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This paper aims to discuss the development of a defect classification system that can be used to detect and classify powder bed surface defects from captured layer images without the need for specialised computational hardware. The idea is to develop this system by making use of more traditional machine learning (ML) models instead of using computationally intensive deep learning (DL) models.

The approach that is used by this study is to use traditional image processing and classification techniques that can be applied to captured layer images to detect and classify defects without the need for DL algorithms.

The study proved that a defect classification algorithm could be developed by making use of traditional ML models with a high degree of accuracy and the images could be processed at higher speeds than typically reported in literature when making use of DL models.

This paper addresses a need that has been identified for a high-speed defect classification algorithm that can detect and classify defects without the need for specialised hardware that is typically used when making use of DL technologies. This is because when developing closed-loop feedback systems for these additive manufacturing machines, it is important to detect and classify defects without inducing additional delays to the control system.

Effective rail surface defects detection method is the basic guarantee to manufacture high-quality rail. However, the existed visual inspection methods have disadvantages such as poor ability to locate the rail surface region and high sensitivity to uneven reflection. This study aims to propose a bionic rail surface defect detection method to obtain the high detection accuracy of rail surface defects under uneven reflection environments.

Through this bionic rail surface defect detection algorithm, the positioning and correction of the rail surface region can be computed from maximum run-length smearing (MRLS) and background difference. A saliency image can be generated to simulate the human visual system through some features including local grayscale, local contrast and edge corner effect. Finally, the meanshift algorithm and adaptive threshold are developed to cluster and segment the saliency image.

On the constructed rail defect data set, the bionic rail surface defect detection algorithm shows good recognition ability on the surface defects of the rail. Pixel- and defect-level index in the experimental results demonstrate that the detection algorithm is better than three advanced rail defect detection algorithms and five saliency models.

The bionic rail surface defect detection algorithm in the production process is proposed. Particularly, a method based on MRLS is introduced to extract the rail surface region and a multifeature saliency fusion model is presented to identify rail surface defects.

This study aims to solve the problem of weld quality inspection, for the aluminum alloy profile welding structure of high-speed train body has complex internal shape and thin plate thickness (2–4 mm), the conventional nondestructive testing method of weld quality is difficult to implement.

In order to solve this problem, the ultrasonic creeping wave detection technology was proposed. The impact of the profile structure on the creeping wave detection was studied by designing profile structural test blocks and artificial simulation defect test blocks. The detection technology was used to test the actual welded test blocks, and compared with the results of X-ray test and destructive test (tensile test) to verify the accuracy of the ultrasonic creeping wave test results.

It is indicated that that X-ray has better effect on the inspection of porosities and incomplete penetration defects. However, due to special detection method and protection, the detection speed is slow, which cannot meet the requirements of field inspection of the welding structure of aluminum alloy thin-walled profile for high-speed train body. It can be used as an auxiliary detection method for a small number of sampling inspection. The ultrasonic creeping wave can be used to detect the incomplete penetration welds with the equivalent of 0.25 mm or more, the results of creeping wave detection correspond well with the actual incomplete penetration defects.

The results show that creeping wave detection results correspond well with the actual non-penetration defects and can be used for welding quality inspection of aluminum alloy thin-wall profile composite welding joints. It is recommended to use the echo amplitude of the 10 mm × 0.2 mm × 0.5 mm notch as the criterion for weld qualification.

Heating, ventilating, air-conditioning and cooling (HVAC) systems are crucial in daily health-care facility services. Design-related defects can lead to maintenance issues, causing service disruptions and cost overruns. These defects can be avoided if a link between the early design stages and maintenance feedback is established. This study aims to use experts’ experience in HVAC maintenance in health-care facilities to list and evaluate the risk of each maintenance issue caused by a design defect, supported by the literature.

Following semistructured interviews with experts, 41 maintenance issues were identified as the most encountered issues. Subsequently, a survey was conducted in which 44 participants evaluated the probability and impact of each design-caused issue.

Chillers were identified as the HVAC components most prone to design defects and cost impact. However, air distribution ducts and air handling units are the most critical HVAC components for maintaining healthy conditions inside health-care facilities.

The unavailability of comprehensive data on the cost impacts of all design-related defects from multiple health-care facilities limits the ability of HVAC designers to furnish case studies and quantitative approaches.

This study helps HVAC designers acquire prior knowledge of decisions that may have led to unnecessary and avoidable maintenance. These design-related maintenance issues may cause unfavorable health and cost consequences.

Detection of hidden defects of aluminum alloy plate with damping coating is a challenging problem. At present, only a few non-destructive testing methods exist to address this engineering problem. Without the restriction of skin effect, remote field eddy current (RFEC) overcomes the interference caused by the damping coating. The RFEC, which has potential advantages for detecting the hidden defects of aluminum plate with damping coating, can penetrate the metal plate to detect buried depth defects. This study aims to test how thick the RFEC sensor can penetrate the metal plate to detect the buried defects.

The magnetic field distribution characteristics are analyzed, the magnetic field intensity distribution is calculated, and the structure and parameters of the coil, magnetic circuit and shielding damping are determined through the two- and three-dimensional finite element simulation methods. Optimal excitation frequency is obtained, and the distance between the excitation coil and detection coil is determined by analyzing the relationship between excitation frequency and remote field points.

Simulation and experimental results verify the feasibility of applying the RFEC detection technology in detecting the hidden defects of aluminum alloy plate with damping coating.

In this paper, the RFEC testing model of hidden defects in aluminum plate sample with damping coating is established by using the finite element method.

This research aims to improve the performance of rail fastener defect inspection method for multi railways, to effectively ensure the safety of railway operation.

Firstly, a fastener region location method based on online learning strategy was proposed, which can locate fastener regions according to the prior knowledge of track image and template matching method. Online learning strategy is used to update the template library dynamically, so that the method not only can locate fastener regions in the track images of multi railways, but also can automatically collect and annotate fastener samples. Secondly, a fastener defect recognition method based on deep convolutional neural network was proposed. The structure of recognition network was designed according to the smaller size and the relatively single content of the fastener region. The data augmentation method based on the sample random sorting strategy is adopted to reduce the impact of the imbalance of sample size on recognition performance.

Test verification of the proposed method is conducted based on the rail fastener datasets of multi railways. Specifically, fastener location module has achieved an average detection rate of 99.36%, and fastener defect recognition module has achieved an average precision of 96.82%.

The proposed method can accurately locate fastener regions and identify fastener defect in the track images of different railways, which has high reliability and strong adaptability to multi railways.

The current paper aims to present a formal model illustrating how payoff imbalances among the members of a team of decision makers (DMs) who must undertake a project condition the final outcome obtained. This result builds on the fact that payoffs imbalances would lead to different performance levels among the employees and managers who compose a team. The analysis is applied to a strategic environment, where a project requiring coordination among the DMs within the team must be developed.

The intuition behind the strategic framework on which the results are based is twofold. The authors build on the literature on social comparisons and assume that employees and managers acquire information on the payoffs received by other members of the team while being affected by the resulting comparisons, and they follow the economic literature on firm boundaries determined via incomplete contracts. In this case, employees and managers may underperform if they feel aggrieved by the outcome of the contract giving place to deadweight losses when developing the project.

The authors illustrate how a team-based performance reward structure may lead to a coordinated equilibrium even when team managers and employees receive different payoffs and exhibit shading incentives based on the payoff differentials between them. The authors will also illustrate how identical shading intensities by both groups of DMs imply that shading by the managers imposes a lower cost on the profit structure of the firm because it leads to a lower decrease in the cooperation incentives of the other members of the team. Finally, the authors show how differences in shading intensity between both types of DMs trigger a strategic defect mechanism within the team that determines the outcome of the project.

The novel environment of team cooperation and defection through shading introduced in this paper is designed to deal with the strategic decisions taken by DMs when undertaking a project within a group. In particular, the intensity of shading applied by the DMs will be endogenously determined by the relative payoffs received, which allows to account for different scenarios, where relative payoff differentials among DMs determine the outcome of the project.

Experimental research was conducted on the effects of surface roughness on ultrasonic non-destructive testing of electron beam melted (EBM) additively manufactured Ti-6Al-4V. Additive manufacturing (AM) is a developing technology with many potential benefits, but certain challenges posed by its use require further research before AM parts are viable for widespread use in the aviation industry. Possible applications of this new technology include aircraft battle damage repair (ABDR), small batch manufacturing to fill supply gaps and replacement for obsolete parts. This paper aims to assess the effectiveness of ultrasonic inspection in detecting manufactured flaws in EBM-manufactured Ti-6Al-4V. Additively manufactured EBM products have a high surface roughness in “as-manufactured” condition which is an artifact of the manufacturing process. The surface roughness is known to affect the results of ultrasonic inspections. Experimental data from this research demonstrate the ability of ultrasonic inspections to identify imbedded flaws as small as 0.51 mm at frequencies of 2.25, 5 and 10 MHz through a machined surface. Detection of flaws in higher surface roughness samples was increased at a frequency of 10 MHz opposed to both lower frequencies tested.

The approach is to incorporate ultrasonic waves to identify flaws in an additive manufactured specimen

A wave frequency of 10 MHz gave good results in finding flaws even with surface roughness present.

To the best of the authors’ knowledge, this was the first attempt that was able to identify small flaws using ultrasonic sound waves in which surface roughness was present.

Considerably diminished quality of life (QoL) is observed in patients with visual field defects after lesions affecting the visual pathway. But little is known to what extent vision-and health-related QoL impairments are associated with psychological distress. In 24 patients with chronic visual field defects (mean age=56.17±12.36) the National Eye Institute-visual functioning questionnaire (NEI-VFQ) for vision-related QoL, the Short Form Health Survey-36 (SF-36) for generic QoL and the revised Symptom-Checklist (SCL-90-R) were administered. Cases with clinically relevant SCL-90-R symptoms were defined. Demographic, QoL and visual field parameters were correlated with SCL-90-R scales. About 40% of the investigated patients met the criteria for the definition of psychiatric caseness. 8/12 NEI-VFQ scales correlated significantly with SCL-90-R phobic anxiety (r-range -0.41 to -0.64, P<0.05), 5/12 NEI-VFQ scales correlated with SCL-90-R interpersonal sensitivity (-0.43 to -0.50), and 3/12 with SCL-90-R depression (-0.51 to -0.57) and obsessive-compulsiveness (-0.41 to -0.43). In contrast, only 1/8 SF-36 scales correlated significantly with SCL-90-R depression, phobic anxiety and interpersonal sensitivity (-0.41 to -0.54). No substantial correlations were observed between visual field parameters and SCL-90-R scales. Significant correlations of SCL-90-R with NEI-VFQ but not with SF-36 suggest that self-rated psychological distress is the result of diminished vision-related QoL as a consequence of visual field loss. The extent of visual field loss itself did not influence the rating of psychological distress directly, since SCL-90-R symptoms were only reported when diminished vision-related QoL was present. Patients with reduced vision-related QoL due to persisting visual field defects should therefore be offered additional neuropsychological rehabilitation and supportive psychotherapeutic interventions even years after the lesion.

The purpose of this paper is to design a unified operational design domain (ODD) monitoring framework for mitigating Safety of the Intended Functionality (SOTIF) risks triggered by vehicles exceeding ODD boundaries in complex traffic scenarios.

A unified model of ODD monitoring is constructed, which consists of three modules: weather condition monitoring for unusual weather conditions, such as rain, snow and fog; vehicle behavior monitoring for abnormal vehicle behavior, such as traffic rule violations; and road condition monitoring for abnormal road conditions, such as road defects, unexpected obstacles and slippery roads. Additionally, the applications of the proposed unified ODD monitoring framework are demonstrated. The practicability and effectiveness of the proposed unified ODD monitoring framework for mitigating SOTIF risk are verified in the applications.

First, the application of weather condition monitoring demonstrates that the autonomous vehicle can make a safe decision based on the performance degradation of Lidar on rainy days using the proposed monitoring framework. Second, the application of vehicle behavior monitoring demonstrates that the autonomous vehicle can properly adhere to traffic rules using the proposed monitoring framework. Third, the application of road condition monitoring demonstrates that the proposed unified ODD monitoring framework enables the ego vehicle to successfully monitor and avoid road defects.

The value of this paper is that the proposed unified ODD monitoring framework establishes a new foundation for monitoring and mitigating SOTIF risks in complex traffic environments.