## 5 Conclusions  
This study successfully developed a real-time online automatic measurement system designed specifically for manufacturing marine propeller blades. The system, which has integrated cloud-based data storage and analysis capabilities, is built on a four-degree-of-freedom measurement platform that has two linear axes and two rotational axes. Furthermore, the system combines a high-resolution CITIZEN displacement sensor with a PLC-based motion control mechanism, thereby enabling precise automated measurement at multiple critical locations on propeller blades.  
The experimental results confirmed that the system achieves measurement precision up to the second decimal place under both the continuous and single-point measurement modes. Notably, the continuous measurement mode was found to have superior performance in terms of precision, stability, and measurement efficiency, highlighting its advantages for deployment in large-scale manufacturing environments. Validation performed using both the standard platform and real blade measurements indicated that the developed system effectively reduces reliance on manual labor, shortens inspection times, and facilitates early detection of casting or machining-induced deformation, thereby improving the overall efficiency of quality control.  
In the future, the present research team will focus on advancing multipoint simultaneous measurement techniques, optimizing sensor placement and system response time, and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mode** & **Mean** & **Variance** & **F** & \(p\) \\ \hline Continuous measurement mode & 1.23 & 0.868 & & \\ \hline Single-point measurement mode & 1.92 & 0.970 & 5.684 & 0.017 *** \\ \hline \hline \end{tabular}
\end{table}
Table 8: [PERSON]’s test for equality of variances in the two measurement modes. (*** \(p\) < 0.001).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**t** & **df** & \(p\) & **Mean Difference** & **Standard Error of Difference** \\ \hline Unequal variance assumed & \(-\)18.492 & 2368.835 & \(<\)0.001 ** & \(-\)0.695 & 0.038 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Independent sample \(t\) test. (** \(p\) < 0.001).  
integrating artificial intelligence-driven data analysis to enhance decision-making support and system self-diagnosis. These developments could facilitate intelligent manufacturing and automated quality management. Moreover, the developed system has the potential to be extended to other freeform surface components, such as fan blades and turbine blades. Overall, it offers a cost-effective and feasible solution for precision surface quality inspection in high-precision machining applications.  
In future research, optimizing the layout of measurement sensors will be critical for improving surface quality control. A well-designed sensor arrangement can ensure full coverage of complex blade geometries, minimize measurement dead zones, and enhance the detection of localized defects or deformation. This, in turn, supports more accurate real-time feedback, enables better control decisions, and improves the overall reliability of high-precision machining processes.  
Conceptualization, K.-Y.H.; Methodology, Y.-M.C.; Software, K.-Y.H.; Validation, K.-Y.H.; Formal analysis, Y.-M.C.; Investigation, Y.-M.C. and K.-Y.H.; Data curation, K.-Y.H.; Writing--original draft, Y.-M.C.; Writing--review & editing, Y.-M.C. and K.-Y.H.; Supervision, Y.-M.C.; Project administration, Y.-M.C.; Funding acquisition, Y.-M.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by National Pingtung University under Research Projects NPTU-AD-114-1-2, NPTU-114-010, and CCS-113-003. The APC was funded by the National Science and Technology Council (NSTC) of Taiwan under Project No. 114-262-E-153-001.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.