The surface evaluation of shot peening process using image processing

Scilab is an open-source, cross-platform computational environment software available for academic and research purposes as a free of charge alternative to the matured computational copyrighted software such as MATLAB. One of important library available for Scilab is image processing toolbox dedicated solely for image and video processing. There are three major toolboxes for this purpose: Scilab image processing toolbox (SIP), Scilab image and video processing toolbox (SIVP) and recently image processing design toolbox (IPD). The target discussion in this paper is SIVP due to its vast use out there and its capability to handle streaming video file as well (note that IPD also supports video processing). Highlight on the difference between SIVP and IPD will also be discussed. From testing, it is found that in term of looping test, Octave and FreeMat are faster than Scilab. However, when converting RGB image to grayscale image, Scilab outperform Octave and FreeMat.

This article demonstrates the application of time-lapsed imaging and image processing to inform the supercritical processing of tissue scaffolds that are integral to many regenerative therapies. The methodology presented provides online quantitative evaluation of the complex process of scaffold formation in supercritical environments. The capabilities of the developed system are demonstrated through comparison of scaffolds formed from polymers with different molecular weight and with different venting times. Visual monitoring of scaffold fabrication enabled key events in the supercritical processing of the scaffolds to be identified including the onset of polymer plasticization, supercritical points and foam formation. Image processing of images acquired during the foaming process enabled quantitative tracking of the growing scaffold boundary that provided new insight into the nature of scaffold foaming. Further, this quantitative approach assisted in the comparison of different scaffold fabrication protocols. Observed differences in scaffold formation were found to persist, post-fabrication as evidenced by micro x-ray computed tomography (micro x-ray CT) images. It is concluded that time-lapsed imaging in combination with image processing is a convenient and powerful tool to provide insight into the scaffold fabrication process.

In coronary angiography (CAG) and percutaneous coronary intervention (PCI), it is important for radiological technologists to optimize the balance between radiation dose and image quality for physicians to be able to perform CAG and PCI most effectively. Evaluation of image processing is necessary to ensure that technologists can optimally adjust image quality for clinical use to the extent that physicians require. However, few phantoms are available for evaluating fluoroscopic image processing, and this makes it necessary to adjust image quality in clinical settings while utilizing the image processing according to the manufacturer’s recommendations. In this study, we developed a dynamic phantom that mimics a pulsating coronary artery for use in image quality analysis of moving images. We also examined whether processed images (image lag) can be physically analyzed. Two issues require special attention in creating a new phantom: establishing the exact position of the simulated blood vessel in the phantom, and providing good reproducibility. The study used the rotational motion of a disk to generate images, with a circular simulated blood vessel on the rotating acrylic disk, with the center of the simulated blood vessel shifted from the center of the acrylic disk. This enabled the reproduction of translational motion of the simulated blood vessel. As a result, because this phantom has signals and afterimages at the same position (of a simulated blood vessel), a quantitative evaluation of the afterimages became possible. In the evaluation of the image processing with the angiograph, it was shown that evaluations of image lag which are typically performed in clinical settings can be performed with the dynamic blood vessel phantom.

Effect of shot peening on static and fatigue properties of self-piercing riveting joints

WITHDRAWN: Optimization of shot peening process parameters using PSO algorithm to maximise the fatigue strength, flexural strength and surface hardness of AA2024-T3 alloy

The scanning and computerized processing of images hadits birth in 1956 at the National Bureau of Standards (NBS,nowNationalInstituteofStandardsandTechnology(NIST))[1].Image enhancementalgorithms weresome of the first tobe developed [2]. Half a century later, literally thousands ofimage processing algorithms have been published. Some ofthese have been specific to certain applications such as theenhancement of latent fingerprints, whilst others have beenmore generic in nature, applicable to all, yet master of none.The scope of these algorithms is fairly expansive, rangingfrom automatically extracting and delineating regions of in-terest such as in the case of segmentation, to improving theperceived quality of an image, by means of image enhance-ment. Since the early years of image processing, as in manysubfields of software design, there has been a portion of thedesign process dedicated to algorithm testing. Testing is theprocessofdeterminingwhetherornotaparticularalgorithmhassatisfieditsspecificationsrelatingtocriteriasuchasaccu-racy and robustness. A major limitation in the design of im-ageprocessingalgorithmsliesinthedifficultyindemonstrat-ingthatalgorithmsworktoanacceptablemeasureofperfor-mance. The purpose of algorithm testing is two-fold. Firstlyit provides either a qualitative or a quantitative method ofevaluating an algorithm. Secondly, it provides a comparativemeasure of the algorithm against similar algorithms, assum-ing similar criteria are used. One of the greatest caveats indesigning algorithms incorporating image processing is howtoconceivethecriteriausedtoanalyzetheresults.Dowede-signacriterionwhichmeasuressensitivity,robustness,orac-curacy? Performance evaluation in the broadest sense refersto a measure of some required behavior of an algorithm,whether it is achievable accuracy, robustness, or adaptabil-ity. It allows the intrinsic characteristics of an algorithm tobe emphasized, as well as the evaluation of its benefits andlimitations.More often than not though, such testing has been lim-ited in its scope. Part of this is attributable to the actual lackof formal process used in performance evaluation of im-age processing algorithms, from the establishment of testingregimes, to the design of metrics. Selection of an appropri-ate evaluation methodology is dependent on the objectiveof the task. For example, in the context of image enhance-ment, requirements are essentially different for screen-basedenhancementandenhancementwhichisembeddedwithinasubalgorithm. Screen-based enhancement is usually assessedinasubjectivemanner,whereaswhenanalgorithmisencap-sulated within a larger system, subjective evaluation is notavailable,andthealgorithmitselfmustdeterminethequalityof a processed image. Very few approaches to the evaluationofimageprocessingalgorithmscanbefoundintheliterature,although the concept has been around for decades. A signif-icant difficulty which arises in the evaluation of algorithmsis finding suitable metrics which provide an objective mea-sure of performance. A performance metric is a meaningfuland computable measure used for quantitatively evaluatingthe performance of any algorithm. Consider the process ofassessing image quality. There is no single quantitative met-ric which correlates well with image quality as perceived bythe human visual system. The process of analyzing failure isintrinsically coupled with the process of performance evalu-ation.Inordertoascertainwhetheranalgorithmfailsornot,youhavetodefinethecharacteristicsofsuccess.Failureanal-ysisistheprocessofdeterminingwhyanalgorithmfailsdur-ing testing. The knowledge generated is then fed back to thedesign process in order to engender refinements in the algo-rithm.Thisisadifficultprocessinapplicationssuchasimageenhancement primarily because there is usually no referenceimagewhichcanbeusedasan“ideal”image.Theassessmentofimagequalityplaysanimportantroleinapplicationssuchas consumer electronics. Metrics could be used to monitororoptimizeimagequalityindigitalcameras,benchmarkandevaluate image enhancement algorithms. There is no singlemetric that correlates well with image quality as perceivedby the human visual system. Selection of an appropriate

<div class="htmlview paragraph">Warm Shot Peening is a shot peening process within warm temperature range. However, the effect of warm shot peening to fatigue strength was not certain and no substantial study has been made.</div> <div class="htmlview paragraph">The requirements of coil spring with higher fatigue strength and sag resistance have been increasing to obtain the mass saving of vehicle. While several new spring materials have been developed in recent years, the essence of those developments were to make the strength(hardness) of spring higher, so-called high strengthened spring. As for high strengthened spring, the shot peening process becomes further essential to relieve the increased notch sensitivity due to higher strength. However, for the shot peening process to high strengthened spring, the shot with higher hardness is also required. This can decrease the life of both shot and shot peening equipments.</div> <div class="htmlview paragraph">Warm shot peening under the tempering temperature may become one solution to those problems, because the hardness of spring can be temporarily reduced only during the shot peening process. In this paper, the following experimental results concerned with the effect of warm shot peening, are summarized.</div> <div class="htmlview paragraph"> <ol class="list nostyle"> <li class="list-item"><span class="li-label">1</span><div class="htmlview paragraph">The relation between spring fatigue life and hardness under one warm shot peening temperature.</div></li> <li class="list-item"><span class="li-label">2</span><div class="htmlview paragraph">The relation between spring fatigue life and shot peening temperature.</div></li> <li class="list-item"><span class="li-label">3</span><div class="htmlview paragraph">Effects of residual stress, coverage, and surface hardness to fatigue life.</div></li> </ol> </div>

Welding is one of the most common approaches employed for fusing metals. However, welding errors are usually encountered during the process due to external factors. The detection and classification of these welding faults are of great importance for the reliability of the weld and the materials which were welded. Traditionally, welding error detection is performed through visual inspection carried out by inspectors or quality control personnel, which is an error-prone and slow process. In this paper, an image processing and machine learning based algorithm is proposed to automatically detect welding defects. The detection of component irregularities and classification is essential in quality control in manufacturing processes. Machine learning algorithms are widely employed for various applications as it reduces the precious human visualization time to classify the welded components manually and errors due this, which cannot be completely eliminated. The proposed algorithm also allows acceptance sampling or 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> inspection of the components as the speed of the classification process is within a few milliseconds with higher accuracy. The presented components are classified into Grades - A, B, C, and D with images obtained in visible spectrum. Experimental results prove that random forest classifier provided an accuracy of 82.8% compared to 77.6 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> of decision trees. Therefore, the welding evaluation process is made effective through machine learning based algorithms. The advantage is that automating the evaluation process makes it quicker and provides an unbiased grading.

The visual inspection of weld beads and subsequent evaluation of the weld quality is an integral part of the commercial welding environment. However, this inspection process tends to be both time and labor intensive. An automated system for the performance of this task has been developed. The sequence of events in the operation of the vision-based system are: (1) image capture; (2) image enhancement; (3) image processing; and (4) image evaluation. To minimize cost and complexity, the system uses conventional video camera and related hardware and software for the image-capture and image enhancement portion of the evaluation process. Various weld processes were observed to have certain characteristic features which were most relevant for the inspection and evaluation of the particular process. Image processing codes were written to extract those features of the weld beads and store this information in data files for subsequent assessment. Numerical algorithms were written, tailored to each of the weld processes, to perform the image evaluation portion of the quality evaluation process. This result is expressed as a relative quality rating which was found to correlate well with a quality rating derived by direct observation by a human inspector of various quality welds. The vision-based weld quality evaluation system has potential for use as a post-weld quality evaluation system, or, due to the high update rate of the overall vision system (>10 Hz), as part of a real-time control system.

Comparison of conventional and severe shot peening effects on the microstructure, texture, roughness, hardness, and electrochemical behavior of austenitic stainless steel

Predictive selection rule of favourable image processing methods for X-ray micro-computed tomography images of tablets

Wear, fatigue, and corrosion are just a few of the issues that mechanical components in engineering experience, leading to surface deterioration. Enhancing the surface characteristics of engineering components is therefore essential. The surface properties of engineering objects can be improved by applying different surface treatments. One of these processes is shot peening (SP). Process parameters are crucial for SP. This necessitates the optimization of SP process parameters. In this study, we applied SP and vibratory shot peening (VSP) processes to different steel materials (AISI 8620, AISI 5140, AISI 4140, and AISI 1020) using different process parameters, aiming to determine the effects of these parameters on hardness, residual stress, and surface roughness. The highest compressive residual stress (CRS) and hardness values for shot-peened samples were obtained at the 24–26 A intensity for all steels. For all steel-group VSP samples, the highest CRS and hardness values were obtained at the 60 s −4 mm parameter. This paper aims to predict Almen intensity values using CRS, surface roughness, and hardness values from various steels. The supplied experimental data was utilized to estimate the SP Almen intensity value using a number of machine learning (ML) methods, eliminating the need for costly and time-consuming experimentation. With an RMSE of 0.0731, R2 of 0.9665, and MAE of 0.0613, the deep neural network (DNN) surpassed the other models in terms of prediction accuracy. The results indicate that artificial intelligence technology could be utilized to accurately evaluate Almen intensity.

The principle of shot peening is to hit the surface of a metallic part with spherical shots to introduce a compressive residual stress field by plastification. The compressive stress introduced by shot peening lowers the number of cracks initiation on the material's surface. This process is thus used to increase fatigue life of many mechanical parts. The efficiency of shot peening depends on parameters such as the coverage rate or the size and speed of the shots. The values chosen for these parameters should be accurate to obtain the required fatigue life; investigations on the influence of the shot peening parameters, should be efficient and easy. A few analytical models have been proposed to better evaluate the residual stress field of a shot peened part, but most of the knowledge on shot peening processes today is empirical. In this study we chose to use a finite element model. Indeed, finite element codes are now powerful enough to simulate shot-peening processes extensively and to analyze the influence of the relevant parameters. This paper presents finite element simulations of shot peening processes and the residual stress fields obtained. We validated the model with a confrontation between numerical and experimental results. Two types of materials often used in aircraft and automobile industry were chosen for the study. The mechanical properties of these two materials were experimentally measured. In the same way, the coverage rate, the diameter and the speed of shots used to treat the surface of the materials were evaluated. Selected data were extracted from the literature and experimental measurements carried out in our laboratory. Three-dimensional models reproducing the experimental conditions were then investigated to simulate the shot peening of these materials. Results such as the displacement and the residual stress field are presented and compared to experimental results when possible. The models proposed show how the finite element simulation is able to predict the residual stress field of a shot peened part. The possibility to vary parameters values easily confirms the interest of shot peening numerical simulations.

The effects of different shot peening conditions such as pressure (from 68.9–551.6 kPa), media type (media S230 and S110) and double shot peening on the microstructure, mechanical and tribological properties of shot peened AISI 4340 low-alloy steels, were systematically investigated. Their surface topography, hardness and roughness were analyzed by scanning electron microscopy, a Vickers micro-hardness tester and surface profilometer, respectively, while the cross-sectional sub-surface structures of the as-received and shot peened samples were characterized by means of optical microscopy and nanoindentation. The tribological properties were determined by using a ball-on-disc micro-tribological tester. It was shown that shot peening pressure and media type had a significant effect on the surface roughness, hardness, microstructure, wear resistance and friction of the shot peened AISI 4340 steel samples. It also indicated that the double shot peening process was a promising treatment, not only to reduce the surface roughness of the material after undergoing the first shot peening process with large media and high intensity, but to improve the tribological properties of the shot peened material.

The field of image processing is experiencing significant advancements to support professionals in analyzing histological images obtained from biopsies. The primary objective is to enhance the process of diagnosis and prognostic evaluations. Various forms of cancer can be diagnosed by employing different segmentation techniques followed by postprocessing approaches that can identify distinct neoplastic areas. Using computer approaches facilitates a more objective and efficient study of experts. The progressive advancement of histological image analysis holds significant importance in modern medicine. This paper provides an overview of the current advances in segmentation and classification approaches for images of follicular lymphoma. This research analyzes the primary image processing techniques utilized in the various stages of preprocessing, segmentation of the region of interest, classification, and postprocessing as described in the existing literature. The study also examines the strengths and weaknesses associated with these approaches. Additionally, this study encompasses an examination of validation procedures and an exploration of prospective future research roads in the segmentation of neoplasias.