Papers by Keyword: Image Processing

Abstract: Metal additive manufacturing encompasses multiple techniques, among which Selective Laser Melting (SLM) is extensively employed for fabricating highly complex, precise, and uniquely shaped metal parts. However, obtaining accurate product characteristics often requires complex experimentation, which can potentially damage the products. Thus, there is a need to develop an automated method for predicting product characteristics. To forecast these attributes, details related to metal additive manufacturing products were documented, including process parameters and textural features. These features were extracted from product’s longitudinal sectional images and layer-by-layer images, using the gray-level co-occurrence matrix (GLCM). Subsequently, machine learning (ML) models such as Support Vector Regression (SVR), XGBoost, and LightGBM were employed to predict product properties and compare their performance. The experimental results indicated stronger correlations between process parameters and textural features in longitudinal section images compared to layer-by-layer ones. Moreover, the models demonstrated high predictive accuracy, particularly XGBoost and LightGBM, with R² score approaching 0.9 for all properties. These findings highlight the superiority and feasibility of the proposed approach. Furthermore, this method shows potential for accurately predicting a variety of product properties, fulfilling the needs of multiple application scenarios.

Abstract: We have developed methodologies for the detection of microdefects on painted surfaces and the evaluation of machined surface quality through the use of the patterned area illumination. The objective of this study is to optimize illumination for surface inspection of products by reproducing the illumination situation using the patterned area illumination. In this study, a ray tracing method, which is a computer graphics technique, was employed to reproduce a scenario in which a product surface is illuminated by the aforementioned patterned area illumination. In the conventional ray tracing method, the patterned area illumination is not taken into account. Accordingly, the surface to be inspected was postulated to be a mirror surface, and the illumination pattern was projected onto it in order to reproduce the reflection of the illumination pattern on the inspection surface. The developed simulation method enables several key optimizations. Firstly, it allows for the optimization of the illumination device by reproducing the inspection surface with a curved surface. Secondly, it enables the data augmentation of teacher data for machine learning for a versatile defect detector. Thirdly, it allows for the optimization of the circular pattern used to estimate the shape of micro defects. Finally, it enables the reproduction of pattern projection onto a machined surface for the evaluation of machining quality of the machined surface.

Abstract: The paper discusses the results of studying the hardening process of a gypsum-based composite material using a thermal imager. Thermal imaging research involves periodically obtaining thermal images of hardening material with pronounced hydration exotherm. It is assumed that the intensity of hydration and heat release depend on the state of the heterogeneous system, the structure of the forming material and the degree of aggregation of particles of hydrating components. The resulting images contain both visible and hidden information about the physical and chemical processes occurring in the material. To fully obtain such information, computer image processing methods were used. Intensity histograms were constructed and analyzed, for which regular changes were observed in the process of structure formation. As a generalization of the observed patterns, a working hypothesis is proposed about the filling of scales of physicochemical characteristics and, in particular, large-scale structural scales, in the process of structure formation. An image processing algorithm has been developed that makes it possible to construct isothermal cells‒areas of material with the same temperature. The geometric characteristics of the resulting areas, forming a partition of the study area, were studied by an automated method and reflected using histograms. The interpretation of the influence of a changing temperature distribution on the properties of a material is based on the idea of an approximate correspondence between a network of temperature cells and a Voronoi network that defines regions of a disordered structure. The spatiotemporal features of thermal processes are considered, indicating a possible decrease in the strength characteristics of the material.

Abstract: Wire ropes represent a distinct category of ropes synthesized through the intertwining or braiding of individual steel wires. This unique construction confers notable attributes such as strength, flexibility, and durability to the resultant rope. The pervasive wire ropes across diverse industries underscores their capacity to adeptly manage substantial loads and endure adverse environmental conditions finding its application in mechanical, civil, mining, and marine engineering. This paper presents usage of image processing method to detect the deflection of a steel wire rope. The system comprises of dividing the wire rope into different sections, spatial referencing, frame separation, color-based detection, morphological operations, data collection and visualization. The steel wire rope deflection program will allow designers to conveniently process the transverse deflection trajectories of a steel wire rope in real time. One may further introduce any control actions when the deflection distance reaches a threshold value. The image-based algorithm enabled a robust detection of the deformed shape as a function of time, thus obtaining its dynamic trajectories. The deflection shapes and trajectories are compared with numerical predictions made using Cosserat Rod theory, which considers the geometric nonlinearities introduced due to large deflection. The numerical solution gave a rough estimate of the static deformation state of the wire, which agrees with the experimental results. This study will be useful for Structural Health Monitoring, Safety Assurance, Fatigue Analysis and Performance optimization. Moreover, the Continuum Mechanics employs Cosserat rod theory to model continuum robots which will provide enhanced computational efficiency and better dynamic simulation capabilities[18]. The deflection shapes and trajectories are compared with analytical predictions made using Cosserat Rod theory, which considers the geometric nonlinearities introduced due to large deflection.

Abstract: Landsat satellite images are images that represent the ocean and land areas of the earth. Image data can be used for various purposes such as environmental analysis, remote sensing, mapping, and others. However, the quality of Landsat imagery is often unsatisfactory due to interference or noise from sources such as sensors, transmission, atmosphere, and storage. Therefore, they can reduce the contrast, sharpness, and information of landsat satellite images. Some of these disturbances prevent people from obtaining clear geographical locations. In order to overcome this problem, an effective and efficient method of Landsat satellite image quality improvement is needed. This research uses an image improvement method, namely discrete cosine transformation. The discrete cosine transformation method is used to reduce image noise by dividing it into each basic element. The method can perform the calculation process metematically and applicatively in the process of Landsat satellite image improvement. The processed results obtained are used to design and implement Landsat satellite image enhancement using the discrete cosine transformation method.

Abstract: In order to promote the sustainable growth of the wool industry and protect consumers' legitimate rights, rapid identification of the country of origin for wool of the same type is deemed crucial. This research presents a computer graphic recognition training model that utilizes median and Wiener filtering techniques to effectively reduce noise in the raw wool fiber images. Employing a support vector machine as the classifier and integrating a polynomial kernel function, this model achieves swift and accurate identification of Chinese and Australian Merino wool fibers. Experimental results underscore that following image recognition training, the model attains an impressive 92.5% comprehensive and precise identification rate for Chinese and Australian Merino wool fibers, effectively distinguishing the origin of wool from the same category. This approach not only provides a valuable reference for identifying the origin of similar wool types but also holds the potential to standardizing the wool fibre material market and assuring the consumer’s confidence on wool products.

Abstract: This study investigated the effects of tool runout on chatter vibration taking images of a machined surface to assess the vibration strength, number of vibrations, and phase difference depending on the spindle speed and axial depth of the cut. This study obtained significant results regarding the stability pocket represented by the spindle speed. We observed that the stability limit changed depending on tool runout.

Abstract: The paper proposed a deep convolutional neural network together with image processing techniques to detect assembly defects of vehicle components in assembly lines. Traditional detection method such as automatic optical inspection is strongly affected by environmental variation coming from the changes of light source, transfer belt, and component type, therefore, complicated thresholds should be adjusted case by case. The proposed method tries to avoid these problems which is fast and straight forward with satisfactory detection accuracy compared to traditional method.

Abstract: The molten steel level in twin-roll strip casting (TRC) has a significant impact on the heat transfer process between the molten steel and the rolls, as well as the subsequent solidification process of the steel. Therefore, ensuring a specific and stable molten steel level is crucial for the quality of as-casting strips. To achieve this, a precise and real-time molten steel level detection system is required. This paper utilizes machine vision technology to measure the molten steel level. A general mathematical model for the molten steel level in the TRC process is established. An image processing method for measuring the molten steel level using a single camera is proposed, including image segmentation, edge detection, and multiple coordinate transformations of the molten pool contour. The adverse effect of the inlet or nozzle is taken into account. Experimental measurements were conducted, and the results indicate that a single camera can accurately measure the molten steel level. Potential sources of error or limitations that may impact the accuracy of the proposed method is discussed.

Abstract: Nowadays new applications based on the 3D printing technique demand increasingly strict product quality requirements. The in-situ monitoring of variables associated with the manufacturing process through the application of different techniques could help to evaluate the process and ultimately to ensure product quality. In this regard, the acquisition and evaluation of variables and indexes derived from thermographic analysis during the process are key for an early defect detection and can contribute to quality estimation. In this work, a new methodology is proposed for the monitoring and analysis of the additive manufacturing process based on the processing of thermographic images from an LWIR (Long Wave Infrared) camera. The methodology and the suitability of the variables and indexes extracted during the monitoring of the manufacturing process are discussed for the case of a 3D fused filament fabrication of polymers.