Automated Inspection Techniques Research Articles

Optimal Pricing and Retailing Strategy for an Assembled Product Manufacturing–Remanufacturing Process under Carbon Emission Regulations and Autonomation

Online-to-offline (O2O) retailing offers unique opportunities for customizable assembled products with spare parts. Customers can browse and configure their desired product online, selecting from various components. Imperfect production, where a certain percentage of products have defects, can be amplified in the manufacturing system. Stricter carbon emission regulations put pressure on manufacturers to minimize waste. This creates a tension between discarding imperfect products, generating emissions, and potentially offering them at a discount through the O2O channel, which could raise quality concerns for consumers. In this study, an imperfect single-stage production process is examined, incorporating manufacturing–remanufacturing within a single stage for assembled products containing various spare parts. The study explores an investment scenario aimed at enhancing the environmental sustainability of the product. Additionally, two carbon emissions regulation strategies, specifically carbon cap-and-trade regulation and carbon taxation, are evaluated for their effectiveness in mitigating carbon footprints. The identification of waste, particularly in the form of defective items, is achieved through automated inspection techniques. The demand for spare parts associated with the assembled products is intricately linked to the selling prices set across diverse channels. Finally, the total profit of the manufacturing system is maximized with the optimized value of the selling prices, order quantity, backorder quantity, and investments in autonomated inspection, setup cost, and green technology. Numerical illustrations show that system profit was optimized when the defective rate followed a triangular distribution under carbon cap-and-trade regulation and when green technology investment helped to enhance retailer profit by 18.12%, whereas autonomated inspection increased retailer profit by 10.27%.

Orbital-Rail-Type Automatic Inspection Device for Pipeline Welds Using Radiation Dose Prediction Results from FLUKA Simulation

Pipeline welds typically do not have secondary reinforcement, rendering welds highly vulnerable to leakage accidents caused by the movement of gases or liquids. Therefore, identifying internal defects in welds through radiographic testing (RT) is critical for a visual and quantitative evaluation of weld defects. In this study, we developed a device that can automatically inspect the circumferential connection between pipes by applying a digital radiography testing (DRT) technique that can convert radiation signals into real-time electrical signals by using a digital detector array (DDA). Gamma rays were used to minimize spatial constraints in the inspection environment and optimization was performed to satisfy quality requirements set by international standards. Furthermore, FLUKA simulation was performed to predict radiation intensity for accurate radiation leakage identification to enable the shielding design to be supplemented with lead rubber. This measure considerably reduces the safe distance for radiation leakage during field testing. The results confirmed the feasibility of a novel automated inspection technique that integrates automatic inspection devices and ensures safety using radiation, the byproduct of which is a hazardous material.

Leveraging Vision Transformers for Detecting Porosity in Wind Energy Composites

The structural robustness and operational efficiency of wind turbine rotor blades are crucial for the overall effectiveness of wind energy systems, often constructed with fiber-reinforced polymers (FRPs) and adhesives. However, porosity within these materials poses a significant threat, weakening structural strength and effectiveness. Air pockets lead to stress concentration points, reducing load-carrying capacity and elevating the risk of blade failure, especially under dynamic wind loads. Manual detection of these air pockets is laborious, necessitating automated inspection techniques. Advanced imaging technologies, such as computed tomography (CT) scanning and deep learning, hold promise for identifying and quantifying porosity in FRPs and adhesives, reducing labor while enhancing accuracy. The study introduces a transformer-based model for porosity detection, departing from convolution-based methods, emphasizing the incorporation of global context throughout the network. Leveraging Vision Transformer (ViT) framework advances, the model is applied to porosity segmentation in wind energy blades, showing promising results with limited datasets. The prospect of using larger datasets suggests potential for a versatile solution in segmenting porosity or voids in various wind energy blade composites, including adhesives.

Crack detection of concrete structures using deep convolutional neural networks optimized by enhanced chicken swarm algorithm

With the rapid increase of ageing infrastructures worldwide, effective and robust inspection techniques are highly demanding to evaluate structural conditions and residual lifetime. The damages on structural surfaces, for example, spalling, crack, rebar buckling and exposure, are important indicators to assess the structural condition. In fact, several state-of-the-art automated inspection techniques using these indicators have been developed to reduce human-conducted onsite inspection activities. However, the efficiency of these techniques is still required to be improved in terms of accuracy and computational cost. In this study, a vision-based crack diagnosis method is developed using deep convolutional neural network (DCNN) and enhanced chicken swarm algorithm (ECSA). A DCNN model is designed with a deep architecture, consisting of six convolutional layers, two pooling layers and three fully connected layers. To enhance the generalisation capacity of trained model, ECSA is introduced to optimize meta-parameters of the DCNN model. The model is trained and tested using image patches cropped from raw images obtained from damaged concrete samples. Finally, a comparative study on different crack detection techniques is conducted to evaluate performance of the proposed method via a group of statistical evaluation indicators.

Multi-Method END Study in the Search for 'Defects' in an Aeronautical Radar Element

Non-Destructive Testing (NDT) has gone from being a simple laboratory curiosity to an indispensable tool in the industry to determine the level of quality achieved in its products. The new concepts of Integrated Manufacturing (CIM) bring a more universal concept of quality compared to the past philosophy based on Quality Control as a group specialized in checking whether production works within certain specifications. Even so, NDTs have not lost interest, but have seen their interest increased due to automated inspection techniques. It has become a contribution to the structuring of quality as it allows to move from purely empirical criteria to other more objective and that constitute the link between design and evaluation (Ramirez et al, 1996).
 The work presented, although it does not propose new methods or techniques of NDT, has the interest of converging into a single object five conventional methods each of which provides partial information about their quality of manufacture and must synthesize the results in order to evaluate it. Furthermore, it shows a situation of the application of NDT in which these must be applied in the absence of reference standards, as they do not exist. This peculiar situation is completely different from the usual situation in the use of NDT in the industry, both in manufacturing processes and in maintenance inspections.

Detection and classification of citrus green mold caused by Penicillium digitatum using multispectral imaging.

Fungal decay is a prevalent condition that mainly occurs during transportation of products to consumers (from harvest to consumption) and adversely affects postharvest operations and sales of citrus fruit. There are a variety of methods to control pathogenic fungi, including UV-assisted removal of fruit with suspected infection before storage, which is a time-consuming task and associated with human health risks. Therefore it is essential to adopt efficient and dependable alternatives for early decay detection. In this study, detection of orange decay caused by Penicillium genus fungi was examined using spectral imaging, a novel automated inspection technique for agricultural products. The reflectance parameter (including mean reflectance) and reflectance distribution parameters (including standard deviation and skewness) of surfaces were extracted from decayed and rotten regions of infected samples and healthy regions of non-infected samples. The classification accuracy of rotten, decayed and healthy regions at 4 and 5 days after fungal inoculation was 98.6 and 100% respectively using the mean and skewness of 500 nm, 800 nm, 900 nm and modified normalized difference vegetation index (MNDVI) spectra. Comparison of results between healthy and infected samples showed that early real-time detection of Penicillium digitatum using multispectral imaging was possible within the near-infrared (NIR) range. © 2018 Society of Chemical Industry.

High-speed and crack-free direct-writing of microchannels on glass by an IR femtosecond laser

Fabrication of high-resolution 3D structures with laser radiation on the surface of brittle materials has always been a challenging task. Even with femtosecond laser machining, micro-cracks and edge chipping occur. In order to evaluate processing modes optimal both in quality and productivity, we investigated high-speed (50kHz) femtosecond laser processing of BK7 glass with the use of design of experiments and regression analysis. An automated inspection technique was developed to extract quality characteristics of test-objects. A regression model was obtained appropriate to fabricate microchannels with a predefined depth in the range of 1–30µm with average accuracy of 5%. It was found that high quality machining modes are in the range of 0.91–2.27µJ energy pulses, overlap of 53–62%, three and more number of passes. A material removal rate higher than 0.3mm3/min was reached and microfluidic structures were formed based on data obtained.

Textural fabric defect detection using statistical texture transformations and gradient search

The inspection of the fabric defects is an important problem, which highly affects both the quality and the cost in the textile industry. Because of consistency and accuracy problems, the inspection of the fabric defect by human experts is neither feasible nor efficient. This requires development and use of automated inspection techniques. Thus, in this study, a texture analysis method, which uses sum and difference histograms (SDH) conjointly with co-occurrence matrices, is proposed to introduce an objective criterion for defect detection. To accomplish the detection task with high accuracy, several features were extracted from SDH and then, a defect search technique, which was developed in the context of this study, was applied. Moreover, several experiments and parameter analysis were performed to carry out detection at feasible computation time and memory storage. The developed method was applied to 28 kinds of raw woven fabric defects and 27 of them (i.e. 93.1%) were successfully recognized by the proposed detection system. The quantitative results and qualitative discussions show the effectiveness of the developed strategy.

An automated ultrasonic inspection approach for flip chip solder joint assessment

Acoustic Micro Imaging (AMI) techniques have been widely used as a non destructive evaluation (NDE) tool in evaluating both microelectronic packages and solder joints. However, there are a lack of analysis tools and automated inspection techniques to facilitate the inspection process. This paper presents a novel approach to extract the solder joint’s features from ultrasound images for automated solder joint assessment. The proposed method consists of three stages: solder joint detection, feature extraction, and defect evaluation. Firstly, a gradient based circular Hough transform is employed to detect the solder joints in acquired ultrasound images. Subsequently, feature extraction is carried out by a radial gradient based region growing algorithm. Finally, defect evaluation is achieved by analysing the variation of the image features automatically. Cross-sectional analysis was performed to validate and evaluate the performance of the solder joint inspection system. The results show that image features obtained by the system can be used easily to distinguish between a healthy and fractured joint, thus confirming the feasibility of the automated AMI inspection technique.

Model based in-process monitoring with optical coherence tomography

The demands on in-process 3D monitoring in ceramic micromanufacturing industry require a high-precision, non-destructive, rapid and automated inspection technique for measuring the thickness of component layer, determining the shape and dimensions of the embedded 3D structures, and detecting the de-bonding, cracks, warping and deformation. One of the promising metrology techniques is optical coherence tomography (OCT). With the dedicated image processing algorithm and the industrial product data exchange standard, the model-based integration of OCT as a new metrology tool is demonstrated. As a generic standard for any product or manufacturing information, ISO 10303 STEP AP242 is employed for the measured data model. Unambiguous data representation is achieved by integrating additional modelling constraints. The proposed framework allows fully using the technical advantages of OCT to in-process 3D monitoring.

AIWIN—a fast and jigless inspection technique for machined parts

Precision inspection of manufactured components having multiple complex surfaces and variable tolerance definition is an involved, complex and time-consuming function. In routine practice, a jig is used to present the part in a known reference frame to carry out the inspection process. Jigs involve both time and cost in their development, manufacture and use. This paper describes 'as is where is inspection' (AIWIN), a new automated inspection technique that accelerates the inspection process by carrying out a fast registration procedure and establishing a quick correspondence between the part to inspect and its CAD geometry. The main challenge in doing away with a jig is that the inspection reference frame could be far removed from the CAD frame. Traditional techniques based on iterative closest point (ICP) or Newton methods require either a large number of iterations for convergence or fail in such a situation. A two-step coarse registration process is proposed to provide a good initial guess for a modified ICP algorithm developed earlier (Ravishankar et al., Int J Adv Manuf Technol 46(1-4):227-236, 2010). The first step uses a calibrated sphere for local hard registration and fixing the translation error. This transformation locates the centre for the sphere in the CAD frame. In the second step, the inverse transformation (involving pure rotation about multiple axes) required to align the inspection points measured on the manufactured part with the CAD point dataset of the model is determined and enforced. This completes the coarse registration enabling fast convergence of the modified ICP algorithm. The new technique has been implemented on complex freeform machined components and the inspection results clearly show that the process is precise and reliable with rapid convergence. © 2011 Springer-Verlag London Limited.

Process knowledge based multi-class support vector classification (PK-MSVM) approach for surface defects in hot rolling

Random surface defects occur during the hot bar rolling of steels and are identified either by manual or by automated inspection techniques. Manual inspection techniques are purely based on the process knowledge of the inspector such as the location, type and kind of defects, and the primary sources of these defects. The automated techniques, to identify and classify the defects, rely on machine vision technologies and image processing algorithms based on support vector machines, wavelets, image processing and statistical inference. Both these approaches have their own advantages and limitations. To improve the accuracy of classification of these defects a process knowledge based support vector classification scheme is proposed (called PK-MSVM) which combines feature extraction task of automated inspection with the process knowledge. The defect observation data from the imaging sensor is transformed to include this process knowledge. Three attributes of the defects – length to width ratio, longitudinal location and transverse location- are used for this transformation are they are closely related to the thermo-mechanics of the rolling process. Different formulations of the multi-class support vector machines (MSVMs) are compared for this classification with or without process knowledge based transformation: one-against-one, one-against-all and Hastie’s algorithm of multi class SVM. It is found that the new approach (PK-MSVM) performs better than traditional MSVM for all the three formulations. For the best case, the performance sees a jump of more than 100%. Thus incorporating process knowledge in identification and classification does increase the reliability of inspection considerably.

Automated Solder Inspection Technique for BGA-Mounted Substrates by Means of Oblique Computed Tomography

High-density large-scale integration (LSI) packages such as ball grid arrays (BGAs) are being utilized in car electronics and communication infrastructure products. These products require a high speed and reliable inspection technique for their solder joints. Oblique computed tomography (OCT) was proposed as a novel imaging technique for BGA-mounted substrates, and it is being introduced in many manufacturing factories. Although operators examine OCT images manually, the establishment of an automated inspection technique is required from the viewpoint of an operator's load and the fluctuation of the inspection results. In this paper, a novel automated solder inspection technique by means of OCT is proposed. This technique consists of position adjustment, bump extraction, character extraction and judgement. Moreover, by combining five characteristic features, the condition of a solder bump was determined in computer algorithm. In this paper, linear discriminate analysis and an artificial neural network technique are introduced as the determination methods. In the experiments, these techniques are evaluated by using actual BGA-mounted substrates. The correct rate of inspection reached 99.7% in both the determination methods, which clearly indicates that proposed method may be useful in practice.

Computer-aided visual inspection for integrated quality control

This paper first briefly introduces the fundamentals of automated inspection techniques, followed by a categorization of typical applications. At its focus the paper elaborates on issues relating to the need for, the approach to and the benefits of integrating automatic inspection systems within the context of computer integrated PCB manufacturing. It argues that automatic inspection systems should assume the role of not only quality inspection systems but also important quality information generators on the shop floor. In particular, integrated quality control will be realized through an integration of inspection systems with process/quality control systems such as statistical process control (SPC).