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This article deals with the creation of a digital twin for an experimental assembly system based on a belt conveyor system and an automatized line for quality production check. The point of interest is a Bowden holder assembly from a 3D printer, which consists of a stepper motor, plastic components, and some fastener parts. The assembly was positioned in a fixture with ultra high frequency (UHF) tags and internet of things (IoT) devices for identification of status and position. The main task was parts identification and inspection, with the synchronization of all data to a digital twin model. The inspection system consisted of an industrial vision system for dimension, part presence, and errors check before and after assembly operation. A digital twin is realized as a 3D model, created in CAD design software (CDS) and imported to a Tecnomatix platform to simulate all processes. Data from the assembly system were collected by a programmable logic controller (PLC) system and were synchronized by an open platform communications (OPC) server to a digital twin model and a cloud platform (CP). Digital twins can visualize the real status of a manufacturing system as 3D simulation with real time actualization. Cloud platforms are used for data mining and knowledge representation in timeline graphs, with some alarms and automatized protocol generation. Virtual digital twins can be used for online optimization of an assembly process without the necessity to stop that is involved in a production line.

This study explores the optimization of fused deposition modeling (FDM), a prominent 3D printing technology known for its accessibility and cost-effectiveness. The research aimed to identify and reduce errors associated with key printing parameters, specifically the layer height, printing temperature, and printing speed. Advanced tools such as a Keyence laser scanner and microscope were used to evaluate the dimensional accuracy and surface quality of various samples. The results indicate that the optimal settings for the layer height (0.16 mm), printing temperature (250 °C), and printing speed (350 mm/s) significantly minimize variation, resulting in more consistent and accurate prints. The results also showed that the samples printed with these optimized parameters had the lowest variability, underscoring the critical importance of precisely managing these factors. The findings highlight the critical role of fine-tuned FDM parameters in improving the quality and reliability of printed objects and provide valuable insights for further advances in 3D printing processes.

This study investigates the real-time monitoring of 3D printing using an endoscopic camera system integrated directly into the print head. The embedded endoscope enables continuous observation of the area surrounding the extruder, facilitating real-time inspection of the currently printed layers. A convolutional neural network (CNN) is employed to analyse captured images in the direction of print progression, enabling the detection of common defects such as stringing, layer shifting, and inadequate first-layer adhesion. The primary innovation of this work lies in its capacity for online quality assessment and immediate classification of print integrity within predefined thresholds. This system allows for the prompt termination of printing in the case of critical faults or dynamic adjustment of printing parameters in response to minor anomalies. The proposed solution offers a novel pathway for optimising additive manufacturing through real-time feedback on layer formation.

Small series production with a high level of variability is not suitable for full automation. So, a manual assembly process must be used, which can be improved by cooperative robots and assisted by augmented reality devices. The assisted assembly process needs reliable object recognition implementation. Currently used technologies with markers do not work reliably with objects without distinctive texture, for example, screws, nuts, and washers (single colored parts). The methodology presented in the paper introduces a new approach to object detection using deep learning networks trained remotely by 3D virtual models. Remote web application generates training input datasets from virtual 3D models. This new approach was evaluated by two different neural network models (Faster RCNN Inception v2 with SSD, MobileNet V2 with SSD). The main advantage of this approach is the very fast preparation of the 2D sample training dataset from virtual 3D models. The whole process can run in Cloud. The experiments were conducted with standard parts (nuts, screws, washers) and the recognition precision achieved was comparable with training by real samples. The learned models were tested by two different embedded devices with an Android operating system: Virtual Reality (VR) glasses, Cardboard (Samsung S7), and Augmented Reality (AR) smart glasses (Epson Moverio M350). The recognition processing delays of the learned models running in embedded devices based on an ARM processor and standard x86 processing unit were also tested for performance comparison.

The assisted assembly of customized products supported by collaborative robots combined with mixed reality devices is the current trend in the Industry 4.0 concept. This article introduces an experimental work cell with the implementation of the assisted assembly process for customized cam switches as a case study. The research is aimed to design a methodology for this complex task with full digitalization and transformation data to digital twin models from all vision systems. Recognition of position and orientation of assembled parts during manual assembly are marked and checked by convolutional neural network (CNN) model. Training of CNN was based on a new approach using virtual training samples with single shot detection and instance segmentation. The trained CNN model was transferred to an embedded artificial processing unit with a high-resolution camera sensor. The embedded device redistributes data with parts detected position and orientation into mixed reality devices and collaborative robot. This approach to assisted assembly using mixed reality, collaborative robot, vision systems, and CNN models can significantly decrease assembly and training time in real production.

One of the most important operations in the technological production process is the inspection of the manufactured product. The gradual wear of the tool affects the achievement of the required quality of the functional surfaces. In this research, we present the results of measuring the diameter deviation with a new generation laser triangulation sensor (LTS). At the same time, we have performed parametric optimization of several multi-responses, such as insert wear on the VBB flank side of cutting edge and diameter deviation Δd for a C45 steel sample during dry turning and using a sintered carbide insert, using the method of grey relational analysis (GRA) in combination with the Taguchi L16 orthogonal array. The optimal setting of input factors for multi-response parameters is ap 4-f 4-vc 1 i.e., depth of cut 0.5 mm, feed 0.4 mm per revolution, and a cutting speed of 70 m/min. At the same time, we present an evaluation of the significance of input factors using the method ANOVA.

The article deals with optimization of input data for wireless IoT devices with database handled by cloud framework. Tested hardware consists of ESP32 board with Wi-Fi technology support (Thinger.IO cloud platform) and Sensor Tag development boards with integrated sensors and Bluetooth connection communicated with IBM Watson IoT framework. The new idea is how to send optimized measured data to IoT platform which is limited to one message per second usually. We propose two separate approaches for input data: accumulation data in IoT device to multi-value packet and noise elimination solved by advanced interpolation (Kalman filter). Both principles can be combined to reduce noise and increase of data frequency stored in cloud platform for next knowledge datamining.

This paper focuses on a group of medieval musical fragments of Premonstratensian origin, which are now parts of the bindings of some municipal administrative books in the Košice City Archives. No complete musical codex of Premonstratensian provenance survived from the medieval period from the territory of Slovakia. Therefore, these newly discovered fragments are important evidence of the lively and unique scribal tradition of the Premonstratensians from the Late Middle Ages.