This study investigates the thermal properties and sterilization efficacy of polylactic acid (PLA) components fabricated via fused deposition modeling (FDM), focusing on PLA’s compatibility with autoclave sterilization protocols. While PLA is extensively recognized for its biobased and biodegradable characteristics, its limited thermal stability has traditionally restricted its application in high-temperature sterilization settings, such as in medical contexts. In our research, we examined three distinct specimen geometries—cylindrical, rectangular, and curved—subjecting them to thermal post-processing through constrained annealing, employing salt or silicone as the embedding medium. Following this process, we exposed the specimens to elevated temperatures, simulating typical sterilization conditions. The outcomes indicated that the annealed PLA specimens exhibited dimensional stability at temperatures exceeding 170 °C, thereby demonstrating their viability for steam sterilization procedures. To translate these findings into practical applications, we selected a small, complex geometrically relevant component, the Easy Bone Collector (EBC) shell, for autoclave testing at 134 °C. Post-sterilization, the part successfully retained its shape and functionality, indicating that, with appropriate thermal conditioning, PLA can be effectively utilized to manufacture cost-efficient, autoclavable components suitable for medical use. These results reveal a promising and sustainable approach to producing reusable, sterilization-compatible PLA devices, particularly in low-volume or single-use applications where biodegradability is advantageous.

This paper presents a novel interface that leverages a smartwatch for controlling industrial robotic arms. By harnessing the gyroscope and advanced gesture recognition capabilities of the smartwatch, our solution facilitates intuitive, real-time manipulation that caters to users ranging from novices to seasoned professionals. A dedicated application is implemented to aggregate sensor data via an open-source library, providing a streamlined alternative to conventional control systems. The experimental setup consists of a smartwatch equipped with a data collection application, a robotic arm, and a communication module programmed in Python. Our aim is to evaluate the practicality and effectiveness of smartwatch-based control in a real-world industrial context. The experimental results indicate that this approach significantly enhances accessibility while concurrently minimizing the complexity typically associated with automation systems.

This paper presents an integrated workflow for smart manufacturing, combining CAD modeling, Digital Twin synchronization, and automated visual inspection to detect defective fuses in industrial electrical panels. The proposed system connects Onshape CAD models with a collaborative robot via the ThingWorx IoT platform and leverages computer vision with HSV color segmentation for real-time fuse validation. A custom ROI-based calibration method is implemented to address visual variation across fuse types, and a 5-s time-window validation improves detection robustness under fluctuating conditions. The system achieves a 95% accuracy rate across two fuse box types, with confidence intervals reported for statistical significance. Experimental findings indicate an approximate 85% decrease in manual intervention duration. Because of its adaptability and extensibility, the design can be implemented in a variety of assembly processes and provides a foundation for smart factory systems that are more scalable and independent.