1. A machine vision approach to assessing steel properties through spark imagingGoran Munđar, Miha Kovačič, Uroš Župerl, 2025, izvirni znanstveni članek Opis: Accurate and efficient evaluation of steel properties is crucial for modern manufacturing. This study presents a novel approach that combines spark imaging and deep learning to predict carbon content in steel. By capturing and analyzing sparks generated during grinding, the method offers a fast and cost-effective alternative to conventional testing. Using convolutional neural networks (CNNs), the proposed models demonstrate high reliability and adaptability across different steel types. Among the tested architectures, MobileNet-v2 achieved the best performance, balancing accuracy and computational efficiency. The findings highlight the potential of machine vision and artificial intelligence in non-destructive steel analysis, providing rapid and precise insights for industrial applications.
Ključne besede: carbon content prediction, convolutional neural networks, deep learning, machine vision, spark imaging, steel analysis
Objavljeno v DKUM: 03.11.2025; Ogledov: 0; Prenosov: 3
 Celotno besedilo (1,84 MB)
Gradivo ima več datotek! Več... |
2. Enhancing robotic bin-picking through machine vision: investigating the impact of detection speed and bin fill levels : preučevanje vpliva zaznave objektov ob različno napolnjenih zabojihSuhaib Ebrahim Mambayil Ebrahimkutty, 2025, diplomsko delo Opis: Robotic bin-picking systems have become important in modern intralogistics as warehouses automate to address labour shortages and rising e-commerce demands. These systems operate through the coordinated use of (a) a robotic manipulator, (b) a 3D machine vision system, and (c) robotic grippers. When integrated with Automated Storage and Retrieval Systems (AS/RS), these systems offer an efficient approach to improving the order-picking performance.
In this study, the performance of a selected 3D machine vision system was evaluated based on two key parameters: detection mode and bin fill levels. A series of structured laboratory experiments was conducted using a UR5e collaborative robot equipped with three types of robotic grippers and a Pickit M-HD2 3D vision system. Objects of varying shapes and orientations were tested under different detection modes and bin fill levels. Faster detection modes reduced the processing time but resulted in more detection failures, especially with complex shapes or densely populated bins. In contrast, slower modes improved accuracy but increased the cycle time. Normal mode offered the best balance. The detection reliability decreased at higher bin fill levels and with irregularly shaped objects, due to occlusion and limited visibility. By analysing the detection time and successful detection, insights were gained into how appropriate detection configurations can improve both reliability and throughput in robotic bin-picking systems integrated with AS/RS.
Ključne besede: intralogistics, Robotic Bin-Picking, machine vision system, object detection
Objavljeno v DKUM: 31.07.2025; Ogledov: 0; Prenosov: 12
Celotno besedilo (1,97 MB) |
3. Robot for navigation in maize crops for the Field Robot Event 2023David Iván Sánchez-Chávez, Noé Velázquez-López, Guillermo García-Sánchez, Alan Hernández-Mercado, Omar Alexis Avendaño-Lopez, Mónica Elizabeth Berrocal-Aguilar, 2024, izvirni znanstveni članek Opis: Navigation in a maize crop is a crucial task for the development of autonomous robots in agriculture, with numerous applications such as spraying, monitoring plant growth and health, and detecting weeds and pests. The Field Robot Event 2023 (FRE) continued to challenge universities and other research teams to push the development of algorithms for agricultural robots further. The Universidad Autónoma Chapingo has been developing a robot for various agricultural tasks, aiming to provide a low-cost alternative to work with Mexican farmers in the future. For this edition of the FRE, a navigation algorithm was created using an encoder, an IMU (Inertial Measurement Unit), an RPLIDAR (Rotating Platform Light Detection and Ranging), and cameras to collect data for decision-making. The algorithm was developed in ROS Melodic, dividing the task into steps that were tested to determine the robot's actual movements. The system navigates by using ROIs (regions of interest) and the mass center to guide the robot between maize rows. It calculates the mean of the final orientation values before reaching the end of a row, which is detected using an RPLIDAR. For turns and straight-line movements to reach the next row, the orientation is used as a guide. To detect plants for spraying, lasers located on each side of the vehicle are employed. Obstacle detection relies on a YOLOv5 (You Only Look Once) trained model and a laser, while reverse navigation uses a rear camera. During the competition, the robot faced challenges such as dealing with grass, the small size of the plants, and the need to use a different power source, which affected its performance.
Ključne besede: machine vision, convolutional neural network (CNN), regions of interest (ROI), autonomous navigation
Objavljeno v DKUM: 23.04.2025; Ogledov: 0; Prenosov: 1
 Povezava na datoteko |
4. Rapid assessment of steel machinability through spark analysis and data-mining techniquesGoran Munđar, Miha Kovačič, Miran Brezočnik, Krzysztof Stępień, Uroš Župerl, 2024, izvirni znanstveni članek Opis: The machinability of steel is a crucial factor in manufacturing, influencing tool life, cutting
forces, surface finish, and production costs. Traditional machinability assessments are labor-intensive
and costly. This study presents a novel methodology to rapidly determine steel machinability using
spark testing and convolutional neural networks (CNNs). We evaluated 45 steel samples, including
various low-alloy and high-alloy steels, with most samples being calcium steels known for their
superior machinability. Grinding experiments were conducted using a CNC machine with a ceramic
grinding wheel under controlled conditions to ensure a constant cutting force. Spark images captured
during grinding were analyzed using CNN models with the ResNet18 architecture to predict V15
values, which were measured using the standard ISO 3685 test. Our results demonstrate that the
created prediction models achieved a mean absolute percentage error (MAPE) of 12.88%. While
some samples exhibited high MAPE values, the method overall provided accurate machinability
predictions. Compared to the standard ISO test, which takes several hours to complete, our method is
significantly faster, taking only a few minutes. This study highlights the potential for a cost-effective
and time-efficient alternative testing method, thereby supporting improved manufacturing processes.
Ključne besede: steel machinability, spark testing, data mining, machine vision, convolutional neural networks
Objavljeno v DKUM: 12.09.2024; Ogledov: 15; Prenosov: 28
Celotno besedilo (5,24 MB)
Gradivo ima več datotek! Več... |
5. Design of an Embedded Position Sensor with Sub-mm Accuracy : magistrsko deloMatej Nogić, 2019, magistrsko delo Opis: This master’s thesis presents the development of a machine-vision based localization unit developed at Robert Bosch GmbH, Corporate Sector Research and Advance Engineering in Renningen, Germany. The localization unit was developed primarily for position detection purposes with three degrees of freedom in highly versatile manufacturing systems but has an immense potential to be used anywhere where a precise, low-cost localization method on a two-dimensional surface is required. The complete product development cycle was carried out, from the components selection, schematic and optical system design, to the development of machine vision algorithms, four-layer Printed Circuit Board design and evaluation using an industrial robot. Thanks to the use of a patented two-dimensional code pattern, the localization unit can cover a surface area of 49 km2. The size and speed optimized, self-developed machine-vision algorithms running on a Cortex-M7 microcontroller allow achieving an accuracy of 100 µm and 60 Hz refresh rate.
Ključne besede: localization, machine-vision, code pattern, image sensor, embedded system
Objavljeno v DKUM: 14.01.2020; Ogledov: 1340; Prenosov: 62
Celotno besedilo (18,20 MB) |
