are often encountered during the machining process, such as sudden fluctuations in cutting forces and low machining stability. To address these issues, [PERSON] et al. [16] developed a cutting-force-estimation system based on current sensing. By integrating a low-cost sensor module (e.g., PZEM-004T) with LabVIEW, the system enables real-time monitoring of electrical current during robotic cutting operations, and its output was validated to be strongly correlated with actual cutting forces. [PERSON] et al. [16] conducted experiments using a five-axis platform and implemented robotic machining of propeller prototypes made from polylactic acid (PLA). They successfully obtained the trends indicating how the current varied with the cutting depth and feed rate. For instance, an increase in cutting force generally corresponds with a synchronous rise in motor current. Changes in the contact area could be inferred, and machining condition suitability could be assessed. Overall, the system proposed by [PERSON] et al. (2024) [16] is a cost-effective and practical alternative to expensive six-axis force sensors for real-time force estimation and, thus, contributes to the development of real-time monitoring modules for intelligent robotic machining in the future.