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.

Within the context of Industry 4.0, industrial systems are increasingly integrating digital tools such as discrete-event simulation (DES) and digital twins to enhance operational performance and facilitate data-driven decision-making. This research focuses on the design and implementation of an innovative digital twin to diagnose and optimize the productivity of manufacturing systems. A key advancement of this tool involves the integration of a material flow simulator, specifically WITNESS Horizon, with a suite of mathematical and digital models to create an integrated manufacturing digital tool. Rather than modeling individual pieces of equipment or isolated workstations, this digital twining approach encompasses the entire manufacturing architecture. To demonstrate the capabilities and efficacy of this integrated tool, we applied it to a real-world case study: the “Hub R2-11” production line. Utilizing the diagnostic features of the tool for this case study, we identified significant bottlenecks, revealing that over 70% of the conveyor was blocked and more than 60% of the workstation was underutilized. Utilizing the simulation and optimization features of the tool, we increased productivity by restructuring the manufacturing architecture. This involved implementing parallel machining, regulating inputs, and incorporating robotic palletizing. The resultant manufacturing architecture demonstrated a substantial improvement, increasing weekly pallet output from 17 to 41 while reducing conveyor block time by more than 50%. Additionally, a financial assessment indicated a favorable Net Present Value (NPV) and an Internal Rate of Return (IRR) exceeding 35% over three years. The research presented here employs a digital approach grounded in realistic operational constraints, effectively bridging technical innovations with economic feasibility. The findings underscore that this integrated manufacturing digital tool presents a scientifically robust and economically sound strategy for optimizing production systems.

This paper presents a logistic flow of assembling automotive rear axles. The product is presented in detail starting from the detailed research and analysis of relevant documentation about its functionality, including the manufacturing logistic flow diagram and the required equipment for the product manufacturing and assembly. This study is focused on optimizing the logistic flow for the manufacturing and assembly of automotive rear axles using WITNESS Horizon for system modeling and simulation in order to conduct system diagnostics, identify problems, and find solutions that will facilitate the optimization process. The study included a comprehensive assessment of the logistic flow, highlighting the performance of the equipment involved and identifying potential bottlenecks. Using the results obtained after the simulations, the Simplex linear mathematical method was applied to maximize production efficiency and profitability, considering the suppliers’ capacity constraints and the components’ delivery requirements. The results demonstrated a significantly optimized rear-axle production process, with increased profitability and improved productivity by eliminating identified bottlenecks. This research contributes to a deeper understanding of the complexities within the automotive industry and provides a solid foundation for continuously improving manufacturing and assembly processes.

This study applies to a virtual commissioning (VC) workflow with discrete-event simulation in WITNESS Horizon to diagnose and improve an automated brushless stator assembly line. A validated model of the full route—Stator Assembly Machine (SAM), Linear Transport System (LTS), Winding Machine (WM), Terminal Welding Machine (TWM), Inspection Machine (IM) and Electric Tester (ET)—was executed over a one-shift horizon (28,800 s). We compared the baseline configuration with an optimized scenario that retrieved robot tasks and refined LTS routing. Key performance indicators (KPIs) were resource utilization (Busy/Idle/Blocked) and completed operations. The results are quantitative and specific. Blocking at the SAM interface collapsed from 73.32% to 0% at PressPosition and from 80.64% to 0% at Robot2. LTS transitioned from 97.46% Blocked to 0%, with the share of Move/Running increasing to 14.76% (from ~0%). Line output—measured as completed assemblies at SAM—increased from 368 to 425 units per shift (+15.5%). Similar gains were recorded at other stations (e.g., WM1: 351 → 424 operations, +20.8%). These changes reflect the removal of the primary transfer bottleneck and a more balanced utilization across stations. The study demonstrates that VC can deliver actionable commissioning guidance. By quantifying where blocking occurs and testing alternative control strategies in a virtual environment, it is possible to raise throughput while maintaining stable operation. The modeling approach and metrics are reusable for related electromechanical assembly lines.

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.

Background: Integrating Radio Frequency Identification (RFID) technology into storage areas within the wiring harness manufacturing industry enables real-time component traceability and supports the implementation of fully automated inventory processes. While RFID systems provide continuous data regarding component type, quantity, and location, periodic inventory validation is still required to verify and correct records in the warehouse management system. Methods: This study examines the feasibility of passive ultra-high-frequency (UHF) RFID technology for automatic inventory management in a components warehouse. It also reviews relevant scientific literature on autonomous RFID signal measurement and Synthetic Aperture Radar (SAR)-based localization methods, which are subsequently adapted for inventory applications. An experimental setup is developed to characterize the reading field, hysteresis effects, and the influence of distance and tag orientation on detection performance. Results: The findings indicate that RFID-based automatic inventory is achievable with high accuracy and stability, especially when tag trajectories correspond to areas of high detection probability and antenna polarization is optimally configured. Conclusions: The proposed RFID-based system can be implemented with minimal hardware changes and low investment, thereby improving stock accuracy, traceability, and operational efficiency in automotive component logistics.