Research

Jump to: Review

16 pages, 19500 KiB

Open AccessArticle

Comparison of Different NDT Techniques for Evaluation of the Quality of PCBs Produced Using Traditional vs. Additive Manufacturing Technologies

by Elena Jasiūnienė, Renaldas Raišutis, Vykintas Samaitis and Audrius Jankauskas

Sensors 2024, 24(6), 1719; https://doi.org/10.3390/s24061719 - 07 Mar 2024

Viewed by 502

Abstract

Multilayer printed circuit boards (PCBs) can be produced not only in the traditional way but also additively. Both traditional and additive manufacturing can lead to invisible defects in the internal structure of the electronic component, eventually leading to the spontaneous failure of the [...] Read more.

Multilayer printed circuit boards (PCBs) can be produced not only in the traditional way but also additively. Both traditional and additive manufacturing can lead to invisible defects in the internal structure of the electronic component, eventually leading to the spontaneous failure of the device. No matter what kind of technology is used for the production of PCBs, when they are used in important structures, quality control is important to ensure the reliability of the component. The nondestructive testing (NDT) of the structure of manufactured electronic components can help ensure the quality of devices. Investigations of possible changes in the structure of the product can help identify the causes of defects. Different types of manufacturing technologies can lead to diverse types of possible defects. Therefore, employing several nondestructive inspection techniques could be preferable for the inspection of electronic components. In this article, we present a comparison of various NDT techniques for the evaluation of the quality of PCBs produced using traditional and additive manufacturing technologies. The methodology for investigating the internal structure of PCBs is based on several of the most reliable and widely used technologies, namely, acoustic microscopy, active thermography, and radiography. All of the technologies investigated have their advantages and disadvantages, so if high-reliability products are to be produced, it would be advantageous to carry out tests using multiple technologies in order to detect the various types of defects and determine their parameters. Full article

(This article belongs to the Special Issue Non-destructive Inspection with Sensors)

► Show Figures

Figure 1

23 pages, 4710 KiB

Open AccessArticle

Evaluation of Pipe Thickness by Magnetic Hammer Test with a Tunnel Magnetoresistive Sensor

by Jun Ito, Yudai Igarashi, Ryota Odagiri, Shigetaka Suzuki, Hiroshi Wagatsuma, Kazuhiro Sugiyama and Mikihiko Oogane

Sensors 2024, 24(5), 1620; https://doi.org/10.3390/s24051620 - 01 Mar 2024

Viewed by 1050

Abstract

A new nondestructive inspection method, the magnetic hammer test (MHT), which uses a compact and highly sensitive tunnel magnetoresistance (TMR) sensor, is proposed. This method complements the magnetic flux leakage method and eliminates the issues of the hammer test. It can therefore detect [...] Read more.

A new nondestructive inspection method, the magnetic hammer test (MHT), which uses a compact and highly sensitive tunnel magnetoresistance (TMR) sensor, is proposed. This method complements the magnetic flux leakage method and eliminates the issues of the hammer test. It can therefore detect weak magnetic fields generated by the natural vibration of a pipe with a high signal-to-noise ratio. In this study, several steel pipes with different wall thicknesses were measured using a TMR sensor to demonstrate the superiority of MHT. The results of the measurement show that wall thickness can be evaluated with the accuracy of several tens of microns from the change in the natural vibration frequency of the specimen pipe. The pipes were also inspected underwater using a waterproofed TMR sensor, which demonstrated an accuracy of less than 100 μm. The validity of these results was by simulating the shielding of magnetic fields and vibration of the pipes with the finite element method (FEM) analysis. The proposed noncontact, fast, and accurate method for thickness testing of long-distance pipes will contribute to unmanned, manpower-saving nondestructive testing (NDT) in the future. Full article

(This article belongs to the Special Issue Non-destructive Inspection with Sensors)

► Show Figures

Figure 1

17 pages, 8066 KiB

Open AccessArticle

Flood Detection in Steel Tubes Using Guided Wave Energy Leakage

by Rito Mijarez

Sensors 2023, 23(3), 1334; https://doi.org/10.3390/s23031334 - 25 Jan 2023

Viewed by 1219

Abstract

A study that evaluated the use of ultrasonic-guided waves to detect water in hollow pipes is presented. In this work, a guided wave system employed a 40 kHz piezoelectric (PZT) transmitter and a PZT ultrasound transducer. The transmitter was based on a battery-operated [...] Read more.

A study that evaluated the use of ultrasonic-guided waves to detect water in hollow pipes is presented. In this work, a guided wave system employed a 40 kHz piezoelectric (PZT) transmitter and a PZT ultrasound transducer. The transmitter was based on a battery-operated microcontroller, and the receiver was composed of a digital signal processor (DSP) module connected to a PC via a USB for monitoring purposes. The transmitter and receiver were attached, non-intrusively without perfect alignment, to the external wall of a steel tube 1 m × 270 mm × 2 mm in size. Flood detection was performed based on guided wave attenuation due to energy leakage from the internal steel wall of the tube to water. Two approaches were carried out. The former was an off-line signal response based on the wavelet energy entropy analysis of a received pulse; the latter was a real-time hit-and-miss analysis centered on measuring the time–space in-between two transmitted pulses. Experiments performed in the laboratory successfully identified flooded tubes. Full article

(This article belongs to the Special Issue Non-destructive Inspection with Sensors)

► Show Figures

Figure 1

13 pages, 11033 KiB

Open AccessArticle

Automatic Thinning Detection through Image Segmentation Using Equivalent Array-Type Lamp-Based Lock-in Thermography

by Seungju Lee, Yoonjae Chung, Chunyoung Kim and Wontae Kim

Sensors 2023, 23(3), 1281; https://doi.org/10.3390/s23031281 - 22 Jan 2023

Cited by 1 | Viewed by 1302

Abstract

Among the non-destructive testing (NDT) techniques, infrared thermography (IRT) is an attractive and highly reliable technology that can measure the thermal response of a wide area in real-time. In this study, thinning defects in S275 specimens were detected using lock-in thermography (LIT). After [...] Read more.

Among the non-destructive testing (NDT) techniques, infrared thermography (IRT) is an attractive and highly reliable technology that can measure the thermal response of a wide area in real-time. In this study, thinning defects in S275 specimens were detected using lock-in thermography (LIT). After acquiring phase and amplitude images using four-point signal processing, the optimal excitation frequency was calculated. After segmentation was performed on each defect area, binarization was performed using the Otsu algorithm. For automated detection, the boundary tracking algorithm was used. The number of pixels was calculated and the detectability using RMSE was evaluated. Clarification of defective objects using image segmentation detectability evaluation technique using RMSE was presented. Full article

(This article belongs to the Special Issue Non-destructive Inspection with Sensors)

► Show Figures

Figure 1

16 pages, 4717 KiB

Open AccessArticle

Defect Detection and Imaging in Composite Structures Using Magnetostrictive Patch Transducers

by Akram Zitoun, Steven Dixon, Mihalis Kazilas and David Hutchins

Sensors 2023, 23(2), 600; https://doi.org/10.3390/s23020600 - 05 Jan 2023

Cited by 1 | Viewed by 1313

Abstract

The use of thin magnetostrictive patches to generate and detect guided waves within the composite samples is investigated for defect detection. This approach has been implemented using SH0 shear horizontal guided waves in both CFRP and GFRP plates. A magnetostrictive patch transducer was [...] Read more.

The use of thin magnetostrictive patches to generate and detect guided waves within the composite samples is investigated for defect detection. This approach has been implemented using SH0 shear horizontal guided waves in both CFRP and GFRP plates. A magnetostrictive patch transducer was able to generate SH0 waves with known directional characteristics. The synthetic aperture focusing technique (SAFT) was then used to reconstruct images of defects using multiple transmission and detection locations. The results for imaging defects in both types of material are presented. Full article

(This article belongs to the Special Issue Non-destructive Inspection with Sensors)

► Show Figures

Figure 1

15 pages, 2588 KiB

Open AccessArticle

Using Fuzzy Logic to Increase Accuracy in Mango Maturity Index Classification: Approach for Developing a Portable Near-Infrared Spectroscopy Device

by Ali Khumaidi, Yohanes Aris Purwanto, Heru Sukoco and Sony Hartono Wijaya

Sensors 2022, 22(24), 9704; https://doi.org/10.3390/s22249704 - 11 Dec 2022

Cited by 3 | Viewed by 1519

Abstract

Grading is a decisive step in the successful distribution of mangoes to customers according to their preferences for the maturity index. A non-destructive method using near-infrared spectroscopy has historically been used to predict the maturity of fruit. This research classifies the maturity indexes [...] Read more.

Grading is a decisive step in the successful distribution of mangoes to customers according to their preferences for the maturity index. A non-destructive method using near-infrared spectroscopy has historically been used to predict the maturity of fruit. This research classifies the maturity indexes in five classes using a new approach involving classification modeling and the application of fuzzy logic and indirect classification by measuring four parameters: total acidity, soluble solids content, firmness, and starch. These four quantitative parameters provide guidelines for maturity indexes and consumer preferences. The development of portable devices uses a neo spectra micro development kit with specifications for the spectrum of 1350–2500 nm. In terms of computer technology, this study uses a Raspberry Pi and Python programming. To improve the accuracy performance, preprocessing is carried out using 12 spectral transformation operators. Next, these operators are collected and combined to achieve optimal performance. The performance of the classification model with direct and indirect approaches is then compared. Ultimately, classification of the direct approach with preprocessing using linear discriminant analysis offered an accuracy of 91.43%, and classification of the indirect approach using partial least squares with fuzzy logic had an accuracy of 95.7%. Full article

(This article belongs to the Special Issue Non-destructive Inspection with Sensors)

► Show Figures

Figure 1

14 pages, 14182 KiB

Open AccessArticle

Developing Novel Gas Discharge Emitters of Acoustic Waves in Air for Nondestructive Testing of Materials

by Daria A. Derusova, Vitaly O. Nekhoroshev, Victor Y. Shpil’noi and Vladimir P. Vavilov

Sensors 2022, 22(23), 9056; https://doi.org/10.3390/s22239056 - 22 Nov 2022

Cited by 1 | Viewed by 1151

Abstract

This study was devoted to the development of novel devices and a methodology intended for generating ultrasonic waves in an air medium by using atmospheric pressure gas discharge. In the proposed electrode system, the discharge process was accompanied by the generation of acoustic [...] Read more.

This study was devoted to the development of novel devices and a methodology intended for generating ultrasonic waves in an air medium by using atmospheric pressure gas discharge. In the proposed electrode system, the discharge process was accompanied by the generation of acoustic waves on the emitter surface and, consequently, in the ambient air. The gas discharge emitter vibrations were analyzed by applying the technique of Scanning Laser Doppler Vibrometry (SLDV). It was shown that the magnitude of displacements matched the corresponding characteristics of classical piezoelectric and magnetostrictive transducers. The use of the Fast Fourier transform procedure supplied amplitude–frequency spectra of vibrations generated by the gas discharge emitter. The amplitude–frequency spectrum analysis showed that the proposed emitter was able to generate acoustic waves in the air with frequencies from 50 Hz to 100 kHz, and such a device can be used for the nondestructive testing (NDT) of materials. The results of the statistical analysis of vibration displacements in the repetitive pulsed mode were discussed. A non-stable characteristic of the vibration displacement of the emitter membrane was demonstrated. The parameters of such instability were associated with the features of gas discharge processes. In the experiments, the proposed gas discharge emitter was used in combination with SLDV for inspecting carbon-fiber-reinforced polymer composites. The experiments demonstrated the possibility of using an air-coupled gas discharge transmitter to generate acoustic waves in NDT applications. Full article

(This article belongs to the Special Issue Non-destructive Inspection with Sensors)

► Show Figures

Figure 1

18 pages, 5911 KiB

Open AccessArticle

Performance Comparison of Multiple Convolutional Neural Networks for Concrete Defects Classification

by Palisa Arafin, Anas Issa and A. H. M. Muntasir Billah

Sensors 2022, 22(22), 8714; https://doi.org/10.3390/s22228714 - 11 Nov 2022

Cited by 7 | Viewed by 1958

Abstract

Periodical vision-based inspection is a principal form of structural health monitoring (SHM) technique. Over the last decades, vision-based artificial intelligence (AI) has successfully facilitated an effortless inspection system owing to its exceptional ability of accuracy of defects’ pattern recognition. However, most deep learning [...] Read more.

Periodical vision-based inspection is a principal form of structural health monitoring (SHM) technique. Over the last decades, vision-based artificial intelligence (AI) has successfully facilitated an effortless inspection system owing to its exceptional ability of accuracy of defects’ pattern recognition. However, most deep learning (DL)-based methods detect one specific type of defect, whereas DL has a high proficiency in multiple object detection. This study developed a dataset of two types of defects, i.e., concrete crack and spalling, and applied various pre-built convolutional neural network (CNN) models, i.e., VGG-19, ResNet-50, InceptionV3, Xception, and MobileNetV2 to classify these concrete defects. The dataset developed for this study has one of the largest collections of original images of concrete crack and spalling and avoided the augmentation process to replicate a more real-world condition, which makes the dataset one of a kind. Moreover, a detailed sensitivity analysis of hyper-parameters (i.e., optimizers, learning rate) was conducted to compare the classification models’ performance and identify the optimal image classification condition for the best-performed CNN model. After analyzing all the models, InceptionV3 outperformed all the other models with an accuracy of 91%, precision of 83%, and recall of 100%. The InceptionV3 model performed best with optimizer stochastic gradient descent (SGD) and a learning rate of 0.001. Full article

(This article belongs to the Special Issue Non-destructive Inspection with Sensors)

► Show Figures

Figure 1

Review

Jump to: Research

33 pages, 744 KiB

Open AccessReview

An In-Depth Study of Vibration Sensors for Condition Monitoring

by Ietezaz Ul Hassan, Krishna Panduru and Joseph Walsh

Sensors 2024, 24(3), 740; https://doi.org/10.3390/s24030740 - 23 Jan 2024

Cited by 1 | Viewed by 2128

Abstract

Heavy machinery allows for the efficient, precise, and safe management of large-scale operations that are beyond the abilities of humans. Heavy machinery breakdowns or failures lead to unexpected downtime, increasing maintenance costs, project delays, and leading to a negative impact on personnel safety. [...] Read more.

Heavy machinery allows for the efficient, precise, and safe management of large-scale operations that are beyond the abilities of humans. Heavy machinery breakdowns or failures lead to unexpected downtime, increasing maintenance costs, project delays, and leading to a negative impact on personnel safety. Predictive maintenance is a maintenance strategy that predicts possible breakdowns of equipment using data analysis, pattern recognition, and machine learning. In this paper, vibration-based condition monitoring studies are reviewed with a focus on the devices and methods used for data collection. For measuring vibrations, different accelerometers and their technologies were investigated and evaluated within data collection contexts. The studies collected information from a wide range of sources in the heavy machinery. Throughout our review, we came across some studies using simulations or existing datasets. We concluded in this review that due to the complexity of the situation, we need to use more advanced accelerometers that can measure vibration. Full article

(This article belongs to the Special Issue Non-destructive Inspection with Sensors)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Non-destructive Inspection with Sensors

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (9 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI