Sensors For Quality Control Research Articles

Non-contact microwave sensor for FDM 3D printing quality control

ABSTRACT In recent years, 3D printing has undergone a transformational journey – from prototyping technology, limited to laboratories, to a production method used in industry – becoming a powerful tool for creating functional objects. Despite this progress, 3D printing faces ongoing challenges related to defect detection and print quality control. We present an innovative, non-contact, fast and cheap microwave sensor for detecting defects in printed components, related to both mechanical errors and conductivity inhomogeneities. We discuss in detail the methodology of the microwave surface mapping technique and the results obtained for several samples printed with popular filaments. Tests show the ability to detect faults with millimetre precision. The simple design of the entire quality control module allows for easy integration with various types of 3D printers, from popular ones used by hobbyists to professional, highly specialised ones.

Engraved Split-Ring Resonators as Potential Microwave Sensors for Olive Oil Quality Control

Engraved Split-Ring Resonators as Potential Microwave Sensors for Olive Oil Quality Control

Non‐destructive grading technique for mangoes using a flexible impedance sensing system and YOLOv5s_CBAM

AbstractFlexible sensors for food quality control are experiencing rapid development. The purpose of this study is to address the time‐consuming issues associated with traditional fruit grading methods by utilizing a homemade flexible impedance sensing system (FISS). A customized spiral slide system was innovatively designed in the study to simulate the grading process on a fruit assembly line with multimodal features for low power consumption and non‐destructive evaluation. FISS integrated components such as a camera, spiral slider, 3D printed slider, latex ball, flexible impedance electrodes, and back‐end measurement circuits and successfully achieved accurate food quality assessment by using a visual classification model for primary grading based on YOLOv5s‐CBAM and an impedance feature classification model for secondary grading of mangoes based on the coefficient of variation method and threshold coefficient method. With an impressive assessment accuracy of 97.07% and a classification speed of up to 3 s/piece, the system successfully classified mangoes into seven grades covering overripe, fully ripe, ripe, unevenly ripe, unripe, underripe, and rotten states. By eliminating the reliance on complex instruments and expensive equipment, FISS provides a cost‐effective alternative for food quality control, significantly reducing operational costs.Practical applicationsThe purpose of this study is to address the time‐consuming issues associated with traditional fruit grading methods by utilizing a homemade flexible impedance sensing system (FISS). A customized spiral slide system was innovatively designed in the study to simulate the grading process on a fruit assembly line with multimodal features for low power consumption and non‐destructive evaluation. FISS integrated components such as a camera, spiral slider, 3D printed slider, latex ball, flexible impedance electrodes, and back‐end measurement circuits and successfully achieved accurate food quality assessment by using a visual classification model for primary grading based on YOLOv5s‐CBAM and an impedance feature classification model for secondary grading of mangoes based on the coefficient of variation method and threshold coefficient method. With an impressive assessment accuracy of 97.07% and a classification speed of up to 3 s/piece, the system successfully classified mangoes into seven grades covering overripe, fully ripe, ripe, unevenly ripe, unripe, underripe, and rotten states. By eliminating the reliance on complex instruments and expensive equipment, FISS provides a cost‐effective alternative for food quality control, significantly reducing operational costs.

Soft sensor for in-line quality control of turning processes based on non-destructive testing techniques and advanced data fusion

AbstractThis study describes the systematic process of training, testing, and validating a soft sensor designed for quality control of a turning process on components made of AISI 4140 steel. The soft sensor allows product quality to be predicted and unfavorable surface conditions to be identified, in particular the appearance of a phenomenon known as “White Layer”, often characterized in the case of AISI 4140 steel by an ultra-fine-grained microstructure (UFG). Basis of the soft sensor is a data fusion supported by non-destructive testing techniques (NDT), particularly micromagnetic methods (3MA). A critical part of this work is to address challenges such as lift-off compensation and in-process detection using 3MA. The application of machine-learning techniques, including Principal Component Analysis (PCA) and regression analysis, is detailed. These techniques result in robust models capable of detecting the occurrence of the White Layer phenomenon while minimizing the influence of measurement setup variations and process disturbances. In addition, the study demonstrates the integration of NDT into the machining process which drives the soft sensor and allows suitable adjustments of the process parameters. The data-driven soft sensor approach demonstrates a possible In-Line control system and discusses different control theories and their respective advantages and disadvantages. This system can effectively set targeted surface conditions in real time during the turning process.

High-Q litz wire NQR sensor for medical applications

RELEVANCE. The design and development of radio frequency (RF) coil sensors is an important engineering and, at the same time, fundamental task for those radio spectroscopic instruments that require an increase in sensitivity, measured as a signal-to-noise ratio (SNR). Radio spectroscopy of nuclear quadrupole resonance (NQR), especially in nitrogen compounds, in which the resonant frequency is very low and ranges from a few megahertz or lower to hundreds of kilohertz, requires the use of special solutions to increase the sensitivity. PURPOSE. Theoretical substantiation and search for a technical solution that allows achieving high sensitivity on standard equipment through the use of a high-quality sensor. METHODS. Methods for optimizing the design of sensors for NQR/NMR spectrometers are considered. The design of the sensor for the NQR spectrometer, which contains an inductance coil wound with a Litz wire, has been calculated and designed. RESULTS. A high-quality coil for the spectrometer sensor was made, which gives an increase in the quality factor by about 1.5 times. The use of a spectrometer with this coil made it possible to confidently record weak noisy signals of paracetamol at a low duty cycle. The sensitivity of the sensor made it possible to distinguish preparations from different manufacturers by their spectral characteristics. CONCLUSIONS. A solenoid sensor has been developed, modeled and manufactured, which has a high quality factor and allows recording quadrupole resonance signals of drugs (paracetamol) by a non-destructive method directly through the package. The possibility of using such a sensor for quality control of medicines, detection of falsified and counterfeit medicines is shown.

Portable Instruments Based on NIR Sensors and Multivariate Statistical Methods for a Semiautomatic Quality Control of Textiles

Near-infrared (NIR) spectroscopy is a widely used technique for determining the composition of textile fibers. This paper analyzes the possibility of using low-cost portable NIR sensors based on InGaAs PIN photodiode array detectors to acquire the NIR spectra of textile samples. The NIR spectra are then processed by applying a sequential application of multivariate statistical methods (principal component analysis, canonical variate analysis, and the k-nearest neighbor classifier) to classify the textile samples based on their composition. This paper tries to solve a real problem faced by a knitwear manufacturer, which arose because different pieces of the same garment were made with “identical” acrylic yarns from two suppliers. The sweaters had a composition of 50% acrylic, 45% wool, and 5% viscose. The problem occurred after the garments were dyed, where different shades were observed due to the different origins of the acrylic yarns. This is a challenging real-world problem for two reasons. First, there is the need to differentiate between acrylic yarns of different origins, which experts say cannot be visually distinguished before garments are dyed. Second, measurements are made in the field using portable NIR sensors rather than in a controlled laboratory using sophisticated and expensive benchtop NIR spectrometers. The experimental results obtained with the portable sensors achieved a classification accuracy of 95%, slightly lower than the 100% obtained with the high-performance laboratory benchtop NIR spectrometer. The results presented in this paper show that portable NIR sensors combined with appropriate multivariate statistical classification methods can be effectively used for on-site textile quality control.

Multiple In-Mold Sensors for Quality and Process Control in Injection Molding.

The simultaneous improvement of injection molding process efficiency and product quality, as required by Industry 4.0, is a complex, non-trivial task that requires a comprehensive approach, which involves a combination of sensoring and information techniques. In this study, we investigated the suitability of in-mold pressure sensors to control the injection molding process in multi-cavity molds. We have conducted several experiments to show how to optimize the clamping force, switchover, or holding time by measuring only pressure in a multi-cavity mold. The results show that the pressure curves and the pressure integral are suitable for determining optimal clamping force. We also proved that in-channel sensors could be effectively used for a pressure-controlled SWOP. In the volume-controlled method, only the sensors in the cavity were capable of correctly detecting the end of the filling. We proposed a method to optimize the holding phase. In this method, we first determined the integration time of the area under the pressure curve and then performed a model fit using the relationship between the pressure integral and product mass. The saturation curve fitted to the pressure data can easily determine the gate freeze-off time from pressure measurements.

ATLAS ITk strip sensor quality control procedures and testing site qualification

The high-luminosity upgrade of the Large Hadron Collider, scheduled to become operational in 2029, requires the replacement of the ATLAS Inner Detector with a new all-silicon Inner Tracker. Radiation hard n+-in-p micro-strip silicon sensors were developed by the ATLAS Inner Tracker strip collaboration and are produced by Hamamatsu Photonics K.K. Production of the total amount of 22000 strip sensors has started in 2020 and will continue until 2025. The ATLAS strip sensor collaboration has the responsibility to monitor the quality of the fabricated devices by performing detailed measurements of individual sensor characteristics and by comparing the obtained results with the tests done by the manufacturer. Dedicated Quality Control procedures were developed to check whether the delivered large-format sensors adhere to the ATLAS specifications. The institutes performing the Quality Control testing of the pre-production and production ATLAS ITk strip sensors had to initially be qualified for multiple high-throughput tests by successfully completing the Site Qualification process. The Quality Control procedures and the qualification process are described in this paper.

Luminescent Silicon Nanowires as Novel Sensor for Environmental Air Quality Control.

Air quality monitoring is an increasingly debated topic nowadays. The increasing spillage of waste products released into the environment has contributed to the increase in air pollution. Consequently, the production of increasingly performing devices in air monitoring is increasingly in demand. In this scenario, the attention dedicated to workplace safety monitoring has led to the developing and improving of new sensors. Despite technological advancements, sensors based on nanostructured materials are difficult to introduce into the manufacturing flow due to the high costs of the processes and the approaches that are incompatible with the microelectronics industry. The synthesis of a low-cost ultra-thin silicon nanowires (Si NWs)-based sensor is here reported, which allows us the detection of various dangerous gases such as acetone, ethanol, and the ammonia test as a proof of concept in a nitrogen-based mixture. A modified metal-assisted chemical etching (MACE) approach enables to obtain ultra-thin Si NWs by a cost-effective, rapid and industrially compatible process that exhibit an intense light emission at room temperature. All these gases are common substances that we find not only in research or industrial laboratories, but also in our daily life and can pose a serious danger to health, even at small concentrations of a few ppm. The exploitation of the Si NWs optical and electrical properties for the detection of low concentrations of these gases through their photoluminescence and resistance changes will be shown in a nitrogen-based gas mixture. These sensing platforms give fast and reversible responses with both optical and electrical transductions. These high performances and the scalable synthesis of Si NWs could pave the way for market-competitive sensors for ambient air quality monitoring.

Voltammetric glassy carbon sensor approach for the extended stability studies of doxorubicin in lyophilized dosage form

Doxorubicin (DOX) is an anthracycline antibiotic that is widely used in the clinical treatment of cancer patients. DOX has a high market value. Electroanalytical methods for DOX analysis are an alternative and promising approach compared to chromatographic techniques. In this context, electroanalysis provides a low-cost method for determining drugs such as DOX lyophilized powder for the injection. Differential pulse voltammetry with a glassy carbon electrode was used. DOX stability after reconstitution was performed, and the correct time for safe administration to patients in hospitals was determined. The electroanalytical method showed a limit of detection of 0.54��mol�L?1 and limit of quantification of 1.83 �mol L�1, which is enough for the application in quality control of DOX. The high-performance liquid chromatography analysis was also applied in pharmaceutical samples containing DOX to compare with the proposed method, showing that the obtained results are relatively similar for both methods. Therefore, the electroanalytical approach shows the viability of an attractive alternative technique for applying this sensor for drug quality control.

New sensor for quality control

New sensor for quality control

Automated visual inspection and defect detection of large-scale silicon strip sensors

For the Phase-II Upgrade of the ATLAS Detector, the Inner Detector will be replaced with the Inner Tracker (ITk), consisting of a pixel and a strip tracker. The 17,888 silicon strip detector modules comprising the ITk strip tracker will be assembled from silicon strip sensors and flexes with readout chips in a manual assembly process performed at 20 module assembly sites in a complex distribution chain, which requires quality control steps to be performed after each distribution and assembly step. Sensor quality control requires a visual inspection of the full sensor area (about 100 cm2) of each sensor to detect and log any defects (e.g. scratches, breakdown areas or chipped corners) or contamination. Since manual surveys of full sensor areas for several thousand sensors are both time-consuming and prone to errors, alternative methods were investigated to automate the process and improve its reliability. This paper presents a setup developed to take high-resolution images of full silicon strip sensors with high repeatability quickly and an algorithm developed for the automated detection of defects, built using functions and filters from popular open-source visual processing packages OpenCV and Scikit-image. Methods were developed both for small-scale high-resolution images and full-size sensor images with lower resolution — both are presented here.

The identification of quality control methods used in the packaging production process

Purpose: The aim of the article is to identify the quality control methods used in companies producing packaging and to obtain knowledge of the scope of the use of optical sensors. Design/methodology/approach: Enterprises located in Poland in the Greater Poland Voivodeship, producing paper, board, corrugated and plastic packaging. The triangulation strategy was used in the study in which Computer Assisted Web Interview (CATI), Individual In-depth Interviews (IDIs), and Case Study were applied. The subject of research was the identification of quality control methods at individual stages of packaging production and the responsibility for carrying it out, as well as the method of presenting the control results. In addition, the use of optical sensors for quality control was assessed. Findings: Digitalization of manufacturing leading to the Industry 4.0 concept provides novel tools for quality control and data storing. It enables real-time data collection and processing for even better even better management of the production process and quality control. However, the concept of Industry 4.0 (including Big Data Analysis, Internet of Things, Artificial Intelligence, Neural Networks) is not new, still many companies find it difficult to implement it and benefit from its capabilities. The surveyed packaging companies are characterized by low use of the possibilities related to the use of more advanced methods of product quality control at individual stages of the production process. The potential for improvement is the area of process and product control through the use of optical sensors/actuators, etc., and the form of keeping records of the product quality checks carried out. Research limitations/implications: The results of this research are not representative, but they encourage the authors to carry out a broader and more in-depth analysis of the research subject on a national scale. Practical implications: Research indicates the need to improve the quality control process in the production of packaging and a wider use of modern solutions with the use of optical sensors. Originality/value: An article can be an element influencing the management to better select quality control methods at various stages of the production process. Keywords: quality control, packaging, control methods, optical sensors, Internet of Things (IoT). Category of the paper: Research paper.

Assessment of the Quality of Chest Compressions Performed by Health-Care Workers Under Simulated Conditions

Aim of the study: to investigate chest compression parameters by city hospital staff under simulated conditions with and without the use of a sensor device for quality control of chest compressions.Materials and Methods. The study was conducted in Moscow's multidisciplinary hospitals. The study included 359 medical staff members. The participants were divided into 4 groups: physicians (n=97) and nurses (n=82) from intensive care units (ICU) and physicians (n=92) and nurses (n=88) from specialized departments. Participants performed 2 minutes of chest compressions without a chest compressions quality control (CCQC) sensor, followed by 2 minutes of chest compressions using a defibrillator sensor with audiovisual prompts from the device turned on. The percentage of target compressions, rate and depth of compressions were analyzed.Results. Compression parameters in the group of ICU doctors were outside the reference range (% target compression — 0.5 (0.0; 14.5)%, rate 124.1±17.8 per minute, depth 5.6±1.1 cm), in the group of ICU nurses, the percentage of target compressions was 0.0 (0.0; 3.5)%, rate — 123.6±23.7 per minute, depth — 5.3±1.2 cm, in the group of specialist doctors the percentage of target compressions was 0.0 (0.0; 1.2) %, rate — 123.8±23.2 per minute, depth — 5.8±1.2 cm, in specialized nurses group the percentage of target compressions was 0.0 (0.0; 6.1)%, rate — 119.7±29.5 per minute, depth — 5.6±1.2 cm. There was a significant improvement in compression performance in all groups when the sensor device was used: in ICU physicians the percentage of target compressions was 81.6 (64.80; 87.90)%, rate — 124.1±17.8 per minute, depth — 5.5±0.2 cm; in ICU nurses the percentage of target compressions was 69.1 (47.4; 80.6), rate — 123.6±23.7 per minute, depth — 5.3±0.3 cm, in specialist doctors the percentage of target compressions was 69.30 (50.50; 78.70), rate — 123.8±23.2 per minute, depth — 5.4±0.3 cm, in specialized nurses the percentage of target compressions reached 63.70 (42.90; 75.80), rate — 119.7±29.5 per minute, depth — 5.4±0.3 cm. There were no differences in analysed compression parameters between staff in different departments or positions.Conclusion. Compression parameters (percentage of target compressions, rate, depth) were not influenced by the department where the staff member worked and the position held (doctor or nurse). The use of a compression quality sensor device has improved compression parameters by reducing rate and normalizing depth. The use of the sensor does not increase the percentage of target compressions to the maximum values, indicating the need for training by an instructor.

IRQC, a surveillance pathway for 40S ribosomal quality control during mRNA translation initiation.

Post-translational modification of ribosomal proteins enables rapid and dynamic regulation of protein biogenesis. Site-specific ubiquitylation of 40S ribosomal proteins uS10 and eS10 plays a key role during ribosome-associated quality control (RQC). Distinct, and previously functionally ambiguous, ubiquitylation events on the 40S proteins uS3 and uS5 are induced by diverse proteostasis stressors that impact translation activity. Here, we identify the ubiquitin ligase RNF10 and the deubiquitylating enzyme USP10 as the key enzymes that regulate uS3 and uS5 ubiquitylation. Prolonged uS3 and uS5 ubiquitylation results in 40S, but not 60S, ribosomal protein degradation in a manner independent of canonical autophagy. We show that blocking progression of either scanning or elongating ribosomes past the start codon triggers site-specific ubiquitylation events on ribosomal proteins uS5 and uS3. This study identifies and characterizes a distinct arm in the RQC pathway, initiation RQC (iRQC), that acts on 40S ribosomes during translation initiation to modulate translation activity and capacity.

Insertable and reusable SERS sensors for rapid on-site quality control of fish and meat products

Surface-enhanced Raman spectrometry (SERS) is one of the most important on-site testing tools. However, it remains a great challenge for the SERS to achieve on-site safety evaluation for fish and meat products, due to their extremely complicated compositions of wide-ranging chemicals, the potential existence of unknown micro-organisms, and the difficulty to directly acquire spectra information from inside the product. Here, we report a convenient strategy for mass fabrication of a novel type of SERS sensors through electrochemical nanoetching of commercial Ag needles. The thus obtained SERS sensor features rich surface nanostructures of ultrahigh SERS sensitivity, making them highly compatible with low-cost portable Raman spectrometers to realize on-site detection. These sensors can be inserted into the products to read out the depth-profile information, e.g., from purine, proteins, and lipids. Unlike the conventional insertable SERS sensors that are coated with foreign plasmonic nanoparticles, the novel sensor possesses nano-carved plasmonic surface structures that are highly resistant to aggregation and materials loss, enabling much improved durability well suited for depth-profiling applications. Interestingly, these sensors can be repetitively regenerated by acid rinsing while maintain high enhancement effect. Instant detection of fish or meat spoilage at early stage is realized in a few minutes using the novel sensors reported here, demonstrating an attractive means for on-site food quality control in a rapid, low-cost, and convenient manner.

Smart fingertip sensor for food quality control: Fruit maturity assessment with a magnetic device

Automated technologies for quality inspection of fruits have attracted great interest in the food industry. The development of nondestructive mechanisms to assess the quality of individual fruit prior to sale may lead to an increase in overall product quality, value, and consequently, producer competitiveness. However, the existing methods have limitations. Herein, a texture sensor based on highly sensitive hair-like cilia receptors, to allow a quick quality evaluation of fruit is proposed. The texture sensor consists of up to 100 magnetized nanocomposite cilia attached to a chip with magnetoresistive sensors in a full Wheatstone bridge architecture. In this paper we demonstrate the use of ciliary sensors in scanning fruits (blueberries and strawberries) in different maturation stages. The contact of the cilia with the fruit skin provided qualitative information about its texture in terms of ripeness stage. Less mature fruits exhibited, on average, a highest peak voltage of 0.14 mV for blueberries and 0.12 mV for strawberries, while overripe fruits exhibited 0.58 mV and 0.56 mV, respectively. The results were confirmed by sensorial assessment of the fruit freshness, and therefore attesting the application potential of the sensing technology for fruit quality control.

Mitochondrial protein import as a quality control sensor.

Mitochondria are organelles involved in various functions related to cellular metabolism and homoeostasis. Though mitochondria contain own genome, their nuclear counterparts encode most of the different mitochondrial proteins. These are synthesised as precursors in the cytosol and have to be delivered into the mitochondria. These organelles hence have elaborate machineries for the import of precursor proteins from cytosol. The protein import machineries present in both mitochondrial membrane and aqueous compartments show great variability in pre-protein recognition, translocation and sorting across or into it. Mitochondrial protein import machineries also interact transiently with other protein complexes of the respiratory chain or those involved in the maintenance of membrane architecture. Hence mitochondrial protein translocation is an indispensable part of the regulatory network that maintains protein biogenesis, bioenergetics, membrane dynamics and quality control of the organelle. Various stress conditions and diseases that are associated with mitochondrial import defects lead to changes in cellular transcriptomic and proteomic profiles. Dysfunction in mitochondrial protein import also causes over-accumulation of precursor proteins and their aggregation in the cytosol. Multiple pathways may be activated for buffering these harmful consequences. Here, we present a comprehensive picture of import machinery and its role in cellular quality control in response to defective mitochondrial import. We also discuss the pathological consequences of dysfunctional mitochondrial protein import in neurodegeneration and cancer.

Fiber Optic Gas Sensors Based on Lossy Mode Resonances and Sensing Materials Used Therefor: A Comprehensive Review.

Pollution in cities induces harmful effects on human health, which continuously increases the global demand of gas sensors for air quality control and monitoring. In the same manner, the industrial sector requests new gas sensors for their productive processes. Moreover, the association between exhaled gases and a wide range of diseases or health conditions opens the door for new diagnostic applications. The large number of applications for gas sensors has permitted the development of multiple sensing technologies. Among them, optical fiber gas sensors enable their utilization in remote locations, confined spaces or hostile environments as well as corrosive or explosive atmospheres. Particularly, Lossy Mode Resonance (LMR)-based optical fiber sensors employ the traditional metal oxides used for gas sensing purposes for the generation of the resonances. Some research has been conducted on the development of LMR-based optical fiber gas sensors; however, they have not been fully exploited yet and offer optimal possibilities for improvement. This review gives the reader a complete overview of the works focused on the utilization of LMR-based optical fiber sensors for gas sensing applications, summarizing the materials used for the development of these sensors as well as the fabrication procedures and the performance of these devices.

Quantitative glycoproteomics reveals cellular substrate selectivity of the ER protein quality control sensors UGGT1 and UGGT2.

UDP-glucose:glycoprotein glucosyltransferase (UGGT) 1 and 2 are central hubs in the chaperone network of the endoplasmic reticulum (ER), acting as gatekeepers to the early secretory pathway, yet little is known about their cellular clients. These two quality control sensors control lectin chaperone binding and glycoprotein egress from the ER. A quantitative glycoproteomics strategy was deployed to identify cellular substrates of the UGGTs at endogenous levels in CRISPR-edited HEK293 cells. The 71 UGGT substrates identified were mainly large multidomain and heavily glycosylated proteins when compared to the general N-glycoproteome. UGGT1 was the dominant glucosyltransferase with a preference toward large plasma membrane proteins whereas UGGT2 favored the modification of smaller, soluble lysosomal proteins. This study sheds light on differential specificities and roles of UGGT1 and UGGT2 and provides insight into the cellular reliance on the carbohydrate-dependent chaperone system to facilitate proper folding and maturation of the cellular N-glycoproteome.