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This study aims to investigate the effect of countersunk rivet head dimensions on the fatigue performance of the riveted specimens of 2024-T3 alloy.

The relationship between rivet head dimensions and fatigue behavior was investigated by finite element method and fatigue test. The fatigue fracture of the specimens was analyzed by scanning electron microscopy.

A change of the rivet head dimensions will cause a change in the stress concentration and residual normal stress, the stress concentration near the rivet hole causes the fatigue crack source to be located on the straight section of the countersunk rivet hole and the residual normal stress can effectively restrain the initiation and expansion of fatigue cracks. The fatigue cycle will cause the rivet holes to produce different degrees of surface wear.

The fatigue life of the specimens with the height of the rivet head of 2.28 mm and 2.00 mm are similar, but the specimens with the height of the rivet head of 1.72 mm were far higher than the other specimens.

The aim of this paper is to study the effect of laser shock peening (LSP) on mechanical behaviour of the laser-directed energy deposition (LDED)-based printed 15-5 PH stainless steel with U and V notches. The study specifically concentrates on the evaluation of effect of scan strategy, machining and LSP processing on microstructural, texture evolution and fatigue behaviour of LDED-printed 15-5 PH steel.

For LSP treatment, 15-5 PH steel was printed using LDED process with bidirectional scanning strategy (XX [θ = 0°) and XY [θ = 90°]) at optimised laser power of 600 W with a scanning speed of 300 mm/min and a powder feed rate of 3 g/min. Furthermore, LSP treatment was conducted on the V- and U-notched fatigue specimens extracted from LDED-built samples at laser energy of 3.5 J with a pulse width of 10 ns using laser spot diameter of 3 mm. Post to the LSP treatment, the surface roughness, fatigue life assessment and microstructural evolution analysis is performed. For this, different advanced characterisation techniques are used, such as scanning electron microscopy attached with electron backscatter diffraction for microstructure and texture, X-ray diffraction for residual stress (RS) and structure information, Vicker’s hardness tester for microhardness and universal testing machine for low-cycle fatigue.

It is observed that both scanning strategies during the LDED printing of 15-5 PH steel and laser peening have played significant role in fatigue life. Specimens with the XY printing strategy shows higher fatigue life as compared to XX with both U- and V-notched conditions. Furthermore, machining and LSP treatment led to a significant improvement of fatigue life for both scanning strategies with U and V notches. The extent of increase in fatigue life for both XX and XY scanning strategy with V notch is found to be higher than U notch after LSP treatment, though without LSP samples with U notch have a higher fatigue life. As fabricated sample is found to have the lowest fatigue life as compared to machines and laser peened with both scan strategies.

This study presents an innovative method to improve the fatigue life of 15-5 PH stainless steel by changing the microstructure, texture and RS with the adoption of a suitable scanning strategy, machining and LSP treatment as post-processing. The combination of preferred microstructure and compressive RS in LDED-printed 15-5 PH stainless steel achieved with a synergy between microstructure and RS, which is responsible to improve the fatigue life. This can be adopted for the futuristic application of LDED-printed 15-5 PH stainless steel for different applications in aerospace and other industries.

The purpose of this study is to reduce the residual stress in welded workpieces, optimize the vibratory stress relief treatment process through the use of a vibration generator and enhance the durability and longevity of the workpiece by developing a vibratory stress relief robot that incorporates a multi-manipulator system.

The multi-manipulator combination work is designed so that each manipulator is deployed according to the requirements of vibration stress relief work. Each manipulator works independently and coordinates with others to achieve multi-dimensional vibratory stress relief of the workpiece. A two-degree-of-freedom mobile platform is designed to enable the transverse and longitudinal movement of the manipulator, expanding the working space of the robot. A small electromagnetic superharmonic vibration generator is designed to produce directional vibrations in any orientation. This design addresses the technical challenge of traditional vibration generators being bulky and unable to achieve directional vibrations.

The residual stress relief experiment demonstrates that the residual stress of the workpiece is reduced by approximately 73% through three-degree-of-freedom vibration. The multi-dimensional vibration effectively enhances the relief effect of residual stress, which is beneficial for improving the strength and service life of the workpiece.

A new multi-manipulator robot is proposed to alleviate the residual stress generated by workpiece welding by integrating vibratory stress relief with robotics. It is beneficial to reduce material and energy consumption while enhancing the strength and service life of the workpiece.

In this study, two postprocessing techniques, namely, conventional burnishing (CB) and ultrasonic-assisted burnishing (UAB), were applied to improve the fatigue behavior of 316 L stainless steel fabricated through selective laser melting (SLM). The effects of these processes on surface roughness, porosity, microhardness and fatigue performance were experimentally investigated. The purpose of this study is to evaluate the feasibility and effectiveness of ultrasonic-assisted burnishing as a preferred post-processing technique for enhancing the fatigue performance of additively manufactured components.

All samples were subjected to a sandblasting process. Next, the samples were divided into three distinct groups. The first group (as-Built) did not undergo any additional postprocessing, apart from sandblasting. The second group was treated with CB, while the third group was treated with ultrasonic-assisted burnishing. Finally, all samples were evaluated based on their surface roughness, porosity, microhardness and fatigue performance.

The results revealed that the initial mean surface roughness (Ra) of the as-built sample was 11.438 µm. However, after undergoing CB and UAB treatments, the surface roughness decreased to 1.629 and 0.278 µm, respectively. Notably, the UAB process proved more effective in eliminating near-surface pores and improving the microhardness of the samples compared to the CB process. Furthermore, the fatigue life of the as-built sample, initially at 66,000 cycles, experienced a slight improvement after CB treatment, reaching 347,000 cycles. However, the UAB process significantly enhanced the fatigue life of the samples, extending it to 620,000 cycles.

After reviewing the literature, it can be concluded that UAB will exceed the capabilities of CB in terms of enhancing the surface roughness and, subsequently, the fatigue performance of additive manufactured (AM) metals. However, the actual impact of the UAB process on the fatigue life of AM products has not yet been thoroughly researched. Therefore, in this study, this paper used the burnishing process to enhance the fatigue life of 316 L stainless steel produced through the SLM process.

Sufficient sample data are the necessary condition to ensure high reliability; however, there are relatively poor fatigue test data in the engineering, which affects fatigue life's prediction accuracy. Based on this, this research intends to analyze the fatigue data with small sample characteristics, and then realize the life assessment under different stress levels.

Firstly, the Bootstrap method and the principle of fatigue life percentile consistency are used to realize sample aggregation and information fusion. Secondly, the classical outlier detection algorithm (DBSCAN) is used to check the sample data. Then, based on the stress field intensity method, the influence of the non-uniform stress field near the notch root on the fatigue life is analyzed, and the calculation methods of the fatigue damage zone radius and the weighting function are revised. Finally, combined with Weibull distribution, a framework for assessing multiaxial low-cycle fatigue life has been developed.

The experimental data of Q355(D) material verified the model and compared it with the Yao’s stress field intensity method. The results show that the predictions of the model put forward in this research are all located within the double dispersion zone, with better prediction accuracies than the Yao’s stress field intensity method.

Aiming at the fatigue test data with small sample characteristics, this research has presented a new method of notch fatigue analysis based on the stress field intensity method, which is combined with the Weibull distribution to construct a low-cycle fatigue life analysis framework, to promote the development of multiaxial fatigue from experimental studies to practical engineering applications.

Incipient detection of pavement deterioration (such as crack identification) is critical to optimizing road maintenance because it enables preventative steps to be implemented to mitigate damage and possible failure. Traditional visual inspection has been largely superseded by semi-automatic/automatic procedures given significant advancements in image processing. Therefore, there is a need to develop automated tools to detect and classify cracks.

The literature review is employed to evaluate existing attempts to use Hough transform algorithm and highlight issues that should be improved. Then, developing a simple low-cost crack detection method based on the Hough transform algorithm for pavement crack detection and classification.

Analysis results reveal that model accuracy reaches 92.14% for vertical cracks, 93.03% for diagonal cracks and 95.61% for horizontal cracks. The time lapse for detecting the crack type for one image is circa 0.98 s for vertical cracks, 0.79 s for horizontal cracks and 0.83 s for diagonal cracks. Ensuing discourse serves to illustrate the inherent potential of a simple low-cost image processing method in automated pavement crack detection. Moreover, this method provides direct guidance for long-term pavement optimal maintenance decisions.

The outcome of this research can help highway agencies to detect and classify cracks accurately for a very long highway without a need for manual inspection, which can significantly minimize cost.

Hough transform algorithm was tested in terms of detect and classify a large dataset of highway images, and the accuracy reaches 92.14%, which can be considered as a very accurate percentage regarding automated cracks and distresses classification.

The nickel-based alloys Inconel 625 and Inconel 718 stand out due to their high strength and corrosion resistance in important industries like aerospace, aviation and automotive. Even though they are widely used, current techniques of producing materials that are difficult to cut pose several problems from a financial, ecological and even health perspective. To handle these problems and acquire improved mechanical and structural qualities, laser powder bed fusion (LPBF) has been widely used as one of the most essential additive manufacturing techniques. The purpose of this article is to focus on the state of the art on LPBF parts of Inconel 625 and Inconel 718 for microstructure, mechanical behavior and postprocessing.

The mechanical behavior of LPBF-fabricated Inconel is described, including hardness, surface morphology and wear, as well as the influence of fabrication orientation on surface quality, biocompatibility and resultant mechanical properties, particularly tensile strength, fatigue performance and tribological behaviors.

The postprocessing techniques such as thermal treatments, polishing techniques for surface enhancement, mechanical and laser-induced peening and physical operations are summarized.

The highlighted topic presents the critical aspects of the advantages and challenges of the LPBF parts produced by Inconel 718 and 625, which can be a guideline for manufacturers and academia in practical applications.

Designing a sustainable bituminous concrete with long-term performance is a challenging problem. In addition, strength of the subgrade has a crucial impact on pavement design. This paper aims to concentrate on subgrade soil stabilization with granite dust powder (GDP) and crumb rubber powder (CRP) to improve the engineering properties of the soil. Further design of bituminous concrete pavement with cement-treated layers in base and subbase course layers was carried out with life cycle cost analysis and life cycle assessment for 1 km of a four-lane national highway.

Subgrade soil stabilized with GDP and CRP is characterized as per Indian Standards (IS)-2720 to determine the optimum dosage. Further, the mechanistic-empirical pavement design was carried out using Indian Road Congress-37 (2018), analyzed using IITPAVE software and validated with ANSYS software. The life cycle cost analysis is carried out using the net present value method, and the life cycle assessment is performed according to the cradle-to-grave approach.

A soil mix comprising 10% GDP and 2.5% CRP yielded a soaked California bearing ratio value of 6.58%. In addition, the design of bituminous concrete pavement with cement-treated granular layers showed a 26.9% reduction in life cycle cost and 59.4% reduction in total carbon footprint per kilometer compared to the pavement with traditional aggregate layers.

The research on subgrade stabilization with sustainable materials like GDP and CRP incorporating mechanistic empirical pavement design, life cycle cost analysis and life cycle assessment is limited. Overall, the study recommends the use of GDP and CRP to stabilize soil for subgrade application and incorporate cement-treated granular layers, which offer economic and environmental benefits compared to traditional pavement construction.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

In this research, the free vibration sensitivity analysis of cracked fiber metal laminated (FML) beams is investigated numerically and experimentally. The effects of single and double cracks on the frequency of the cantilever beams are simulated using the finite element method (FEM) and compared to the experimental results.

In FEM analysis, the crack defect is simulated by the contour integral technique without considering the crack growth. The specimens are fabricated with an aluminum sheet, woven carbon fiber and epoxy resin. The FML specimens are constructed by bonding five layers as [carbon fiber-epoxy/Al/carbon fiber-epoxy/Al/carbon fiber-epoxy]. First, the location and length of cracks are considered input factors for the frequency sensitivity analysis. Then, the design of the experiment is produced in the cases of single and double cracks to compute the frequency of the beams in the first and second modes using the FEM. The mechanical shaker is used to determine the natural frequency of the specimens. In addition, the predicted response values of the frequency for the beam are used to compare with the experimental results.

Consequently, the results of the sensitivity analysis demonstrate that the location and length of the crack have significant effects on the modes.

Effective interaction diagrams are introduced to investigate crack detection for input factors, including the location and length of cracks in the cases of single and double cracks.