Conventional Inspection Research Articles

High‐speed and contactless inspection of defective micro‐LEDs through the photovoltaic effect

AbstractThe most significant challenge associated with micro‐light‐emitting‐diode (micro‐LED) displays, which are anticipated to be the next generation of display technology, is the high manufacturing cost. In order to reduce manufacturing costs, it is essential to improve yield. Improving the manufacturing yield of them necessitates the evaluation of micro‐LED chips prior to their installation onto substrates. However, the microsize and large quantity of these chips renders inspection difficult with conventional inspection methods. Herein, we propose a method for inspecting micro‐LED chips by measuring the voltage generated between the anode and cathode due to the photovoltaic effect using a developed proximity capacitance image sensor. As this inspection method does not require the use of probe pins to contact LED electrodes, it enables simultaneous inspection of multiple chips in a short time without causing any damage to the electrodes. In this paper, an experimental system equipped with this sensor was developed to demonstrate the basic measurement principle. Moreover, we demonstrated that more than 50,000 micro‐LED chips with a size of 60 μm × 34 μm can be simultaneously inspected in approximately 2 s.

Precision detection and identification method for apparent damage in timber components of historic buildings based on portable LiDAR equipment

For the purpose of preservation and restoration of historic timber buildings, it is critical that damage to timber components must be detected and identified accurately since proper detection and identification lay the foundation for scientific utilization and preservation of cultural heritage. Portable smart devices with integrated LiDAR sensor technology have significantly advanced, enabling them to be major pedestals for damage detection. An iPhone Pro based technique is therefore suggested for the precise detection and diagnosis of apparent damage to the timber components. The study consists of three major procedures. First, an apparent data collection of the timber components was conducted. A high-resolution triangular mesh model was utilized for precision detection and identification. Point cloud and texture data were the main constituents of this data collection. Second, a technique for image processing and apparent damage analysis was proposed for crack detection in historic timber buildings. This approach can be used to identify, recognize,and assess damage to timber components such as cracks, rotting, and knots. Finally, actual measurements of the timber components in Datong, Shanxi Province, at the Great Temple Corner, proved the effectiveness of the iPhone 12 Pro as a substitute approach to the conventional inspection equipment. The findings indicate that the iPhone 12 Pro, which has a built-in LiDAR sensor, performs exceptionally well in terms of effectiveness, accuracy, and graphical interpretation when identifying apparent damage to historic timber components. Moreover, it has the potential to substitute traditional measurement, 3D scanning, and other detection and identification techniques. Even though damage detection and identification of historical timber buildings has reached a mature stage in terms of portable, refined, and digital transformation, such technique can serve as a compatible addition as a safe, affordable, and paradigm shifting technical solution for detection and identification of historic building components.

GRNN-based cascade ensemble model for non-destructive damage state identification: small data approach

AbstractAssessing the structural integrity of ageing structures that are affected by climate-induced stressors, challenges traditional engineering methods. The reason is that structural degradation often initiates and advances without any notable warning until visible severe damage or catastrophic failures occur. An example of this, is the conventional inspection methods for prestressed concrete bridges which fail to interpret large permanent deflections because the causes—typically tendon loss—are barely visible or measurable. In many occasions, traditional inspections fail to discern these latent defects and damage, leading to the need for expensive continuous structural health monitoring towards informed assessments to enable appropriate structural interventions. This is a capability gap that has led to fatalities and extensive losses because the operators have very little time to react. This study addresses this gap by proposing a novel machine learning approach to inform a rapid non-destructive assessment of bridge damage states based on measurable structural deflections. First, a comprehensive training dataset is assembled by simulating various plausible bridge damage scenarios associated with different degrees and patterns of tendon losses, the integrity of which is vital for the health of bridge decks. Second, a novel General Regression Neural Network (GRNN)-based cascade ensemble model, tailored for predicting three interdependent output attributes using limited datasets, is developed. The proposed cascade model is optimised by utilising the differential evolution method. Modelling and validation were conducted for a real long-span bridge. The results confirm the efficacy of the proposed model in accurately identifying bridge damage states when compared to existing methods. The model developed demonstrates exceptional prediction accuracy and reliability, underscoring its practical value in non-destructive bridge damage assessment, which can facilitate effective restoration planning.

A Three-Step Computer Vision-Based Framework for Concrete Crack Detection and Dimensions Identification

Crack detection is significant to building repair and maintenance; however, conventional inspection is a labor-intensive and time-consuming process for field engineers. This paper proposes a three-step computer vision-based framework to quickly recognize concrete cracks and automatically identify their length, maximum width, and area in damage images. In step one, a region-based convolutional neural network (YOLOv8) is applied to train the crack localizing model. In step two, Gaussian filtering, Canny, and FindContours are integrated to extract the reference contour (a pre-designed seal) to obtain the conversion scale between pixels and millimeter-wise sizes. In step three, the recognized crack bounding box is cropped, and the ApproxPolyDP function and Hough transform are performed to quantify crack dimensions based on the conversion ratio. The developed framework was validated on a dataset of 4630 crack images, and the model training took 150 epochs. Results show that the average crack detection accuracy reaches 95.7%, and the precision of quantified dimensions is over 90%, while the error increases as the crack size grows smaller (increasing to 8% when the crack width is within 1 mm). The proposed method can help engineers to efficiently achieve crack information at building inspection sites, while the reference frame must be pre-marked near the crack, which may limit the scope of application scenarios. In addition, the robustness and accuracy of the developed image processing techniques-based crack quantification algorithm need to be further improved to meet the requirements in real cases when the crack is located within a complex background.

Three-Dimensional Reconstruction and Visualization of Underwater Bridge Piers Using Sonar Imaging.

The quality of underwater bridge piers significantly impacts bridge safety and long-term usability. To address limitations in conventional inspection methods, this paper presents a sonar-based technique for the three-dimensional (3D) reconstruction and visualization of underwater bridge piers. Advanced MS1000 scanning sonar is employed to detect and image bridge piers. Automated image preprocessing, including filtering, denoising, binarization, filling, and morphological operations, introduces an enhanced wavelet denoising method to accurately extract the foundation contour coordinates of bridge piers from sonar images. Using these coordinates, along with undamaged pier dimensions and sonar distances, a model-driven approach for a 3D pier reconstruction algorithm is developed. This algorithm leverages multiple sonar data points to reconstruct damaged piers through multiplication. The Visualization Toolkit (VTK) and surface contour methodology are utilized for 3D visualization, enabling interactive manipulation for enhanced observation and analysis. Experimental results indicate a relative error of 13.56% for the hole volume and 10.65% for the spalling volume, demonstrating accurate replication of bridge pier defect volumes by the reconstructed models. Experimental validation confirms the method's accuracy and effectiveness in reconstructing underwater bridge piers in three dimensions, providing robust support for safety assessments and contributing significantly to bridge stability and long-term safety assurance.

An automatic robot for ultrasonic partial discharge detection of gas-insulated switchgear

PurposeGas-insulated switchgear (GIS) stands as a pivotal component in power systems, susceptible to partial discharge occurrences. Nevertheless, manual inspection proves labor-intensive, exhibits a low defect detection rate. Conventional inspection robots face limitations, unable to perform live line measurements or adapt effectively to diverse environmental conditions. This paper aims to introduce a novel solution: the GIS ultrasonic partial discharge detection robot (GBOT), designed to assume the role of substation personnel in inspection tasks.Design/methodology/approachGBOT is a mobile manipulator system divided into three subsystems: autonomous location and navigation, vision-guided and force-controlled manipulator and data detection and analysis. These subsystems collaborate, incorporating simultaneous localization and mapping, path planning, target recognition and signal processing, admittance control. This paper also introduces a path planning method designed to adapt to the substation environment. In addition, a flexible end effector is designed for full contact between the probe and the device.FindingsThe robot fulfills the requirements for substation GIS inspections. It can conduct efficient and low-cost path planning with narrow passages in the constructed substation map, realizes a sufficiently stable detection contact and perform high defect detection rate.Practical implicationsThe robot mitigates the labor intensity of grid maintenance personnel, enhances inspection efficiency and safety and advances the intelligence and digitization of power equipment maintenance and monitoring. This research also provides valuable insights for the broader application of mobile manipulators in diverse fields.Originality/valueThe robot is a mobile manipulator system used in GIS detection, offering a viable alternative to grid personnel for equipment inspections. Comparing with the previous robotic systems, this system can work in live electrical detection, demonstrating robust environmental adaptability and superior efficiency.

Automated fabric inspection system development aided with convolutional autoencoder-based defect detection

Industrial automatic fabric inspection system, a critical technology in the industry, enhances both total production quantity and quality compared to conventional inspection techniques. This study aims to create a reliable and effective real-time automated visual inspection system for fabrics, focusing on defect detection. The goals of the study can be stated as; installing a system with advanced technology for capturing and processing images swiftly, the development and deployment of a system capable of autonomously learning and scanning fabrics in use, and the creation of a smart framework for accurate fabric defect detection and classification. We focus on the development of unsupervised fabric defect detection using a convolutional autoencoder model, and defect classification using a convolutional neural network model, which takes input as the feature vector generated by the convolutional autoencoder. The experimental outcomes have displayed significant success rates in both detecting defects and classifying them, confirming the effectiveness of the framework in real-time visual inspection systems.

Using Deep Learning for PCB Fault Detection

Electronic components are mainly connected to one another using printed circuit boards, or PCBs. This phase holds significant importance in the production of electronic goods. The finished product may become unusable due to a minor PCB flaw. As a result, throughout the PCB manufacturing process, rigorous and thorough flaw identification procedures are essential. To guarantee the dependability and performance of electronic products, quality control and assurance procedures are essential in the electronics manufacturing process, especially when producing printed circuit boards (PCBs). Conventional inspection techniques are not always effective in locating different kinds of PCB flaws. Defect identification is essential to guaranteeing the dependability and functionality of electronic products since printed circuit boards, or PCBs, are essential parts of electronic gadgets. Conventional approaches to PCB defect detection, like manual inspection or traditional image processing methods, are frequently laborious, imprecise, and prone to human error. This research suggests utilizing the YOLOv5 model, a cutting-edge deep learning-based object detection technique, to create an automated PCB flaw detection system in order to overcome these difficulties

Evaluation of Eddy Current Array Performance in Detecting Aircraft Component Defects

ABSTRACT Eddy current array (ECA) technology is increasingly being used in the aerospace industry for non-destructive testing of aircraft components. This study evaluates the performance of ECA in detecting defects in aircraft components, focusing on its effectiveness, reliability, and sensitivity. The study evaluates the effectiveness of ECA technology in eddy current defectoscopy by introducing a dimensionless efficiency coefficient, then seeks to validate this coefficient through experimental testing of aircraft component materials with artificially induced defects of various sizes, types, and orientations to simulate real-world scenarios. ECA’s sensitivity in detecting small and subsurface defects is analyzed, along with precise defect sizing and positional information. Reliability and repeatability are investigated through repeated measurements. Furthermore, the article analyses the impact of various factors on the performance of ECA, including surface conditions, probe configurations, and inspection parameters. Comparative analysis is performed to assess the advantages and limitations of ECA in comparison to other conventional inspection methods. The findings of this study will contribute to a better understanding of the capabilities and limitations of ECA in detecting aircraft component defects. The results will aid in optimizing inspection strategies, enhancing the reliability of defect detection, and improving the overall maintenance practices in the aerospace industry.

Evaluation of mitral chordae tendineae length using four-dimensional computed tomography.

Mitral valvuloplasty using artificial chordae tendineae represents an effective surgical approach for treating mitral regurgitation. Achieving precise measurements of artificial chordae tendineae length (CL) is an important factor in the procedure; however, no objective index currently exists to facilitate this measurement. Therefore, preoperative assessment of CL is critical for surgical planning and support. Four-dimensional x-ray micro-computed tomography (4D-CT) may be useful for accurate CL measurement considering that it allows for dynamic three-dimensional (3D) evaluation compared to that with transthoracic echocardiography, a conventional inspection method. To investigate the behavior and length of mitral chordae tendineae during systole using 4D-CT. Eleven adults aged > 70 years without mitral valve disease were evaluated. A 64-slice CT scanner was used to capture 20 phases in the cardiac cycle in electrocardiographic synchronization. The length of the primary chordae tendineae was measured from early systole to early diastole using the 3D image. The primary chordae tendineae originating from the anterior papillary muscle and attached to the A1-2 region and those from the posterior papillary muscle and attached to the A2-3 region were designated as cA and cP, respectively. The behavior and maximum lengths [cA (ma), cP (max)] were compared, and the correlation with body surface area (BSA) was evaluated. In all cases, the mitral anterior leaflet chordae tendineae could be measured. In most cases, the cA and cP chordae tendineae could be measured visually. The mean cA (max) and cP (max) were 20.2 mm ± 1.95 mm and 23.5 mm ± 4.06 mm, respectively. cP (max) was significantly longer. The correlation coefficients (r) with BSA were 0.60 and 0.78 for cA (max) and cP (max), respectively. Both cA and cP exhibited constant variation in CL during systole, with a maximum 1.16-fold increase in cA and a 1.23-fold increase in cP from early to mid-systole. For cP, CL reached a plateau at 15% and remained elongated until end-systole, whereas for cA, after peaking at 15%, CL shortened slightly and then moved toward its peak again as end-systole approached. The study suggests that 4D-CT is a valuable tool for accurate measurement of both the length and behavior of chordae tendineae within the anterior leaflet of the mitral valve.

Classifying breast cancer and fibroadenoma tissue biopsies from paraffined stain-free slides by fractal biomarkers in Fourier Ptychographic Microscopy

Breast cancer is one of the most spread and monitored pathologies in high-income countries. After breast biopsy, histological tissue is stored in paraffin, sectioned and mounted. Conventional inspection of tissue slides under benchtop light microscopes involves paraffin removal and staining, typically with H&E. Then, expert pathologists are called to judge the stained slides. However, paraffin removal and staining are operator-dependent, time and resources consuming processes that can generate ambiguities due to non-uniform staining. Here we propose a novel method that can work directly on paraffined stain-free slides. We use Fourier Ptychography as a quantitative phase-contrast microscopy method, which allows accessing a very wide field of view (i.e., mm2) in one single image while guaranteeing high lateral resolution (i.e., 0.5 µm). This imaging method is multi-scale, since it enables looking at the big picture, i.e. the complex tissue structure and connections, with the possibility to zoom-in up to the single-cell level. To handle this informative image content, we introduce elements of fractal geometry as multi-scale analysis method. We show the effectiveness of fractal features in describing and classifying fibroadenoma and breast cancer tissue slides from ten patients with very high accuracy. We reach 94.0 ± 4.2% test accuracy in classifying single images. Above all, we show that combining the decisions of the single images, each patient’s slide can be classified with no error. Besides, fractal geometry returns a guide map to help pathologist to judge the different tissue portions based on the likelihood these can be associated to a breast cancer or fibroadenoma biomarker. The proposed automatic method could significantly simplify the steps of tissue analysis and make it independent from the sample preparation, the skills of the lab operator and the pathologist.

Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study

Effective quality control is crucial in industrial manufacturing for influencing efficiency, product dependability, and customer contentment. In the constantly changing landscape of industrial production, conventional inspection methods may fall short, prompting the need for inventive approaches to enhance precision and productivity. In this study, we investigate the application of smart glasses for real-time quality inspection during assembly processes. Our key innovation involves combining smart glasses’ video feed with a server-based image recognition system, utilizing the advanced YOLOv8 model for accurate object detection. This integration seamlessly merges mixed reality (MR) with cutting-edge computer vision algorithms, offering immediate visual feedback and significantly enhancing defect detection in terms of both speed and accuracy. Carried out in a controlled environment, our research provides a thorough evaluation of the system’s functionality and identifies potential improvements. The findings highlight that MR significantly elevates the efficiency and reliability of traditional inspection methods. The synergy of MR and computer vision opens doors for future advancements in industrial quality control, paving the way for more streamlined and dependable manufacturing ecosystems.

A Novel, Non-Contact NDT Scanner Case Study: Thickness Measurement, Debonding and Defects Detection in Metallic and Composite Parts

The NDT methods currently used in aviation MRO are predominantly labour-intensive and time-consuming processes performed by human operators throughout the lifespan of an aircraft. These techniques are time-consuming, require perpetual training and are highly dependent on the operator’s skills. Thus, there is a growing need for more efficient, automated, and accurate NDT tools that will be able to provide faster and less labour-intensive assessments. This study presents a novel, non-contact, automated NDT scanning system under development, which aims to reduce the inspection time significantly. The proposed technique uses a non-contact, Lamb wave-based approach. A further essential step during the process is to use an automated positioning system. Thickness mapping and defect detection in metal and composite structures have been performed. A local thickness map in the order of 1 mm has been obtained through a fast-scanning process with comparable resolution to conventional inspection techniques. Overall, it is currently concluded that the proposed NDT scanner is a promising tool that potentially can reduce the inspection time while also having the potential to automate the damage assessment resulting in more efficient MRO inspection processes.

Diagnostics of Concrete Box Beam Bridges Using Wireless Sensors and Finite Element Analysis

Abstract Three pretensioned adjacent concrete box beam bridges were studied with a structural health monitoring (SHM) paradigm based on strain measurements and finite element static analyses. An accurate model for one bridge and an approximate model for the other two were created using ansys software. The analyses were used to calculate the strains generated by six concentrated loads that mimic the presence of a truck. Pristine and damage scenarios were implemented, and the associated numerical strains were compared to the experimental strains measured with proprietary wireless sensors during a truck test. As the results from the approximate models deviated significantly from the field response of the bridge, the accurate model applied to one bridge was extended to the other two. The comparison between numerical and experimental results revealed the presence of noncritical anomalies related to strain distribution across adjacent beams. Such issues were confirmed with the examination of the historical strains streamed for several months to a repository, using simple data processing strategies. The intellectual contribution of the work resides in the combination of finite element analysis and SHM applied to three existing bridges with very similar structural characteristics. This combination revealed the presence of noncritical issues impossible to be diagnosed with conventional inspection.

Enhancing milled rice qualitative classification with machine learning techniques using morphological features of binary images

ABSTRACT Rice is a globally important agricultural crop, with extensive cultivation and consumption in Asia. In Thailand, it is a primary food crop and a crucial export commodity. However, ensuring the quality standards of Thai rice is challenging due to variations in grain mixtures, making conventional inspection methods laborious and time-consuming. Human judgment in visual inspection introduces the risk of discrepancies. To address this, a swift and accurate solution is needed for quality analysis and differentiation of rice grain categories. Image processing techniques and machine learning offer a promising approach for accurate rice grain classification and reducing human grading errors. In a recent study focused on jasmine rice (KDML 105) samples, images of rice grains were captured using a developed device. Morphological features related to shape and size were extracted through image processing. The Boruta algorithm was employed to select significant features, which were then used to train various machine learning classifiers. After training and validation, the random forest classifier demonstrated the highest performance and was chosen as the main classification model. It was then tested with a new dataset to evaluate its identification accuracy. The selected model successfully classified four categories of rice grains with an accuracy exceeding 99.00%. While research efforts have primarily focused on classifying rice varieties and detecting grain abnormalities, incorporating a combination of morphology, color, and texture features is essential for highly accurate predictions. However, when it comes to predicting rice grain types with distinct shapes and sizes, considering relevant morphological characteristics during the model development process is sufficient to achieve highly precise and accurate results.

Automated IBEX crawler for PAUT inspection for in-service ferromagnetic assets

In order to guarantee the safety of the gas production plants, Air Liquide conducts NDT inspections regularly closely following the assets’ maintenance plans. One high criticality asset is the Pressure Swing Adsorber (PSA), found in the last stages of the hydrogen production process, where product purification takes place. These adsorbers are pressure vessels and need to be inspected for hydrogen enhanced fatigue cracks. Conventional inspection would require stopping site operation, building scaffolding for the totality of the height, which depending on design can be up to 15 meters tall and 6 meters in diameter, grinding of the inspection surface to remove the paint, conducting the inspection manually by 2 operators, and repainting once inspections are concluded. Commonly, inspection plans for these high-criticality assets outline a high inspection frequency, resulting in elevated costs associated with logistics, personnel and time and monetary resources required to plan and execute each inspection. This necessity is what drove Air Liquide R&D and INTACT to create a partnership and develop an automated crawler that can be deployed on in-service ferromagnetic assets for Phased Array Ultrasonic Testing (PAUT) inspection, using Total Focusing Method (TFM) with INTACT proprietary imaging and software. The robot, named IBEX Crawler, is deployed at the lowest part of the vessel and controlled safely at the ground level by a single operator. It is able to change directions to accommodate the inspection of longitudinal and circular welds. It includes an encoding system that enables operators to exactly locate any indications present. The crawler’s payload includes an electronic 128 elements PAUT board controlled by fiber optics, laser tracking and machine vision camera. The inspection results are analyzed by 3D reconstruction and managed by mechanical integrity assessments proprietary technology, calculating the stability of the found indications and using fracture mechanics and Fitness-For-Service (FFS) to estimate the residual lifetime of the indications and in consequence, the interval between inspections to develop a more accurate maintenance plan. Today INTACT’s IBEX crawlers travel worldwide between Air Liquide sites to ensure the integrity of the assets.

Robot-based spot weld inspection - almost couplant-free, imaging phased array based inspection with PHAsis, integrated and automated by ABB Robotics

Constant innovations and rethinking of existing systems and processes are essential for the fulfilment of business goals of automotive groups - also for the field of quality assurance. In the context of quality assurance of spot welded joints, the basic tasks of inspection processes are to support the definition of welding parameters, the monitoring of welding results, the optimization of the inspection process and, as a consequence, the reduction of costs and production time. Rigid and manual processes, as they are still used in many areas of quality assurance, are based on laboratory conditions. They are built on general assumptions and are a generic procedure without the possibility of learning processes. If these processes developed in the laboratory encounter production realities, they reach their limits and waste valuable efficiency potential. For example, conventional inspection processes are performed randomly and compare the actual with the target state and, in the event of a deviation, only provide the information of an existing process error. However, this pure information is no longer sustainable for intelligent production and requires a function extension in the sense of Industry 4.0. The goal in the sense of Industry 4.0 in this context is to recognize the root causes of these detected deviations, to provide information in real time and the subsequent intelligent reaction of the system. Artificial intelligence provides a learning system which, based on continuous analysis of production parameters, can not only detect predictive deviations of flexible environmental conditions, but also in a further step autonomously takes action. An example is the detection of weld spatter or sharp-edged areas of spot welds and the subsequent initiated grinding before inspection. This presentation focuses on the development of a robot-controlled module for the almost coupling agent-free inspection of spot welds with innovatively applied phased array technology in production (inline and offline) according to the demands of Industry 4.0.

Vision-based Propeller Damage Inspection Using Machine Learning

Unmanned Aerial Vehicles (UAVs) play an increasingly pivotal role in day-to-day rescue operations, offering crucial aerial support in challenging terrain and emergencies, such as drowning. Drone hangars are strategically deployed to ensure swift response in remote locations, overcoming range-limiting constraints posed by battery capacity. However, the UAV's airworthiness, typically ensured through conventional inspections by a technical individual, is paramount to guarantee mission safety. Over time, UAVs are prone to degradation through contact with the external environment, with propellers often being the cause of flight instability and potential crashes. This paper presents an innovative approach to automate UAV propeller inspection to avert incidents preemptively. Leveraging visual recordings and deep learning methodologies, we train a Convolutional Neural Network (CNN) model using both passive and active learning strategies. Our approach successfully detects physical damage on propellers with an impressive accuracy of 85.8%, promising a significant improvement in maintaining UAV flight safety and effectiveness in rescue operations.

DEVELOPMENT OF A QUALITY MANAGEMENT SYSTEM FOR PRECAST CONCRETE FACTORIES

The precast concrete (PC) method involves manufacturing reinforced concrete building components in a factory that are then transported to and assembled on a construction site. Compared to conventional methods, PC is widely employed as an advantageous means of creating a sustainable environment and improving construction quality. However, due to time and cost increase, many modern PC factories inspect only randomly selected component samples, for which they write inspection reports using paper-based forms. The storage and management of these documents associated with inspections within factories are essential because any defects that occur during the manufacturing process adversely affect the subsequent delivery and assembly activities. In this study, a mobile application capable of automated documentation and the storage, and input of systematic data was developed to generate a system for comprehensive quality management and assurance within PC factories. The developed system was tested in a PC factory, achieving a 47% time-saving rate compared to the conventional inspection method. Inspection reports of the developed system contain considerably more information than those of the conventional method and fundamentally prevent the risk of document damage and loss as they are automatically archived on a server in digital format.

Estimation of the size and location of an artificial weld seam defect based on the actual ultrasonic welding phenomenon for online inspection

A series of research has been conducted to analyze the quality of weld seams by non-destructive testing methods. Research on defect analysis using the actual ultrasonic welding phenomenon has not been addressed yet. To ensure a comprehensive quality evaluation, this research estimates the size and location of artificial weld seam defects by the actual ultrasonic welding phenomenon without conventional inspection systems. The welding parameters (power, pressure force, and speed) were carefully selected to ensure optimal bond strength. A superimposed type of seam was produced longitudinally by PVC-coated hybrid textiles in rough to smooth surface contact for two welding groups, using a welding width of 10 mm with an activated cooling air effect. Artificial defects were introduced across the weld seam at five different locations spaced 50 mm apart using Teflon films of 3 and 5 mm width. The actual weld phenomenon of ultrasonic welding process parameters was determined after the recorded machine parameters were converted. The effect of welding process parameters on the seam quality was also analyzed, comparing the weld seam quality between different welding groups and Teflon widths. Based on the discovered graph of the actual welding phenomenon, the location and extent of artificial weld seam defects were estimated. The artificial weld seam defect with a thickness of 0.059 mm at a width of 3.85 mm was estimated at an interval of 50.77 mm using 275 N welding pressure force and 120 W welding power at 2 m min−1 welding speed for 3 mm Teflon width. The results showed that the estimated values closely align with the actual size and position of defects. Overall, this research contributes to the development of a non-destructive testing approach for detecting weld seam defects in ultrasonic welding, emphasizing the importance of these techniques for online inspection and control of weld seam quality.