In-line Inspection Research Articles

On-line inspection of lattice structures and metamaterials via in-situ imaging in Additive Manufacturing

As advanced production capabilities are moving towards novel types of geometries as well as higher customization demands, a new and more efficient approach for process and part qualification is becoming an urgent need in industry. The layerwise nature of additive manufacturing (AM) potentially allows anticipating qualification tasks in-line and in-process, aiming at reducing the time and costs devoted to post-process inspections, enabling at the same time an early detection of defects since their onset stage. Such opportunity is particularly attractive in the presence of highly complex shapes like lattice structures or metamaterials, which have been increasingly investigated for industrial adoption in various sectors, aiming to achieve enhanced mechanical properties and innovative functionalities. This paper presents a novel methodology to inspect the geometry of lattice structures while the part is being built. The method is specifically designed to tackle the natural variability affecting layerwise images gathered in laser powder bed fusion. To this aim, it combines the segmentation of in-situ powder bed images of solidified layers with a data modelling approach to synthesize the 3-D shape of each unit cell into a 1-D profile representation. Such low-dimensional representation is suitable to quickly detect undesired distortions that may have a detrimental impact on final quality and performance. By using post-process X-ray computed tomography as ground truth reference, this study shows the effectiveness of the proposed approach for in-line inspection, opening a novel and cost-efficient way to address complex shape qualification for lattice structures in AM.

Machine learning-based outlier detection for pipeline in-line inspection data

Pipeline companies are facing challenges in maintaining the integrity and reliability of their pipelines. They are working towards predictive maintenance using machine learning-based approaches to predicting anomalies. Training machine learning models requires sufficient data. Data quality is therefore becoming important because inaccurate data will lead to an inaccurate or wrong decision on pipeline condition assessment and the following management. This research paper intends to address the data quality issues of pipeline inspection data such as in-line inspection (ILI) data using machine learning models. Different machine learning models developed by random forest regression, linear regression, and nearest neighbors’ methods were tested to detect outliers in the ILI data. In this paper, the ILI data collected from an oil pipeline over a period of 22 years was applied to testing and analysis. To verify the outlier detection results of machine learning models, we used statistical analysis including Z-score method to check and find if there are any gaps in the analysis. It verifies that all these methods show almost the same or very similar results for the detection of the outliers. Hence, this study presents a robust method for the field applications in the pipeline industry.

An active learning framework assisted development of corrosion risk assessment strategies for offshore pipelines

Aggressive fluids and prolonged service life result in an increasing internal corrosion risk of offshore pipelines, especially for perforation. A framework was constructed by active learning (AL) aimed at assessing the future corrosion risk of offshore pipelines and rapidly developing in-line inspection (ILI) strategies. Compared with Support Vector Regression (SVR), K-Nearest Neighbors (KNN) and Random Forest (RF), the Gradient Boosting Regression (GBR) model exhibits greater robustness and stability under both 10-fold cross-validation (CV) and bootstrap method. The RMSE and R2 are 1.48 and 0.83, respectively. Through the double iterative operation of input parameters and models, the AL framework can significantly reduce the demand for training samples, while maintaining accurate predictions. This study contributes to promoting the application of active learning methods in pipeline integrity management and planning, and providing assistance for the construction of intelligent and digital oilfields.

Implementation strategy for launch and performance improvement of high throughput manufacturing inspection systems

Product technologies are changing rapidly in advanced automotive propulsion systems. These products are driving the need for new manufacturing processes and new inspection methods. To keep new propulsion systems affordable and ensure these new products are introduced with high quality, automotive manufacturers are seeking automated inspection solutions with low cost and near-zero error rates to inspect 100% of the items. In this paper, a progressive deployment strategy of a hybrid inspection system is presented and studied in the context of technology development and rapid deployment. It enabled us to begin with human inspection and gradually phase-in automated inspection technology, while almost never failing to identify a bad item. This strategy was applied successfully to inspect ultrasonic welds in lithium ion battery packs. At the time of this study, a 75% reduction in human inspection was achieved with prospects for further reduction. Actual results from the implementation of this strategy in production are presented. Recommendations are made regarding the most appropriate time to employ this strategy and how it could increase the use of advanced automated in-line inspection technologies.

Translation of MFL and UT data by using generative adversarial networks: A comparative study

Magnetic flux leakage (MFL) and ultrasonic testing (UT) are widely used in-line inspection technologies to detect corrosion defects along pipelines. The integration of MFL and UT data has the potential to provide complementary insights that facilitate a comprehensive assessment of pipeline integrity. However, due to the inherent dissimilarity with their underlying physical principles, these techniques yield notable disparities in signal characteristics, posing challenges in integrating these multimodal data. This study aims to establish a translation mapping between MFL and UT signals to achieve consistent physical interpretations across the two modalities. Thus, this study explored the feasibility of generative adversarial network (GAN) based models encompassing both supervised and unsupervised translation approaches contingent on the availability of aligned data. Furthermore, two translation modes, MFL-UT and UT-MFL, were analyzed, respectively, to understand the effectiveness of the translation direction. The experimental results demonstrate satisfactory performance for both aligned and unaligned data translation, with the UT-MFL translation direction yielding superior results. Overall, the translation approaches pave the way for future applications, especially in subsequent data analysis tasks such as registration, comparison, and fusion of multimodal data.

Mid-infrared optical coherence tomography and machine learning for inspection of 3D-printed ceramics at the micron scale

IntroductionIn this paper, recent developments in non-destructive testing of 3D-printed ceramics and monitoring of additive manufacturing of ceramics are presented.MethodsIn particular, we present the design and use of an inline mid-infrared optical coherence tomography (MIR-OCT) system to evaluate printed and micro-structured specimens in lithography-based ceramic manufacturing (LCM).ResultsThe proposed system helps with the detection of microdefects (e.g., voids, inclusions, deformations) that are already present in green ceramic components, thereby reducing the energy and costs incurred.DiscussionThe challenges during integration are discussed. Especially, the prospects for MIR-OCT imaging combined with machine learning are illustrated with regard to inline inspection during LCM of printed ceramics.

Advancements in Electronic Component Assembly: Real-Time AI-Driven Inspection Techniques

This study presents an advanced methodology for improving electronic assembly quality through real-time, inline inspection utilizing state-of-the-art artificial intelligence (AI) and deep learning technologies. The primary goal is to ensure compliance with stringent manufacturing standards, notably IPC-A-610 and IPC-J-STD-001. Employing the existing infrastructure of pick-and-place machines, this system captures high-resolution images of electronic components during the assembly process. These images are analyzed instantly by AI algorithms capable of detecting a variety of defects, including damage, corrosion, counterfeit, and structural irregularities in components and their leads. This proactive approach shifts from conventional reactive quality assurance methods by integrating real-time defect detection and strict adherence to industry standards into the assembly process. With an accuracy rate exceeding 99.5% and processing speeds of about 5 ms per component, this system enables manufacturers to identify and address defects promptly, thereby significantly enhancing manufacturing quality and reliability. The implementation leverages big data analytics, analyzing over a billion components to refine detection algorithms and ensure robust performance. By pre-empting and resolving defects before they escalate, the methodology minimizes production disruptions and fosters a more efficient workflow, ultimately resulting in considerable cost reductions. This paper showcases multiple case studies of component defects, highlighting the diverse types of defects identified through AI and deep learning. These examples, combined with detailed performance metrics, provide insights into optimizing electronic component assembly processes, contributing to elevated production efficiency and quality.

Testing of battery separators: transducers and methods for air-coupled ultrasonic sensing.

Abstract Battery separator me3mbranes are thin polymeric microporous films placed between battery electrodes. They are a key component that strongly affects both battery performance and security and controlling its main properties is critical for the industry. In particular, the manufacturing process demands tests that allow checking both the mechanical integrity (porosity, thickness, pore size, etc.) of the materials and the consistency of these properties during the construction of the batteries. The use of an air-coupled ultrasonic system is proposed in this work. It is expected that this will make possible the non-destructive and quick testing of these materials, in order to evaluate thickness, stiffness, pore distribution and pore size of the separator. By means of three pairs of transducers, the modulus and phase of the transmission coefficient was measured from 0.35 to 1.4 MHz in different materials. The measurements show a clear modification of the film response produced by the presence of porosity. This suggests that the transmission of ultrasonic waves in battery separators using air-coupled ultrasound may have two contributions: one corresponds to the propagation though the solid part, the other corresponds to propagation in the pores. The former allows to determine properties like film thickness and elastic constants, while the latter allows to determine porosity and pore size and pore tortuosity. This work set the basis for the development of a quality test for the in-line inspection of these materials during their fabrication. The observed ultrasonic response in microporous separators suggest the possibility to use low frequency air-coupled transducers with limited sensitivity, where the use of MEM transducers at industrial scale can be advantageous.

Novel Water Probe for High-Frequency Focused Transducer Applied to Scanning Acoustic Microscopy System: Simulation and Experimental Investigation.

A scanning acoustic microscopy (SAM) system is a common non-destructive instrument which is used to evaluate the material quality in scientific and industrial applications. Technically, the tested sample is immersed in water during the scanning process. Therefore, a robot arm is incorporated into the SAM system to transfer the sample for in-line inspection, which makes the system complex and increases time consumption. The main aim of this study is to develop a novel water probe for the SAM system, that is, a waterstream. During the scanning process, water was supplied using a waterstream instead of immersing the sample in the water, which leads to a simple design of an automotive SAM system and a reduction in time consumption. In addition, using a waterstream in the SAM system can avoid contamination of the sample due to immersion in water for long-time scanning. Waterstream was designed based on the measured focal length calculation of the transducer and simulated to investigate the internal flow characteristics. To validate the simulation results, the waterstream was prototyped and applied to the TSAM-400 and W-FSAM traditional and fast SAM systems to successfully image some samples such as carbon fiber-reinforced polymers, a printed circuit board, and a 6-inch wafer. These results demonstrate the design method of the water probe applied to the SAM system.

Non-contact viscosity detection for cement slurry using laser scanning of wave motion under rod impact

Concrete workability was typically assessed using slump tests and viscometers, which involved contact-based measurements and required sampling, making them unsuitable for online measurements. This study proposed a non-contact inspecting approach by utilizing a laser profiler to scan the mixture surface height changes at high speed and high resolution when the mixture was impacted by a free-fall rod. The energy transfer efficiency was defined to analyze the energy transferred from the rod kinetic energy to the mixture waving energy, and the mechanism between the material hydration and the surface dynamic response was revealed. The maximum wave peaks, waveform areas, and energy transfer efficiencies were extracted as critical features and established a robust correlation with the mixture viscosity with R2 of 0.945, 0.919, and 0.949 correlation with the viscosity, respectively, proving that the proposed approach can be utilized in the in-line liquid material inspection. This study demonstrated the laser scanning method as a simple and effective approach, providing the possibility of non-contact detection to characterize the rheological properties by analyzing the surface fluctuations.

Robotical Automation in CNC Machine Tools: A Review

Abstract Robotics and automation have significantly transformed Computer Numerical Control (CNC) machining operations, enhancing productivity, precision, and efficiency. Robots are employed to load and unload raw materials, workpieces, and finished parts onto CNC machines. They can efficiently handle heavy and bulky components, reducing the demand of manual labour and minimizing the risk of injuries. Robots can also be used in CNC machine tools to perform tasks such as automatic tool changing system, part inspection, and workpiece positioning. Automation technologies, including in-line inspection systems and Non-Destructive Testing (NDT) methods, can be integrated into CNC machining cells to enhance accuracy and reduce scrap and rework in machining operations. These systems collect real-time data on process parameters and machine tool performance to predict maintenance, optimize machining parameters, and improve overall efficiency. In the current study, applications of robotics and automation in the modification of CNC machine tools are reviewed and discussed. Different applications of robotics and automation in CNC machine tools, such as automated material handling, automatic tool changing, robotic work cells, adaptive machining, machine tending, quality inspection, data monitoring and analysis, and production line integration, are discussed. Thus, by analysing recent achievements in published papers, new ideas and concepts of future research works are suggested. As a result, accuracy as well as productivity in the process of part production can be enhanced by applying robotics and automation in CNC machining operations.

A Reliability Assessment Method for Natural Gas Pipelines with Corroded Defects That Considers Detection Cycles

With the development of natural gas pipelines, the proportion of aged pipelines in service has been increasing, and corrosion remains a primary cause of pipeline failure. Regular inspections and reliability assessments are crucial to ensure the safe operation of pipelines. This study investigated an efficient reliability assessment method for corroded pipelines that considers in-line inspection intervals. First, this study compared the commonly used limit state equations for corrosion defects to select one suitable for X80-grade steel pipelines. Additionally, a Tail-Fit Monte Carlo Simulation (TF-MCS) algorithm was proposed to improve the computational speed by 30 times compared to traditional Monte Carlo simulations. Then, this study explored the inspection intervals used for reliability assessments of corroded pipelines. Finally, the parameter sensitivity was analyzed considering the yield strength, maximum operating pressure, and pipe diameter. This study ensures the reliable operation of corroded gas pipelines.

A probabilistic framework for external pitting corrosion growth modelling for buried steel pipelines considering soil properties

This paper introduces a novel framework for developing reliable probabilistic predictive corrosion growth models for buried steel pipelines using pipeline inspection data. The framework adopts a power-law function of time model formulation, accounting for nonconstant damage growth rates, and considers the correlation between defect depth and length growth models. The proposed framework explicitly incorporates local influential soil properties in the model formulation; thus, it requires no segmentation and homogenous defect growth assumption and provides defect-specific growth models. The framework is applicable regardless of the availability of matched or non-matched defect data. For corrosion initiation time estimation, two different approaches are proposed: one is to use a Poisson process to account for defect occurrence, which can also predict newly generated defects since the last inspection, and the other is to use multivariate linear regression of soil and pipe properties. The statistics of unknown model parameters are assessed using a Bayesian updating framework in which the model error can be incorporated. The proposed framework is applied using two different sets of data: one set of inline inspection (ILI) data and one set of field excavation data. A case study is conducted, where time-dependent system reliability of an in-service pipeline is assessed considering small leak and burst failure modes using the developed defect growth models. The impact of the growth model accuracy on the probability of failure is investigated, and the importance analysis is performed to identify the most influential random variables to the probability of failure.

Failure assessment of corroded offshore pipelines using code-based approaches and a combination of numerical analysis and artificial neural network

The main purpose of the present paper is to investigate the failure pressure and the remaining life of the corroded offshore pipelines utilizing code-based methods such as ASME B31G, modified ASME B31G, DNV RP-F101 and FFS Level-1. Besides, the results of these approaches are compared with the responses of Finite Element Method (FEM) using Artificial Neural Network (ANN) model. To this purpose, the probability distribution of the corrosion defect length and depth as well as the corrosion rate values are obtained from an In-Line Inspection (ILI) survey of an offshore pipeline project in the Persian Gulf. In addition, a 3D verified numerical model of a corroded pipeline is constructed to conduct the numerical analyses. Using the numerical analysis data, a Multi-Layer Perceptron (MLP) model is created to estimate the failure pressure of the pipeline with arbitrary defect dimensions. Moreover, the remaining life of the corroded pipeline is quantified in order to prevent the catastrophic consequences of the pipeline failure. The results show that the MLP model is capable of estimating the failure pressure of the corroded offshore pipeline with quite acceptable accuracy compared with the mentioned code-based methods. Furthermore, the calculated failure pressure of the corroded pipeline can be sorted in ascending order as: (1) FFS-Level 1 (2) ASME modified B31G (3 & 4) ASME B31G & DNV RP-F101 (5) FEM + MLP. In fact, the failure pressure and the remaining life calculated by DNV RP-F101 are closer to the FEM + MLP results for the defects with small and medium dimensions. Diversely, the DNV RP-F101 method underestimates the failure pressure of the corroded pipeline when the defect dimensions become larger.

Uplifting the study of the inline inspection technique on the buckling pipelines in pipeline integrity management strategy

This work reports the development of inline inspection (ILI) methodology to enhance the pigging activity for the dented pipeline, facilitate the pigging process to prevent Pipeline Inspection Gauge (PIG) from getting stuck and improve the safety passage for buckled pipelines. The recent report unveils the condition of the UNPIGGABLE pipelines, which reduce the inner diameter of pipelines to 257.51 mm, equivalent to 27.58 % of the initial diameter and restricts the pigging activity. In this report, the pull-through test coupled with the collapsibility test was conducted. The success of the test above allows the ILI equipment based on the magnetic flux leakage (MFL) technique to record the internal and external wall loss inwardly and geometric defect on diameter of the pipelines. The prepared artificial dented pipeline was made before it underwent several tests. Based on the pull-through test, the maximum force of 27000 N is more significant than the pipeline operating pressure to enable the MFL tool to pass through the pipelines despite exhibiting the geometry anomaly. Compressing the opposite magnetic yoke of the collapsibility test is critical, showing that the ILI MFL tool reaches its maximum compression of 242 mm. The value is lower than the minimum internal diameter of 257 mm. The ILI results show that the highest metal loss was achieved at 73 % at 15504 m at the bottom of the inspected pipelines. At the same time, the dented area reduces to more than 6 % of the pipelines’ nominal outer diameter and imposes the pipe’s integrity status to red. The distinctive result of the research can be used to model the future unprecedented pigging process when buckles appear in pipelines

Quantitative shear stress imaging in high throughput in-line Si wafer inspection

Defect inspection methods are basic steps in electronic chip manufacturing and power device production processes in the semiconductor industry. Based on stress induced photoelastic anisotropy, in-line polarized stress imaging (PSI) and quantitative determination of the local shear stress component are possible in single crystalline silicon wafers. Hereafter, such polarization stress images are compared and validated quantitatively with results of finite element simulations based on different 3D elastic models. Uniquely, high resolution at high throughputs is achieved for 300 mm wafer size in the present quantitative stress imaging study.

Systematic Evaluation of Ultrasonic In-Line Inspection Techniques for Oil and Gas Pipeline Defects Based on Bibliometric Analysis.

The global reliance on oil and gas pipelines for energy transportation is increasing. As the pioneering review in the field of ultrasonic defect detection for oil and gas pipelines based on bibliometric methods, this study employs visual analysis to identify the most influential countries, academic institutions, and journals in this domain. Through cluster analysis, it determines the primary trends, research hotspots, and future directions in this critical field. Starting from the current global industrial ultrasonic in-line inspection (ILI) detection level, this paper provides a flowchart for selecting detection methods and a table for defect comparison, detailing the comparative performance limits of different detection devices. It offers a comprehensive perspective on the latest ultrasonic pipeline detection technology from laboratory experiments to industrial practice.

Inline Inspection of Packaged Food Using Microwave/Terahertz Sensing—An Overview with Focus on Confectionery Products

Electromagnetic systems, in particular microwave/terahertz sensing technologies, are the newest among nondestructive sensing technologies. Currently, increased attention is pointed towards their use in various applications. Among these, food inspection stands out as a primary area due to its potential risk to human safety. As a result, substantial efforts are currently focused on utilizing microwave/terahertz imaging as a tool to enhance the efficacy of food quality assessments. This paper deals with the exploitation of microwave/terahertz imaging technology for food quality control and assessment. In particular, the work aims at reviewing the latest developments regarding the detection of internal quality parameters, such as foreign bodies, i.e., plastic, glass, and wood substances/fragments, as well as checking the completeness of the packaged food under consideration. Emphasis is placed on the (inline) inspection of wrapped/packaged food, such as chocolates, cookies, pastries, cakes, and similar confectionery products, moving along production conveyor belts. Moreover, the paper gives a recent overview of system prototypes and industrial products and highlights emerging research topics and future application directions in this area.

The Effect of The Determination of Plant Quarantine Installations and Inline Inspections On The Acceleration of Agricultural Commodity Exports In The Working Area of The Surabaya Agricultural Quarantine Center

This research aims to determine the impact of establishing plant quarantine facilities and inline inspection on export acceleration at the Surabaya Plant Quarantine Center. The study employs a quantitative approach and collects data from agricultural export companies operating in regions that implement plant quarantine measures and inline inspection. The analysis method used in this research is the Kruskal-Wallis test to evaluate the relationship between the establishment of plant quarantine facilities and inline inspection and export acceleration. The results of the analysis indicate a significant influence of establishing plant quarantine facilities and inline inspection on export acceleration. Export companies registered with effective Plant Quarantine Facilities and inline inspection demonstrate an improvement in their ability to meet plant quarantine requirements and enhance efficiency in inline inspections. This positively impacts the export process, including reducing delays and increasing the confidence of international trading partners. This research provides a better understanding of the role of plant quarantine facilities and inline inspection in driving ean xport acceleration in the agricultural sector. The practical implications of this study emphasize the importance of enhancing plant quarantine infrastructure and implementing effective inline inspection systems to promote agricultural export growth. Moving forward, efforts should be made to improve coordination among relevant agencies in the implementation and supervision of plant quarantine and inline inspection regulations to achieve optimal outcomes in enhancing the export performance of the agricultural sector.

A novel stress concentration inspection method for marine oil and gas pipeline based on UNSM

Stress concentration of marine oil and gas pipelines can lead to deformation and cracks, laying out great hidden dangers for safe operation. This paper proposes a novel in-line inspection method for stress concentration based on UNSM (unsaturated magnetization) for oil and gas pipelines. Firstly, the stress concentration inspection mechanism with UNSM is analyzed, and the equivalent magnetic circuit model of the inspection probe is derived. Secondly, the response signal features of axial, radial and complex stresses were investigated parametrically by numerical simulation. Then, quantitative tensile stress inspection was carried out with the UNSM inspection probe. Finally, the pipeline stress state is continuously scanned by the self-developed defect dynamic scanning experimental system. The results show that the UNSM inspection probe can effectively detect the abnormal stress of the pipeline. Meanwhile, the axial tensile, radial compressive stress and complex stress increase the axial permeability of the pipeline, resulting in a negative correlation of characteristic signals. Besides, the signal feature shows the "basin" distribution and decreases gradually with the increase of the lift-off. The above research provides a reference for developing in-line inspection equipment for the abnormal stress of marine pipelines.