Additive manufacturing, a key enabler of Industry 4.0, is revolutionizing the automatic landscape in manufacturing. The primary challenge in manufacturing innovation centers on the implementation of smart factories characterized by unmanned production facilities and automated management systems. To overcome this challenge, the adoption of 3D printing technologies, which offer significant advantages in standardizing production processes, is crucial. However, a major obstacle in complete automation of additive manufacturing is an inadequate placement of support structures at critical locations, which remains the leading cause of print failures. This study proposed a novel algorithm for accurate detection of island regions known to be critical areas requiring support structures. The algorithm can compare loops on two consecutive layers derived from STL files. In contrast to conventional GPU-based image comparison methods, our proposed CPU-based algorithm enables high-precision detection independent of image resolution. Experimental results demonstrated the algorithm's efficacy in enhancing the reliability of 3D printing processes and optimizing automated workflows. This research contributes to the advancement of smart manufacturing by addressing a critical challenge in the automation of additive manufacturing processes.

In this study, the structural integrity of an engine-generator support structure of hybrid drone is verified through finite element (FE) analysis and experimental investigation. From preliminary experiments, critical failures in four columns of the support structure were observed. Due to the repeated cyclic loads induced by the engine-generator operation, the results of the FE simulation pointed out that fatigue failure is the main cause. To improve the structural integrity, the geometric shape and the material of the structural members are modified and changed, and the safety factor is also reviewed using static structural analysis. The possibility of critical resonance is evaluated through FEM-associated modal analysis and a series of vibration tests. As result, it is confirmed that the re-designed support structure was structurally improved with enough safety margin through FE analysis and experimental investigation, and fatigue life by comparing the predicted value and S-N curve of the material used to the support structure was improved.