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.

This research investigates the optimization of operational efficiency and cost reduction through the enhancement of material flow management within production line stations. Departing from conventional static analyses, the study employs advanced simulation tools to pinpoint performance bottlenecks and inefficiencies via dynamic modelling techniques. The Ishikawa diagram serves as the primary tool for conducting root-cause analysis. Simultaneously, the 5S methodology is implemented to foster workplace organization, standardization, and hygiene practices. In contrast to traditional optimization frameworks, the proposed strategy integrates real-time performance tracking systems, complemented by adaptive feedback mechanisms. This integration permits ongoing assessment of the production process, facilitating iterative improvement cycles. Empirical data gathered from monitored cycle times, equipment utilization rates, and defect frequencies substantiate the validation of implemented changes. The resulting optimized system significantly minimizes downtime and waste, thereby advancing sustainable and scalable operations. Ultimately, this research demonstrates that the fusion of simulation-based insights with lean management principles leads to considerable improvements in manufacturing productivity and overall product quality.

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.

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 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.

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.

Considering the strong need for improvement of security and multimedia systems in the automotive industry, wiring harness production is becoming more and more important. Adapting to the integration of new technologies on the vehicle is a challenge for wiring harnesses manufacturers. Now the production of wiring harnesses is still quite dependent on human resources, the way of distributing the workload on the workstations having a large share in increasing productivity. Components such as terminals, connectors and seals are getting smaller and smaller, making the manual handling more difficult. A solution to this problem could be increasing the automation degree in wiring harness production. The objective of the research topic approached in this article is to identify solutions for optimizing the wiring assembly flow by partially automating the production flow. The concern for the application of automated processes in the production of car wiring is not new, but so far, the wiring manufacturers have been more focused on automating the prefabrication of the elements that compose the harness and not the wiring harness assembly line. From an economic point of view, the automation of the assembly flow would increase productivity by reducing fabrication time and the uncertainty given by the human resource dependence.

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 paper describes authors’ research in developing collaborative virtual reality classrooms for laboratory and distance learning. The proposed platform was developed to facilitate the implementation of virtual reality labs that can be used by students, locally or by remote access from any device.

Identifying the “health state” of the equipment is the domain of condition monitoring. The paper proposes a study of two models: DNN (Deep Neural Network) and CNN (Convolutional Neural Network) over an existent dataset provided by Case Western Reserve University for analyzing vibrations in fault diagnosis. After the model is trained on the windowed dataset using an optimal learning rate, minimizing the cost function, and is tested by computing the loss, accuracy and precision across the results, the weights are saved, and the models can be tested on other real data. The trained model recognizes raw time series data collected by micro electro-mechanical accelerometer sensors and detects anomalies based on former times series entries.

Air pollution has become the most important issue concerning human evolution in the last century, as the levels of toxic gases and particles present in the air create health problems and affect the ecosystems of the planet. Scientists and environmental organizations have been looking for new ways to combat and control the air pollution, developing new solutions as technologies evolves. In the last decade, devices able to observe and maintain pollution levels have become more accessible and less expensive, and with the appearance of the Internet of Things (IoT), new approaches for combating pollution were born. The focus of the research presented in this paper was predicting behaviours regarding the air quality index using machine learning. Data were collected from one of the six atmospheric stations set in relevant areas of Bucharest, Romania, to validate our model. Several algorithms were proposed to study the evolution of temperature depending on the level of pollution and on several pollution factors. In the end, the results generated by the algorithms are presented considering the types of pollutants for two distinct periods. Prediction errors were highlighted by the RMSE (Root Mean Square Error) for each of the three machine learning algorithms used.

As standard practice in orthopedic surgery, the information gathered by analyzing Computer Tomography (CT) 2D images is used for patient diagnosis and planning surgery. Lately, these virtual slices are the input for generating 3D virtual models using DICOM viewers, facilitating spatial orientation, and diagnosis. Virtual Reality (VR) and 3D printing (3DP) technologies are also reported for use in anatomy visualization, medical training, and diagnosis. However, it has not been yet investigated whether the surgeons consider that the advantages offered by 3DP and VR outweigh their development efforts. Moreover, no comparative evaluation for understanding surgeon's preference in using these investigation tools has been performed so far. Therefore, in this paper, a pilot usability test was conducted for collecting surgeons' opinions. 3D models of knee, hip and foot were displayed using DICOM 3D viewer, two VR environments and as 3D-printed replicas. These tools adequacy for diagnosis was comparatively assessed in three cases scenarios, the time for completing the diagnosis tasks was recorded and questionnaires filled in. The time for preparing the models for VR and 3DP, the resources needed and the associated costs were presented in order to provide surgeons with the whole context. Results showed a preference in using desktop DICOM viewer with 3D capabilities along with the information provided by Unity-based VR solution for visualizing the virtual model from various angles challenging to analyze on the computer screen. 3D-printed replicas were considered more useful for physically simulating the surgery than for diagnosis. For the VR and 3DP models, the lack of information on bone quality was considered an important drawback. The following order of using the tools was preferred: DICOM viewer, followed by Unity VR and 3DP.

The present paper aims to highlight the way in which the speeds and accelerations for the numerical control (NC) axes of the systems specific to a didactic stand with Automated Storage & Retrieval System (AS-RS) and guided transport system can be determined experimentally. The stand consists of an AS-RS system where the storage of pallets with products is achieved using 3 storage structures arranged circularly around the transport-transfer system. The pallets reach the entryexit station by means of a two-station guided transport system. Its role is to supply the AS-RS with pallets for storage and to evacuate the pallets from it. Stepper motors were used to drive the NC axes specific to the mentioned systems, and the control was realized by means of programmable logical controllers (PLC). The control programs were made in applications according to these PLC. A Supervisory control and data acquisition (SCADA) type interface was created and thus it was possible to determine experimentally the speeds and accelerations specific to the NC axes of the mentioned systems.

Moving to Industry 4.0 involves the collection of massive amounts of data and the development of big data applications that can ensure a quick data flow between different systems, including massive amounts of data and information collection from smart sensors, and sending them to cloud applications that allow real-time data monitoring and processing. Securing and protecting the transmitted data represents a big issue to be discussed and resolved. In the paper, we propose a new method of data encoding and encryption for cloud applications using PNG format images. The proposed method is described in comparison with one of the classical methods of data encoding and transmission used currently. The paper includes a case study in which the proposed method was used to collect and transmit data from an automated waste collection system. The results show that the proposed method represents a secure, fast and efficient way to send and store the data in the cloud compared to the methods currently used. The proposed method is not limited to being used only in waste management but can be used successfully for any type of manufacturing system from smart factories.

This paper describes authors’ research in developing collaborative virtual reality applications as an interface for monitoring big data by creating a digital twin of the factory and sync the movement of virtual machines with the real ones. The platform allows an interactive reading of the sensor telemetry and processes data, maintenance information and access to a large technical library. For data acquisition and reports, a novel image data method was used. The data values that are encoded as pixel colors of images, using different encoding methods for each data type (text, integer, float, Boolean) are also encrypted using an image as a symmetric encryption key and are stored in the cloud in a time base folder structure, assuring a better data compression, security and speed, compared with the existing solutions based on JSON and NoSQL databases. The platform allows the remote access from the VR environment to the machines consoles and allows parametrization and remote commands.

The current paper presents the design of a glass panels recycling flow and the method used for establishing the optimal processing installation architecture. In the solution provided in the current research, a novel approach centred on applying digital twinning in the design of the requested processing architecture is presented. It involves designing the virtual prototype of the diffused processing architecture and modelling the glass waste flow as a hybrid material flow. Dedicated analysis and simulation software is then used for establishing installation architecture and the specific parameters for each processing and transport capacity. The assessment of different processing scenarios by virtual modelling and simulations can also be used for exploring options to increase productivity and profit for other different recycling architectures. The main practical value of the study consists of creating the means to improve the waste recycling of automotive windshields, float glass or construction glass panels with metallic meshes, all representing categories of waste insufficiently recycled in Romania. The simulation results of the study were validated by tests made on the glass panel recycling installation. Also, a recovery glass rate of minimum 85% of the amount of waste loaded into the recycling system was achieved, obtaining a waste recycling quantity three times higher than initially anticipated.

Companies, professionals and industry experts are using trade shows to share information and to build long-lasting business relationships.A trade show could have some disadvantages such as: higher price, difficulty to attend for potential partners due to distance, cost and time.An alternative could be the use of virtual reality exhibitions.In this paper, research results for developing a virtual reality exhibition platform are presented.The platform provides collaborative and advertisement tools enabling the possibility to interact and run complex activities remotely in a virtually immersive environment.The authors have developed some tools that extends the High Fidelity platform capabilities by designing, modelling and texturing the virtual environment, consisting in a futuristic city, a large exhibition pavilion and the exhibition booths using Maxon Cinema 4D and Substance Painter for modelling.