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The purpose of this paper is the proposal of fatigue-life-prediction curve for cast aluminum alloy A356-T6 with different casting defect sizes.

Four kinds of A356-T6 fatigue specimens were sampled from the actual large-scale cast component, where the cooling rates were different. In addition, three kinds of A356 were casted under different casting conditions to simulate different defect sizes in the actual component. Subsequently, rotating bending fatigue tests were conducted using those samples. The maximum sizes of casting defects were quantitatively evaluated through microstructural observation and extreme value statistics. The fatigue limits of all samples were predicted using hardness and defect sizes based on modified Murakami’s equation.

The modified equation for fatigue limit prediction in A356-T6 was proposed. Fatigue limits were successfully predicted using the proposed equation.

Fatigue limit prediction method using hardness and maximum defect size was limited to steels. This paper proposed the modified method for A356-T6 aluminum alloy with lower elastic modulus. The method was valid for A356-T6 with different defect sizes.

The purpose of this paper is to determine the ultra-high cycle fatigue behavior and ultra-slow crack propagation behavior of selective laser melting (SLM) AlSi7Mg alloy under as-built conditions.

Constant amplitude and two-step variable amplitude fatigue tests were carried out using ultrasonic fatigue equipment. The fracture surface of the failure specimen was quantitatively analyzed by scanning electron microscope (SEM).

The results show that the competition of surface and interior crack initiation modes leads to a duplex S–N curve. Both manufacturing defects (such as the lack of fusion) and inclusions can act as initially fatal fatigue microcracks, and the fatigue sensitivity level decreases with the location, size and type of the maximum defects.

The research results play a certain role in understanding the ultra-high cycle fatigue behavior of additive manufacturing aluminum alloys. It can provide reference for improving the process parameters of SLM technology.

In the field of steel defect detection, the existing detection algorithms struggle to achieve a satisfactory balance between detection accuracy, computational cost and inference speed due to the interference from complex background information, the variety of defect types and significant variations in defect morphology. To solve this problem, this paper aims to propose an efficient detector based on multi-scale information extraction (MSI-YOLO), which uses YOLOv8s as the baseline model.

First, the authors introduce an efficient multi-scale convolution with different-sized convolution kernels, which enables the feature extraction network to accommodate significant variations in defect morphology. Furthermore, the authors introduce the channel prior convolutional attention mechanism, which allows the network to focus on defect areas and ignore complex background interference. Considering the lightweight design and accuracy improvement, the authors introduce a more lightweight feature fusion network (Slim-neck) to improve the fusion effect of feature maps.

MSI-YOLO achieves 79.9% mean average precision on the public data set Northeastern University (NEU)-DET, with a model size of only 19.0 MB and an frames per second of 62.5. Compared with other state-of-the-art detectors, MSI-YOLO greatly improves the recognition accuracy and has significant advantages in computational cost and inference speed. Additionally, the strong generalization ability of MSI-YOLO is verified on the collected industrial site steel data set.

This paper proposes an efficient steel defect detector with high accuracy, low computational cost, excellent detection speed and strong generalization ability, which is more valuable for practical applications in resource-limited industrial production.

The purpose of this paper is to classify body shapes using angular defects instead of sizes.

A large amount of dimensional data from a national anthropometry survey was analysed, and a basic pattern and its polyhedron were also used to create a three‐dimensional body shape from three body sizes. Using this method, the sizes were converted into nine angular defects.

The authors could define the factors explaining body shape characteristics and classify the body shapes into four groups. The four groups could be characterised by two pattern making difficulties of the upper and lower parts of the body as well as by two proportions, of waist girth to bust girth and bust girth to back length. Furthermore, depending on the age, the authors could understand body shape by the angle made.

Using a polyhedron model, the angles could be calculated using an enormous existing data set of sizes. An angular defect serves as an index to indicate the degree of difficulty for developing a flat pattern. If an angular defect of the bust is large, it is difficult to make a paper pattern of a bust dart. On the other hand, if an angular defect of the waist is large, it is easy to make a paper pattern of a waist dart. Thus, each body shape could be simultaneously characterized by two difficulty indices and two proportions of sizes.

In ultrasonic A‐scan technique the depth and the size of the defect in the material can be determined from the position and amplitude of the reflected echo on the cathode ray tube screen. However, the main difficulty in ultrasonic testing is the precise recognition of the defect type. The purpose of this paper is to develop analysis software to interpret defects of single‐v butt‐welded mild steel plates in ultrasonic testing.

This paper establishes a relationship between types of defects in single‐v butt‐welded mild steel plates and the corresponding amplitudes and widths of echo signals, defect positions and beam directions.

Using this relationship it develops analysis software named “ULTRASL‐1” to predict the type of unknown defects by minimizing the effect of the size and geometry of defect on echo amplitude which is the main limitation in using echo amplitude for identification of defect type.

This paper limits for defects like slag, isolated pore, porosity, lack of inter‐run fusion, lack of side‐wall fusion, crack and lack of penetration in single‐v butt‐welded mild steel plates.

The significance of this work is the introduction of a specialized procedure and a software programme to identify type of defect, so that non‐destructive testing personnel with any level of experience can share the expertise of the best operators in the industry. Hence, it will support to reduce one of the main problems concerning ultrasonic testing, i.e. the difficulties in recognition of defect type.

This paper aims to propose a new method to solve the two problems in fabric defect detection. Current state-of-the-art industrial products defect detectors are deep learning-based, which incurs some additional problems: (1) The model is difficult to train due to too few fabric datasets for the difficulty of collecting pictures; (2) The detection accuracy of existing methods is insufficient to implement in the industrial field. This study intends to propose a new method which can be applied to fabric defect detection in the industrial field.

To cope with exist fabric defect detection problems, the article proposes a novel fabric defect detection method based on multi-source feature fusion. In the training process, both layer features and source model information are fused to enhance robustness and accuracy. Additionally, a novel training model called multi-source feature fusion (MSFF) is proposed to tackle the limited samples and demand to obtain fleet and precise quantification automatically.

The paper provides a novel fabric defect detection method, experimental results demonstrate that the proposed method achieves an AP of 93.9 and 98.8% when applied to the TILDA(a public dataset) and ZYFD datasets (a real-shot dataset), respectively, and outperforms 5.9% than fine-tuned SSD (single shot multi-box detector).

Our proposed algorithm can provide a promising tool for fabric defect detection.

The paper includes implications for the development of a powerful brand image, the development of “brand ambassadors” and for managing the balance between stability and change.

This work provides technical support for real-time detection on industrial sites, advances the process of intelligent manual detection of fabric defects and provides a technical reference for object detection on other industrial

Therefore, our proposed algorithm can provide a promising tool for fabric defect detection.

Defects can be caused by a number of factors, such as maintenance damage, ground handling and foreign objects thrown up from runways during an in-service use of composite aerospace structures. Sandwich structures are capable of absorbing large amounts of energy under impact loads, resulting in high structural crashworthiness. This situation is one of the many reasons why sandwich structures are extensively used in many aerospace applications nowadays. Their non-destructive inspection is often more complex. Hence, the choice of a suitable non-destructive testing (NDT) method can play a key role in successful damage detection. The paper aims to discuss these issues.

A comparison of detection capabilities of selected C-scan NDT methods applicable for inspections of sandwich structures was performed using water-squirt, air-coupled and pitch-catch (PC) ultrasonic techniques, supplemented by laser shearography (LS).

Test results showed that the water-squirt and PC techniques are the most suitable methods for core damage evaluation. Meanwhile, the air-coupled method showed lower sensitivity for the detection of several artificial defects and impact damage in honeycomb sandwiches when unfocussed transducers were used. LS can detect most of the defects in the panels, but it has lower sensitivity and resolution for honeycomb core-type sandwiches.

This study quantitatively compared the damage size indication capabilities of sandwich structures by using various NDT techniques. Results of the realised tests can be used for successful selection of a suitable NDT method. Combinations of the presented methods revealed most defects.

This paper aims to use convolutional neural networks (CNNs) to provide an objective approach to classify deteriorated building assets according to the type and extent of damage. This research supports automated inspection of buildings and focuses on roofing elements as one of the most critical and externally distressed elements in buildings.

In this paper, 5,000+ images of deteriorated roofs from several buildings were collected to design a CNN system that automatically identifies and sizes roofing defects. Experimenting with different CNN formulations, the best accuracy is achieved using two-stage CNNs. The first-stage CNN classifies images into defect/no defect, while the second stage classifies the defected images according to the damage type. Based on the image classification, optimization is used to prioritize roof repairs by maximizing the return from limited rehabilitation funds.

The developed CNNs reached 95% and 97% accuracy for the first and second phases, respectively, which is higher than achieved in previous literature efforts. Using the proposed model to automate inspection and condition assessment activities proved to be faster than conventional methods. Repair/replace strategy for a case study of 21 campus buildings based on their condition and budgetary constraints was suggested.

Future research includes testing different data acquisition technologies (e.g. infrared imaging), performing severity-based classification and integrating with BIM for defect localization.

This study provides an objective approach to automate asset condition assessment and improve funding decisions using a combination of image analysis and optimization techniques. The proposed approach is applicable toward other asset types and components.

The purpose of this paper is to use the corresponding magnetic sensor and detection method to detect and image the defects of small diameter pipelines. Urban gas pipeline is an energy transportation tool for urban industrial production and social life, which is closely related to urban safety. Preventing the occurrence of urban gas pipeline transportation accidents and carrying out pipeline defect detection are of great significance for the urban economic and social stability. To perform pipeline defect detection, the magnetic flux leakage internal detection method is generally used in the detection of large-diameter long-distance oil and gas pipelines. However, in terms of the internal detection of small-diameter pipelines, due to the heavy weight, large structure of the detection device and small pipe diameter, the detection is more difficult.

In order to solve the above matters, self-made three-dimensional magnetic sensor and three-dimensional magnetic flux leakage imaging direct method are proposed for studying the defect identification. Firstly, for adapting to the diameter range of small-diameter pipelines, and containing the complete information of the defect, a self-made three-dimensional magnetic sensor is made in this paper to improve the accuracy of magnetic flux leakage detection. And on the basis of it, a small diameter pipeline defect detection system is built. Secondly, as detection signal may be affected by background magnetic field interference and the jitter interference, the complete ensemble empirical mode decomposition with adaptive noise method is utilized to screen the detected signal. As a result, the useful signal is reconstructed and the interference signal is removed. Finally, the defect contour inversion imaging of detection is realized based on the direct method of three-dimensional magnetic flux leakage imaging, which includes three-dimensional magnetic flux leakage detection data and data segmentation recognition.

The three-dimensional magnetic flux leakage imaging experimental results shown that, compared to the actual defects, the typical defects, irregular defects and crack groove defects can be analyzed by the magnetic flux leakage defect contour imaging method in qualitative and quantitative way respectively, which provides a new idea for the research of defect recognition.

A three-dimensional magnetic sensor is made to adapt the diameter range of small diameter pipeline, and based on it, a small-diameter pipeline defect detection system is built to collect and display the magnetic flux leakage signal.

Defects in fabric have been and continue to be a major source of seconds in finished garments. These defects persist despite several visual inspections and intensive efforts to remove defective parts during sewing operations. The increased use of automation in assembly steps will intensify the problem of detection and removal of fabric defects in cut‐parts. Describes a workstation utilizing machine vision which has been designed and constructed to detect and remove defective cut‐parts prior to the initiation of assembly operations. The workstation employs two vision systems — an area camera and a line camera — to inspect parts on a conveyor belt both statically and dynamically. The colour of the parts is also determined and the area and perimeter are measured to detect improperly cut parts. The acceptable parts are then stacked in a manner suitable for input to an automated sewing station. The workstation should permit placing into the assembly operations a set of defect‐free, properly‐cut and colour‐matched parts. It is estimated that this cut‐part inspection system will reduce defects in finished garments by approximately 50 per cent and should greatly simplify the labour‐intensive and costly fabric defect control systems currently in place in most apparel plants.