Abrasion tester device enhanced by artificial intelligence and image processing

Abstract

Abrasion Tester Device Enhanced by Artificial Intelligence and Image Processing Abrasion testing devices are used to measure the resistance of material surfaces against mechanical abrasive effects such as friction, rubbing, and erosion. These devices are widely employed across various industries to determine the wear performance and long-term durability of materials. The results of abrasion tests may vary depending on the expertise of the operator and the environmental conditions under which the test is conducted. The invention enhances the accuracy and objectivity of abrasion tests by integrating artificial intelligence and image processing technologies into the abrasion testing device. In this way, the measurements become more precise and reliable. Furthermore, by reducing the need for operator intervention, human-induced errors are minimized, and the results can be consistently obtained under the same standards each time.

Description

[0001] DESCRIPTION Abrasion Tester Device Enhanced by Artificial Intelligence and Image Processing

[0002] TECHNICAL FIELD

[0003] The invention pertains to the field of testing devices used to measure the wear resistance of materials. Specifically, it aims to enhance the accuracy and objectivity of abrasion tests by integrating artificial intelligence and image processing technologies.

[0004] PRIOR ART

[0005] Abrasion testing devices are used to evaluate the resistance of material surfaces against mechanical abrasive effects such as friction, rubbing, and erosion. These devices are widely employed across various industries to determine the wear performance and long-term durability of materials. However, in accordance with existing abrasion testing standards, the results can vary depending on the expertise of the operator and the environmental conditions of the testing area.

[0006] Technical Problem in the Prior Art, the primary technical challenges encountered in abrasion testing devices generally concern measurement precision, result objectivity, and compliance with established standards. In systems such as abrasion test machines, manual interventions and experience-based evaluation processes tend to amplify these issues. The main problems include subjectivity in measurements, lack of precision in surface analysis, and lengthy evaluation procedures.

[0007] The most critical issue in these measurements is the dependence on operator intervention. The manual operation of the device and the subjective nature of the evaluations can lead to human-induced errors. Consequently, the repeatability and reliability of the obtained results are diminished. All these drawbacks highlight the need for innovative solutions within the technical field. DESCRIPTION OF THE INVENTION

[0008] The purpose of the invention is to enhance the accuracy and objectivity of abrasion tests by integrating artificial intelligence and image processing technologies into the abrasion testing device.

[0009] One advantage of the invention is that it enables more precise and reliable measurements.

[0010] Another advantage is that it allows the analysis of microscopic-level changes in surface wear.

[0011] Additionally, it facilitates the detection of fine details that are difficult to observe visually. The invention automatically detects surface wear patterns, cracks, and deformations, thereby providing a more comprehensive evaluation.

[0012] It also increases the objectivity and repeatability of measurements.

[0013] By reducing the need for operator intervention, the invention minimizes human-induced errors and ensures that results are consistently obtained under the same standards. During testing, the device automatically records and analyzes all collected data.

[0014] The invention further enables the storage of historical test results, the performance of long-term trend analyses, and the easy reporting of outcomes.

[0015] Moreover, it accelerates testing processes and enhances operational efficiency.

[0016] Compared to manual methods, the invention produces results more quickly and minimizes the operator’s workload.

[0017] By providing more precise data, it supports product development processes and raises quality control standards.

[0018] In conclusion, the invention modernizes the abrasion testing device, offering a more reliable, faster, and more efficient testing process.

[0019] List of Figures

[0020] Figure 1 . Schematic Representation of the Abrasion Device Enhanced by Artificial Intelligence and Image Processing

[0021] List of References

[0022] 1. Camera

[0023] 2. Abrasive Wheels 3. Control Unit

[0024] 3.1. Image Processing Module

[0025] 3.2. Artificial Intelligence Module

[0026] 4. Database

[0027] DETAILED DESCRIPTION OF THE INVENTION

[0028] The operation of the system begins when the camera (1 ) captures the image. Since the RGB camera (1 ) provides a continuous data input to the system, the entire process is executed in real time. The images obtained from the camera undergo a series of transformations and are converted into the HSV format.

[0029] In the HSV color model, the Hue, Saturation, and Value parameters are used to numerically represent all mechanical conditions. The same principle applies to measurement parameters. The Hue value corresponds to the tone of the analyzed image, the Saturation value indicates the color intensity or vividness, and the Value parameter represents the brightness level of the image.

[0030] After the captured images are processed in the HSV color space, the resulting data are recorded into at least one database. The database preferably operates on an SQLbased structure. Once all real-time data (processed per second) are analyzed, the data input phase is completed. Subsequently, normalization is applied to the captured images.

[0031] Following the averaging of all values, air bubbles or surface irregularities within the dataset are detected through thresholding. After the thresholding process, markings are applied to visually highlight the identified defects. These outputs are then transferred as input data to a basic artificial intelligence model.

[0032] From the detected defects, an analytical dataset is generated, which is preferably used to train an artificial neural network. The neural network is refined using historical datasets to enhance its learning capability and subsequently enable inference on real data. When the system identifies defective conditions — specifically wear anomalies — it exhibits graceful degradation, maintaining partial functionality instead of complete failure. Following this stage, a more detailed material report is generated, providing comprehensive insights into the wear characteristics and overall performance of the tested material.

Claims

CLAIMS1. The invention is an abrasion device improved by artificial intelligence and image processing, characterized in that it comprises;• an RGB camera (1 ),• an image processing module (3.1 ) that processes the images obtained from the camera,• an artificial intelligence module (3.2) that analyzes data based on the processed images, detects anomalies related to abrasion conditions, identifies abrasion anomalies, and indicates the degree of deterioration,• and at least one database (4) where the data is stored.

2. The invention according to Claim 1 , characterized in that artificial neural networks are preferably used in the analysis processes of the artificial intelligence module (3.2).

3. The invention according to Claim 1 , characterized in that it comprises an image processing module (3.1 ) that converts the data into HSV format, normalizes the converted images, detects bubbles in the dataset through matching, and marks them for visual observation of errors.

Images