Small Surface Defects Research Articles

High-efficiency surface defect detection based on laser ultrasonic state space embedding and compressive sensing

Abstract The laser nonlinear wave modulation spectroscopy(LNWMS) technique has gained considerable attention due to its high sensitivity in detecting small surface defects and its ultra-fast scanning speed. This paper proposes a novel method for synthesizing intact wavefield reference, significantly enhancing the accuracy of surface defect imaging. Moreover, considering the potential for parallel processing of the nonlinearity calculation of ultrasonic signals at scanning points, we incorporate compressive sensing technology to accelerate this process. This innovative approach reduces the computational load to 10% of the original, thereby substantially increasing the imaging speed. The paper validates the method’s superior accuracy and efficiency in defect detection through conducting experiments using a high-speed laser ultrasonic scanning system on aluminum plates and turbine blade, and by comparing with local wavenumber estimation, demonstrating the promising potential of this technology for surface defect analysis.

A Review of Radiology and Optical Coherence Tomography in Dental Diagnostics

Background: The rapid evolution of imaging technologies has significantly transformed dentistry, improving the diagnosis and treatment of various dental conditions. While traditional radiological methods such as X-rays and Cone Beam Computed Tomography (CBCT) remain the cornerstone of dental imaging, the emergence of Optical Coherence Tomography (OCT) offers a non-invasive, radiation-free alternative. This review examines the comparative efficacy of X-ray and OCT technologies in diagnosing hard tissue conditions in the oral cavity. Methods: The review analyzed the application of OCT and radiology in dental diagnostics through a custom-built Swept-Source OCT system, assessing its performance against traditional radiological methods. The study utilized both in vivo and ex vivo evaluations, considering variables such as resolution, depth penetration, and image clarity across different dental conditions. Results: OCT demonstrated superior capabilities in detecting early-stage cavities, fractures, and minor imperfections, particularly when high-resolution imaging was required. However, its limited penetration compared to X-rays restricted its application in comprehensive bone assessments. Radiology, especially CBCT, provided essential volumetric data crucial for evaluating bone density, fractures, and post-surgical conditions, though it posed concerns related to radiation exposure. Combining both modalities proved beneficial, offering complementary insights that enhanced overall diagnostic accuracy. Conclusion: While X-ray-based radiology remains indispensable for broad evaluations of dental and bone structures, OCT presents a valuable adjunct, particularly for high-precision assessments of soft tissues and small surface defects. The synergistic use of these technologies can provide a more holistic approach to dental diagnostics, potentially improving patient care and treatment outcomes. Future advancements in OCT could further expand its role in routine dental practice, enhancing diagnostic accuracy while minimizing radiation exposure risks.

Theoretical limits of magnetic detection of structural surface defects at the nanometre scale

ABSTRACT We present a theoretical study on the magnetic signals of structural surface defects like cracks or indents combined with inhomogeneities on the surface or subsurface inclusions of soft ferromagnetic metals like body-centred cubic Fe or amorphous CoFeB. We discuss limits of early detection of small surface defects on the basis of calculated magnetic stray fields few tens of nm above the surface. The considered surface imperfections have extensions of a few nm which correspond to low multiples of the magnetic exchange lengths of Fe or CoFeB. The detection of such small inhomogeneities requires that the sensor is about as close to the surface as the size of the inhomogeneity is. Furthermore, the step width of a scanning sensor must be of the same size as well. Both these requirements may be fulfilled for instance by scanning microscopy with diamond nitrogen-vacancy-centre quantum sensors.

Surface defect detection of cylinder liner based on improved YOLOv5

Cylinder liner is an internal part of the automobile engine, which plays an important role in the automobile internal combustion engine. Therefore, it is a top priority to accurately and quickly detect the cylinder liner surface defects. In order to effectively achieve the classification and localization of surface defects on the cylinder liner, this paper establishes a dataset for surface defects on cylinder liner and proposes a based on improved YOLOv5 algorithm for detecting surface defects on cylinder liner. Firstly, a machine vision system is established to acquire on-site images and perform manual annotation to build the dataset of surface defects on cylinder liner. Secondly, the GSConv SlimNeck mechanism is introduced to reduce the model complexity; the Bi-directional Feature Pyramid Network (BiFPN) is used to fuse the feature information at different scales to enhance the detection accuracy of small surface defects on cylinder liner; and embedding the SimAM attention mechanism to focus on the object region of interest and improve the accuracy and robustness of the model. The final improved YOLOv5 model reduces the number of model parameters by 15.8% compared to the non-improved YOLOv5. And the experimental results on our self-built dataset for cylinder liner defects show that the mAP0.5 is improved by 0.4%. This means that the accuracy of model detection was not compromised. This method can be applied to actual production processes.

Spherical image acquisition and defect detection of nuclear fuel based on the line scan

Graphite balls are used as matrix materials of nuclear fuel. To ensure the safe use of nuclear energy, it is necessary to detect the surface defects of graphite balls to screen out the balls that do not meet safety requirements. However, due to the small surface defects and complex background of graphite balls, the detection accuracy based on traditional detection methods is difficult to meet engineering requirements. To solve the above problems, this paper designs an image acquisition device for graphite balls based on line scanning to collect spherical images and designs an improved algorithm based on the U2-Netp network to solve the problem of low accuracy of defect detection. Firstly, the algorithm replaces the up-sampling in the RSU module with cubic linear interpolation to enhance the smoothness of the image. Secondly, spatial channel attention and multi-feature fusion modules are built between the encoder and the decoder to improve the accuracy of the model to obtain interesting information. Finally, the prediction output of Sigmoid and Threshold fusion is designed to solve the problems caused by the gradient disappearance and the uneven polarity of positive and negative samples. Tested on the actual collected data set, the improved U2-Netp algorithm improved the average accuracy of detecting surface defects on graphite balls by 8.1% compared to the original U2-Netp algorithm, reaching 85.3%.

Fatigue performance of laser powder bed fusion manufactured TiB2/AlSi10Mg composite: Experimental investigation and fracture mechanics-based life prediction model for defect tolerance analysis

The fatigue performance of AlSi10Mg composites decorated with nano-TiB2 particles fabricated through laser powder bed fusion (LPBF) was first experimentally investigated at room temperature and it exhibited superior fatigue resistance compared with other LPBF AlSi10Mg alloys and other reinforced AlSi10Mg composites. Then the fractography was examined and a fracture mechanics-based life prediction method was proposed to correlate the critical defect information (size and location) and fatigue life. To assess the crack growth driving force for critical defects, we employed Murakami's concept of the maximum stress intensity factor (SIF). The modification was made to include the effect of specimen cross-sectional geometry and initiation location since Murakami’s method would underestimate the maximum SIF in some cases. Meanwhile, the process of small crack propagation was also considered in modeling, and a unified model to describe crack arrest, small crack growth, and long crack growth was established. By inputting information about defect size and location and external loading conditions, the fatigue life could be evaluated and our results demonstrated good estimation within the [1/4,2] scatter band. Furthermore, an unbroken specimen subjected to the highest external loading was examined using X-ray computed tomography (CT) to characterize the defect population. By incorporating the CT statistical results into our life prediction model, outcomes such as non-propagating cracks or small crack growth without leading to ultimate failure could be obtained, which was consistent with CT findings. Concurrently, CT analysis revealed that the detrimental effect of large inner defects could be less significant than that of small surface defects. Therefore, we propose a novel strategy that combines defect size and location to enhance defect tolerance in additive manufacturing (AM) materials.

Digital Twin of Intelligent Small Surface Defect Detection with Cyber-manufacturing Systems

With the remarkable technological development in cyber-physical systems, industry 4.0 has evolved by use of a significant concept named digital twin (DT). However, it is still difficult to construct a relationship between twin simulation and a real scenario considering dynamic variations, especially when dealing with small surface defect detection tasks with high performance and computation resource requirements. In this article, we aim to construct cyber-manufacturing systems to achieve a DT solution for small surface defect detection task. Focusing on DT-based solution, the proposed system consists of an Edge–Cloud architecture and a surface defect detection algorithm. Considering dynamic characteristics and real-time response requirement, Edge–Cloud architecture is built to achieve smart manufacturing by efficiently collecting, processing, analyzing, and storing data produced by factory. A deep learning–based algorithm is then constructed to detect surface defeats based on multi-modal data, i.e., imaging and depth data. Experiments show the proposed algorithm could achieve high accuracy and recall in small defeat detection task, thus constructing DT in cyber-manufacturing.

Non-dimensional scaling of passive adaptive blades for a marine current turbine

For tidal energy to support access to off-shore electricity, further development is needed to decrease costs and increase reliability of current turbines at relevant scales. Blade pitch control strategies can significantly reduce structural loads in above-rated flow conditions by shedding power through decreased angles of attack. This can be accomplished through an active strategy using motorized blades or a passive adaptive strategy using flexible, self-twisting blades. We focus this study on the passive adaptive approach in which the composite fibers of the blade are oriented off-axis to produce a coupling between bend and twist deformations.
 Extending laboratory results to larger, open-water designs requires an understanding of hydrodynamic and hydroelastic scaling. While dimensionless scaling relations have been extensively studied for current turbines with rigid blades, relatively few studies discuss appropriate hydroelastic scaling for passive adaptive blades. In this study, we experimentally apply non-dimensional scaling laws to laboratory-scale passive adaptive turbine blades and demonstrate similarity in blade deformation and non-dimensional loads across scales.
 When Cauchy similarity is achieved between model and full-scale, the same steady-state blade loading and blade deformation are expected. We define Cauchy number as Ca = ρUo2/E, where ρ is the water density, Uo is the freestream velocity upstream of the turbine, and E is the transverse flexural modulus of the blade (i.e., elasticity corresponding to bending in the flapwise direction). We tested the effectiveness of Cauchy-scaling by designing an experiment in which blade bending stiffness and flow speed varied, but Cauchy number remained constant. The first blade used a 7-ply carbon fiber spar while the second blade used a 5-ply carbon fiber spar, both fabricated with unidirectional fibers oriented 10° off-axis and cast in a semi-rigid polyurethane using the same mold. All other non-dimensional parameters relevant to hydrodynamic scaling were held constant, where possible.
 As hypothesized, we observed agreement in thrust coefficient, deflection, and twist when Cauchy similarity was achieved, particularly when flow remained attached over the entire blade span. Small differences of 0-7% were observed in normalized thrust, deflection, and twist compared to 50-65% when Cauchy number was allowed to vary by 50%. We did not observe this similarity for normalized mechanical power between the 5-ply and 7-ply blades, but hypothesize that the source of the disagreement was a small surface defect in the urethane on the 5-ply blade. The experiment will be repeated to confirm this hypothesis and included in future presentations of this work.
 Our experimental result partially demonstrates the effectiveness of using Cauchy number to scale passive adaptive marine current turbine blades and model their steady-state hydrodynamic and hydroelastic behaviors in a consistent, non-dimensional manner. Accurate experimental models are critical to support the development of passive adaptive blades, which may obviate the need for an active pitch mechanism, thereby increasing reliability and decreasing maintenance costs. Finally, we present initial results from a field-scale turbine equipped with rigid and passive adaptive blades, demonstrating a path towards validating our conclusions from lab-scale testing.

A small sample nonstandard gear surface defect detection method

To address the issues of low detection precision, slow detection speed, and imbalanced defect samples in the surface defect detection of nonstandard parts, we propose a method based on the defect detection model YOLOv8-2d and the balancing generative adversarial network (BAGAN). First, we use an attention mechanism without parameters to help the detection model focus on the defect area in the middle-level features based on the feature scale and distribution characteristics of surface defects on nonstandard parts. Second, we introduce deformable convolution and propose a path aggregation feature pyramid network (PADSFPN) with depth-separable convolution, effectively integrating multiscale feature information. Then, we use a Mish network with WIoU as the network head and extract the position and category information of the defect separately, learn through different network branches, and merge the information. Finally, we use BAGAN to expand the small sample defect dataset on the surface of nonstandard parts to achieve dataset balance. The proposed improvements are evaluated through comparative experiments in published twenty test groups, and the effectiveness of the proposed method is demonstrated. Compared with the original model, our method achieves a defect detection precision of 99.83%, a 7.7% improvement; an inference speed of 455 FPS, a 374% increase; and a mAP50 of 97.3%, a gain of 5.03%. Our proposed method maintains high precision while reducing the model's computational complexity and parameter volume, improving its efficiency and robustness in surface defect detection on nonstandard parts.

Very High Cycle Fatigue Data Acquisition Using High-Accuracy Ultrasonic Fatigue Testing Equipment

Abstract Fatigue testing at ultrasonic frequency is a powerful method for rapid generation of high and very high cycle fatigue data. High accuracy of cyclic loading is the basis for reproducible test results, which can be obtained with closed-loop control of vibration amplitude and resonance frequency. Self-heating of test specimens can be suppressed by intermittent (pulse-pause) loading. In the present work, the high-accuracy ultrasonic fatigue testing equipment developed at the University of Natural Resources and Life Science, Vienna (BOKU) is described, and systematic investigations are presented that demonstrate the effects of cycling frequency, specimen size, and intermittent loading. Data obtained with additively manufactured (AMed) austenitic stainless steel 316L at 19 kHz are compared with electromagnetic resonation test results measured at 90 Hz. No influence of cycling frequency on fatigue lifetimes and fatigue limit was found when specimens of comparable size were used for tests at 19 kHz and 90 Hz. In contrast, the use of as-built specimens with smaller testing volume resulted in an increase in cyclic strength, demonstrating a size effect on fatigue properties. Polishing the surfaces of AMed specimens increased the fatigue limit by a factor of two. Furthermore, ultrasonic fatigue tests with intermittent loading and four different pulse lengths as well as continuous cycling were performed. The tests were conducted with wrought aluminum alloy 7075-T651 and with specimens containing small artificial surface defects. Similar lifetimes were measured in all testing series. Moreover, the same fatigue limit is found for all pulse lengths and for continuous cycling. This demonstrates that pulsed loading is a most suitable method to avoid specimen heating at ultrasonic frequency and that there is no influence on measured fatigue properties if ultrasonic loading is appropriately controlled. Measurement artifacts could be identified and avoided by measuring and recording all load amplitudes during ultrasonic fatigue tests.

Weighted Matrix Decomposition for Small Surface Defect Detection.

Detecting small defects against a complex surface is highly challenging but crucial to ensure product quality in industry sectors. However, in the detection performance of existing methods, there remains a huge gap in the localization and segmentation of small defects with limited sizes and extremely weak feature representation. To address the above issue, this paper presents a weighted matrix decomposition model (WMD) for small defect detection against a complex surface. Firstly, a weighted matrix is constructed based on texture characteristics of RGB channels in the defect image, which aims to improve contrast between defects and the background. Based on the sparse and low-rank characteristics of small defects, the weighted matrix is then decomposed into low-rank and sparse matrices corresponding to the redundant background and defect areas, respectively. Finally, an automatic threshold segmentation method is used to obtain the optimal threshold and accurately segment the defect areas and their edges in the sparse matrix. The experimental results show that the proposed model outperforms state-of-the-art methods under various quantitative evaluation metrics and has broad industrial application prospects.

Small samples data augmentation and improved MobileNet for surface defects classification of hot-rolled steel strips

Surface defects in hot-rolled steel strips are one of the common product problems for the steel industry, which harm the product appearance, affect the corrosion and wear resistance, and shorten the product service life. The natural defect samples are sparse, category imbalanced, and expensive manual annotations. Therefore, it is crucial to study the data augmentation and classification methods for small sample surface defects. To solve the above problems and improve the accuracy and real-time performance of defect classification, we propose a random offline data augmentation algorithm (Random-CutMix) and an improved MobileNet architecture (SP-MobileNet). The Random-CutMix algorithm expands the dataset by random sampling to balance the number of each defect class. The SP-MobileNet combines the inverse residual module with the channel shuffle mechanism (CSIn-Module) and pyramid split attention (PSA) module, which facilitates cross-group information flow and improves model representation capability and generalization performance with low computational cost. The accuracy, recall, F1 score, parameter, computational complexity, and frame rate of SP-MobileNet with Random-CutMix on the X-SDD dataset were 95.97%, 95.22%, 95.46%, 6.5 M, and 0.54 G, 72 FPS, respectively. The experiment results indicate that our method outperforms the state-of-the-art methods and provides an effective trade-off between accuracy and instantaneity in actual industrial production.

A Lightweight Modified YOLOX Network Using Coordinate Attention Mechanism for PCB Surface Defect Detection

Surface defect detection for the printed circuit board (PCB) is essential in PCB manufacturing. Existing defect detection networks have several problems: low detection efficiency, high memory consumption, and low sensitivity to small defects. To address these issues, we propose a new lightweight deep-learning-based defect detection network, YOLOX with a modified CSPDarknet and coordinate attention (YOLOX-MC-CA). YOLOX-MC-CA is developed on the YOLOX and uses the coordinate attention (CA) mechanism to improve the recognition capability of small PCB surface defects. The backbone network in YOLOX is also modified into a new CSPdarknet structure with some inverted residual blocks. The modified CSPDarknet (MC) backbone network helps the YOLOX decrease the number of parameters on the premise of guaranteeing the feature extraction ability. We evaluated the YOLOX-MC-CA with an augmented dataset based on a public PCB surface defect dataset. Compared to the squeeze-and-excitation (SE) module, convolutional block attention module (CBAM), and other approaches in previous research, the CA mechanism improves the network with more detection precision for the small PCB surface defects. The experimental results demonstrate that our network is superior to other state-of-the-art (SOTA) networks for PCB surface defect detection, scoring 99.13% on mean average precision (mAP) and 47.6 frames per second (FPS) on detection speed, only occupying a parameter space of 3.79 million (M). It demonstrates that the proposed network is more suitable for deployment on embedded systems.

(Invited) Small Polarons and Surface Defects in Metal Oxide Photocatalysts Studied Using Xuv Reflection-Absorption Spectroscopy

In photocatalytic transition metal oxides, the surface electronic structure and carrier kinetics are complicated by charge trapping at defects as well as by carrier self-trapping as small polarons. These processes are unique to the material interface, where surface carrier dynamics deviate from bulk behavior due to the presence of surface defect states, undercoordination, or interface bonding. This talk will highlight the ability to directly visualize these processes with surface sensitivity, chemical state resolution, and ultrafast time resolution using extreme ultraviolet (XUV) reflection-absorption spectroscopy. This method measures element-specific, core-to-valence spectra analogous to x-ray absorption, but with the added benefits of surface sensitivity and ultrafast time resolution. Using this method, we investigate surface trapping and recombination in α-Fe2O3 and TiO2 as two prototypical metal oxide photocatalysts. In α-Fe2O3, we observe ultrafast polaron formation with a rate that is three times slower than observed in bulk. Kinetic analysis reveals that this is a result of increased lattice relaxation energy for polaron formation at a surface, and we show that these rates can be rationally tuned by surface molecular functionalization. In contrast, in photoexcited TiO2 we observe that hot holes trap as small polarons at oxygen vacancy defects in approximately 50 fs, and hot electrons couple to polar optical phonons leading to vibrational coherence and large polaron formation in approximately 1 ps. These findings provide examples of the unique opportunities enabled by XUV spectroscopy for understanding surface electron dynamics in photocatalytic systems.

A supervised approach for automated surface defect detection in ceramic tile quality control

Surface defect detection is very important to guarantee the quality of ceramic tiles production. At present, this process is usually performed manually in the ceramic tile industry, which is low efficiency and time-consuming. For small surface defects detection of high-resolution ceramic tiles image, an intelligent detection method for surface defects of ceramic tiles based on an improved you only look once version 5 (YOLOv5) algorithm is presented. Firstly, the high-resolution ceramic tile images are cropped into slices, and the Bottleneck module in the YOLOv5s network is optimized by introducing depthwise convolution and replaced in the whole network. Then, feature extraction is performed using the improved Shufflenetv2 backbone, and an attention mechanism is added to the backbone network to improve the feature extraction ability. The path aggregation network (PAN) and Feature Pyramid Networks (FPN) neck are used to enhance the feature extraction, and finally, the YOLO head is used to identify and locate the ceramic tile defects. The multiple sliding windows detection method is proposed to detect the original ceramic tile image which is faster than the single sliding window detection method. The experimental results show that compared with the original YOLOv5s detection algorithm, the parameters of the model are reduced by 20.46 %, the floating point operations are reduced by 26.22 %, and the mean average precision (mAP) of the proposed method is 96.73 % in the ceramic tile image slice test set which has 1.93 % improvement in mAP than the original YOLOv5s. Compare with other object detection methods, the method proposed in this paper also has certain advantages. In the high-resolution ceramic tile images test set, the mAP of the proposed algorithm is 86.44 % by using the multiple sliding window detection method. The ceramic defect detection experiment has verified the feasibility of the method proposed in this paper.

Multi-frequency imaging with non-linear calibration of magnetoresistance sensors for surface and buried defects inspection

Timely inspecting defects has both safety and economic significance in industry. Eddy current testing (ECT) probes with array sensors are widely used for metal structures inspection. However, the sensitivities of array sensors are typically not consistent, resulting in different responses when measuring the same defect signal. Therefore, the array sensors of an array probe are desired to be calibrated. This paper proposes an ECT probe with an array of tunnel magnetoresistance (TMR) sensors and two excitation coils for surface and buried defects inspection. Alternating currents with different frequencies and amplitudes are driven through the two coils simultaneously. The TMR sensors have non-linear resistance vs. magnetic field curves. A non-linear calibration method based on the non-linear characteristic is proposed to calibrate the high frequency signals of the sensors. This calibration method does not need any extra equipment and takes account of the effect of the non-uniformity of the excitation magnetic field to some extent. Experimental result shows that the signal becomes much flatter after the calibration. The relative flat index is reduced from 38.6% to 11% by the calibration process. To validate the detectability of the proposed probe for different kinds of defects, surface and buried defects in aluminum samples are tested by the probe. It is seen that a small surface defect with sizes 1 mm (length) x 0.5 mm (width) x 0.5 mm (height) can be clearly identified from the high-frequency image. Two defects buried 3 mm below the top surface are also inspected. The peak signal values of the two defects are 0.69 V and 0.97 V, respectively, demonstrating that the probe can distinguish the 0.1 mm height difference of the two defects. In addition, a defect buried 10 mm under the surface is imaged by the probe. Thanks to the special probe design and excellent sensitivity of the TMR sensors, the probe has good sensitivity for both surface and buried defects.

Eddy Current Microsensor and RBF Neural Networks for Detection and Characterization of Small Surface Defects

Abstract The growing complexity of industrial processes and manufactured parts, the growing need for safety in service and the desire to optimize the life of parts, require the implementation of increasingly complex quality assessments. Among the various anomalies to consider, sub-millimeter surface defects must be the subject of particular care. These defects are extremely dangerous as they are often the starting point for larger defects such as fatigue cracks, which can lead to the destruction of the parts. Penetrant testing is now widely used for this type of defect, due to its good performance. Nevertheless, it should be abandoned eventually due to environmental standards. Among the possible alternatives, the use of eddy currents (EC) for conductive materials is a reliable, fast, and inexpensive alternative. The study concerns the design and modeling of eddy current probe structures comprising micro-sensors for non-destructive testing. The moving band finite element method is implemented for this purpose to take into account the movement of the sensor, experimental validations were conducted on a nickel-based alloy specimen. The real and imaginary parts of the impedance at every position of the sensor computed by experiments and simulations were in good agreement. The crack detection quality was quantified and the geometric characteristics of the defects were estimated using RBF NN (Radial Basis Function Neural Networks) that were designed and implemented on the acquired signals.

Polymeric Composite Reinforced with PET Fiber Waste for Application in Civil Construction as a Cladding Element.

The construction industry contributes enormously to the high levels of carbon dioxide on the planet. For this reason, the sector has been investing in the development of new products that reduce the environmental impact. This study developed a fibrous polymeric composite using industrial residues of polyethylene terephthalate (PET) fibers for application in civil construction as a cladding element. The thermal and morphological characterization of the fiber was performed using Thermogravimetry (TG) and Scanning Electron Microscopy (SEM). Composites with 1, 3, and 5% PET fibers were obtained. Mechanical, morphological properties, chemical resistance, the effect of ultraviolet radiation and water absorption of the composites were evaluated. The results were compared to parameters established by the Brazilian standard NBR 15.575-3. Fibers had a smooth surface but with small surface defects, diameter between 20 and 30 µm and thermal stability up to 325.44 °C. The addition of 5% PET fibers resulted in an increase of more than 300% in the impact resistance of the composites, but with a reduction in the flexural strength. The mechanical and chemical resistance results met the parameters established by the standard used in the study. The degradation chamber test indicated that PET fibers suffered more from exposure to ultraviolet radiation than the polymeric matrix.

Surface Finishing of Additive Manufactured Ti-6Al-4V Alloy: A Comparison between Abrasive Fluidized Bed and Laser Finishing.

Metal additive manufacturing is a major concern for advanced manufacturing industries thanks to its ability to manufacture complex-shaped parts in materials that are difficult to machine using conventional methods. Nowadays, it is increasingly being used in the industrial manufacturing of titanium-alloy components for aerospace and medical industries; however, the main weakness of structural parts is the fatigue life, which is affected by surface quality, meaning the micro-cracking of small surface defects induced by the manufacturing process. Laser finishing and Abrasive Fluidized Bed are proposed by the authors since they represent cost-effective and environment-friendly alternatives for automated surface finishing. A comparison between these two finishing technologies was established and discussed. Experimental tests investigated both mechanical properties and fatigue performances. The tests also focused on understanding the basic mechanisms involved in fatigue failures of machined Ti-6Al-4V components fabricated via Electron Beam Melting and the effects of operational parameters. X-ray tomography was used to evaluate the internal porosity to better explain the fatigue behaviour. The results demonstrated the capability of Laser finishing and Abrasive Fluidized Beds to improve failure performances. Life Cycle Analysis was additionally performed to verify the effectiveness of the proposed technologies in terms of environmental impact and resource consumption.

Si3N4 Ceramic Ball Surface Defects’ Detection Based on SWT and Nonlinear Enhancement

In order to improve the detection accuracy and efficiency of silicon nitride ceramic ball surface defects, a defect detection algorithm based on SWT and nonlinear enhancement is proposed. In view of the small surface defect area and low contrast of the silicon nitride ceramic ball, a machine vision-based nondestructive inspection system for surface images is constructed. Sobel operation is used to eliminate the nonuniform background, and the silicon nitride ceramic ball surface image is decomposed by SWT. And frequency-domain index low-pass filtering is used to modify the decomposition coefficients, and an adaptive nonlinear model is proposed to enhance defects; finally, the image is reconstructed and segmented by the stationary wavelet inverse transform and the dynamic threshold method, respectively. The enhanced algorithm can effectively identify surface defects and is superior to traditional defect detection algorithms.