Real-time Quality Control Research Articles

Patient-based pre-classified real-time quality control (PCRTQC)

BackgroundPatient-based real-time quality control (PBRTQC) has gained increasing attention in clinical laboratory management. Although its valuable characteristics complement traditional quality control measures, its performance and practical application have faced scrutiny. In this study, patient-based pre-classified real-time quality control (PCRTQC), an extended approach was devised to enhance real-time quality control protocols. MethodsPCRTQC distinguishes itself by incorporating an additional patient pre-classification step utilising the OPTICS algorithm, thus addressing interference from diverse patient types. The complete set of patient test results obtained from a clinical chemistry analyser at The First Hospital of China Medical University in 2021 was utilised. Constant error (CE) and proportional error (PE) were introduced as analytical errors. Four analytes were selected to evaluate the PCRTQC, measuring probability for false rejection (Pfr) and the average number of patient samples until error detection (ANPed). Relevant error detection charts were generated. ResultsThe PCRTQC outperformed regression-adjusted real-time quality control (RARTQC) based on the ANPed by approximately 50% for both the CE and PE, compared to the RARTQC, particularly for the total allowable error threshold. ConclusionThe pre-classification step effectively reduced inter-individual variation and improved data preprocessing, filtering, and modelling. The PCRTQC is a robust framework for real-time quality control research.

A state-of-the-art digital factory integrating digital twin for laser additive and subtractive manufacturing processes

PurposeThis study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.Design/methodology/approachThis study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.FindingsA comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.Originality/valueBased on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.

Toward a concept of digital twin for monitoring assembly and disassembly processes

Quality is one of the key factors in the customer’s selection process between competing products. Producing high-quality, defect-free products that meet consumer expectations is crucial for manufacturing companies to gain a competitive advantage. Accordingly, developing appropriate defect generation models is essential in modern manufacturing companies to predict defects and plan efficient quality control and production. On the other hand, with its ability to support new business models and decision support systems, digital twin technology is one of the new technologies emerging to support digital transformation. Faster optimization algorithms, more powerful computers, and a massive increase in available data are just some of the features of digital twins that can be used to advance simulation toward real-time quality control and optimization of products and production systems. This paper aims to model the generation of defects of product variants in assembly and disassembly processes and evaluate their integration within a digital twin system to prevent the occurrence of defects and ensure product quality. The proposed strategy is expected to improve the optimization, monitoring, and diagnostic capabilities of complex product variants’ assembly and disassembly systems, realizing an upgrade from a single physical implementation to a combination of physical and digital.

Knowledge, attitude, and practice of patient-based real-time quality control in Australasia

Knowledge, attitude, and practice of patient-based real-time quality control in Australasia

Improving Single-Molecule Antibody Detection Selectivity through Optimization of Peptide Epitope Presentation in OmpG Nanopore.

Outer membrane protein G (OmpG) is a monomeric porin found in Escherichia coli, which possesses seven flexible loops. OmpG has been engineered as a nanopore sensor, where its loops can host affinity epitopes for selective detection of biological molecules. In this study, we investigated various loop positions to incorporate a FLAG peptide antigen epitope in the most flexible loop 6 and tested the efficacy and sensitivity of these nanopore constructs in antibody detection. We observed an OmpG construct containing inserted FLAG sequence, which exhibited strong interaction with anti-FLAG antibodies in flow cytometry; however, it could not translate molecule interactions into a readable signal in current recordings. Further optimization of the peptide presentation strategy by replacing specific sections of loop 6 sequences with the FLAG tag created a construct capable of generating unique and distinct signals when interacting with various monoclonal or polyclonal anti-FLAG clones IgG antibodies in the mixture. The peptide display scheme demonstrated in this study can be generalized for the engineering of OmpG sensors, which can be used for screening and validating positive clones during antibody development, as well as for real-time quality control of cell cultures in monoclonal antibody production.

In Situ Analysis of Curling Defects in Powder Bed Fusion of Polyamide by Simultaneous Application of Laser Profilometry and Thermal Imaging

Additive manufacturing (AM) is one of the key technologies in the global manufacturing market within various application sectors. The unique capabilities of AM enable high structural and part complexity, low material waste, and benefits in productivity by reducing design cycles and time to market. Efficient real-time quality control is still an important challenge in AM. In this paper, a real-time and in situ approach for monitoring the process in powder bed fusion of polyamide (PBF-LB/P/PA12) is proposed using the simultaneous application of two individual sensors, enabling the overlay and direct comparison of independent output data. An industrial grade laser profilometer and a thermal infrared (IR) camera were successfully integrated into a commercial system for PBF-LB/P. Artificially created curling defects were induced in a reproducible way by the manipulation of process parameters. The radiometric data was evaluated and processed into 3D topology and profile measurements to highlight peaks and curling progression. The results measured using different powder bed conditions were contrasted with corresponding thermographic data to prove the thermal visibility of curling and the influence of inhomogeneous temperature distribution on geometrical powder surface defects. The experimental setup enables the measuring of the entire powder bed surface inside the machine, with no limitations to sub-areas. Results indicate the measurable presence of curling and related temperature influences. When curling reached maximum values, inverted warpage into the negative z-direction was detected at part center as a further effect. These results can be used for improving real-time quality control in AM.

Impact of ASOS Real-Time Quality Control on Convective Gust Extremes in the USA

Most damage to buildings across the contiguous United States, in terms of number and total cost, is caused by gusts in convective events associated with thunderstorms. Their assessment relies on the integrity of meteorological observations. This study examines the impact on risk due to valid gust observations culled erroneously by the real-time quality control algorithm of the US Automated Surface Observation System (ASOS) after 2013. ASOS data before 2014 are used to simulate the effect of this algorithm at 450 well-exposed stations distributed across the contiguous USA. The peak gust is culled in around 10% of these events causing significant underestimates of extreme gusts. The full ASOS record, 2000–2021, is used to estimate and map the 50-year mean recurrence interval (MRI) gust speeds, the conventional metric for structural design. It is concluded that recovery of erroneously culled observations is not possible, so the only practical option to eliminate underestimation is to ensure that the 50-year MRI gust speed at any given station is not less than the mean for nearby surrounding stations. This also affects stations where values are legitimately lower than their neighbors, which represents the price that must be paid to eliminate unacceptable risk.

Towards in-line real-time characterization of roll-to-roll produced ZTO/Ag/ITO thin films by hyperspectral imaging

Large area manufacturing processes of thin films such as large-area vacuum roll-to-roll coating of dielectric and gas permeation barrier layers in industry require a precise control of e.g. film thickness, homogeneity, chemical compositions, crystallinity and surface roughness. In order to determine these properties in real time, hyperspectral imaging is a novel, cost-efficient, and fast tool as in-line technology for large-area quality control. We demonstrate the application of hyperspectral imaging to characterize the thickness of thin films of the multilayer system ZTO/Ag/ITO produced by roll-to-roll magnetron sputtering on 220 mm wide polyethylene terephthalate substrate. X-ray reflectivity measurements are used to determine the thickness gradients of roll-to-roll produced foils with sub nanometer accuracy that serve as ground truth data to train a machine learning model for the interpretation of the hyperspectral imaging spectra. Based on the model, the sub-layer thicknesses on the complete substrate foil area were predicted which demonstrates the capabilities of this approach for large-scale in-line real-time quality control for industrial applications.

Real-time quality control of optical backscattering data from Biogeochemical-Argo floats.

Biogeochemical-Argo floats are collecting an unprecedented number of profiles of optical backscattering measurements in the global ocean. Backscattering (BBP) data are crucial to understanding ocean particle dynamics and the biological carbon pump. Yet, so far, no procedures have been agreed upon to quality control BBP data in real time. Here, we present a new suite of real-time quality-control tests and apply them to the current global BBP Argo dataset. The tests were developed by expert BBP users and Argo data managers and have been implemented on a snapshot of the entire Argo dataset. The new tests are able to automatically flag most of the "bad" BBP profiles from the raw dataset. The proposed tests have been approved by the Biogeochemical-Argo Data Management Team and will be implemented by the Argo Data Assembly Centres to deliver real-time quality-controlled profiles of optical backscattering. Provided they reach a pressure of about 1000 dbar, these tests could also be applied to BBP profiles collected by other platforms.

Conceptualizing data-driven closed loop production systems for lean manufacturing of complex biomedical devices—a cyber-physical system approach

A model is presented for shifting the manual intensive manufacturing process of complex biomedical devices towards more lean and efficient production process via application of concepts of cyber physical systems in combination with big data and analytics in a closed loop manner. The concept model is capable of handling high product volumes and variety, has ability for self-adaptation and correction in various operating conditions, and offers real-time quality control. The approach acknowledges the challenge of these industries operating in a strict regulated environment and the higher standards of built-in quality required by developing a closed loop process, proposed to be built in accordance to the requirements of regulatory bodies and current Industry 4.0 practices. The proposed model illustrates that modern manufacturing methodologies and concepts can be integrated and adopted in such highly regulated manufacturing environments and that the model can be deployed to different production scenarios.

The application of Near-Infrared Spatially Resolved Spectroscopy in scope of achieving continuous real-time quality monitoring and control of tablets with challenging dimensions

In scope of achieving real-time release of tablets, quality attributes need to be monitored and controlled through Process Analytical Technology tools such as near-infrared spectroscopy (NIRS). The authors evaluated the suitability of NIR-Spatially Resolved Spectroscopy (NIR-SRS) for continuous real-time monitoring and control of content uniformity, hardness and homogeneity of tablets with challenging dimensions. A novel user-friendly research and development inspection unit was used as standalone equipment for the analysis of small oblong tablets with deep-cut break lines. A total of 66 tablets varying in hardness and Active Pharmaceutical Ingredient (API) content were inspected, with each tablet being analysed five times and measurements repeated on three different days. Partial Least Squares (PLS) models were developed to assess content uniformity and hardness, of which the former showed higher accuracy. The authors attempted to visualize tablet homogeneity through NIR-SRS spectra by regressing all spectra obtained during a single measurement using a content uniformity PLS model. The NIR-SRS probe demonstrated its potential towards real-time release testing through its ability to quickly monitor content uniformity, hardness and visualize homogeneity, even for tablets with challenging dimensions.

Residual neural networks to quantify traces of melamine in yogurts through image deconvolution

This work combines intelligent algorithms based on transfer learning and residual neural networks (viz., ResNet34) to process and classify optical images of pure and adulterated yogurt samples. This integration aims to detect the presence of melamine in yogurt in concentrations ranging from 1 to 10 ppm. An image database of 1,888 images is used to train the ResNet34, and 212 blinded images to test and validate its performance. The optimized intelligent algorithm is able to classify the images into 21 classes considering the yogurt type and melamine content, obtaining an accuracy of over 94%. These encouraging results certify a simple yet powerful real-time quality control method for producers and distributers to ensure food safety for the final consumers, while pinpointing the source of potential fraudulent procedures.

Real-time quality control of dripping pill’s weight based on laser detection technology

The present work reports developing the first process analytical technology (PAT)-based real-time feedback control system for maintaining the Ginkgo biloba leaf dripping pills weight during manufacturing. The opening degree of the drop valve and the weight of dripping pills were chosen as the manipulated variable and as the controlled variable, respectively. A proportional-integral controller was programmed to automatically reach the desired dripping pills weight by adjusting the opening degree of the drop valve. The closed-loop feedback control system could automatically compensate for the disturbances and ensure a predefined weight of the dripping pills with excellent robustness, high accuracy, and high efficiency during manufacturing. Furthermore, the closed-loop feedback control system improved the process capability of the dripping process, and the process capability index was > 1.67. This study provides a new approach to real-time control of the weight of dripping pills and improves the process capability during Ginkgo biloba leaf dripping pills manufacturing.

A blockchain-enabled deep residual architecture for accountable, in-situ quality control in industry 4.0 with minimal latency

Real-time and vision-based quality control for industrial processes has drawn great interest from both scientists and practitioners, particularly following the transition to Zero Defect Manufacturing (ZDM) and Industry 4.0. Despite considerable progress, most ZDM approaches focus on the accuracy of the inspection process, often neglecting critical factors for application in the shop floor. On one hand, near real-time methods are needed for early defect detection and containment. On the other hand, data scarcity is an issue causing AI methods to overfit. Another concern is the accountability of AI results, since even if an AI pipeline is successfully deployed, its predictions are not verifiable in the long term. In this work, we explore a real-time solution based on lightweight Deep Residual Networks and Blockchain technology to address these issues. Concretely, we propose a two-phase training strategy to boost the performance of baseline classifiers while maintaining low inference times. The performance of the proposed methodology is presented in two different industrial use cases with strict timing requirements, one concerning battery assembly line and the other antenna manufacturing. We validate the proposed method for defect detection and compare the results with common training strategies demonstrating an improvement of 3% and 10% in F1-score and accuracy on the two cases respectively, while lowering inference time by 2.2× compared to existing light architectures. Contributing to the accountability of AI results, we present an IoT framework using Blockchain deployed in Private Ethereum.

A deep learning solution for real-time quality assessment and control in additive manufacturing using point cloud data

A deep learning solution for real-time quality assessment and control in additive manufacturing using point cloud data

Quadratic Sensitivity Models for Flexible Power Quality Improvement in AC Electrified Railways

With the development of flexible traction power systems (TPSs), more active and flexible power quality improvement is becoming feasible for ac electrified railways. However, the standard definitions of power quality indexes of electrified railways are strongly nonlinear, which poses challenges to the optimal real-time power quality control of flexible TPSs. Therefore, this letter establishes universal and concise mathematical models that directly link the active and reactive power outputs of the traction transformer to the negative sequence current (NSC) and effective power factor (EPF) of traction substations (TSSs). With that, the quadratic sensitivity models (QSMs) of the power outputs of the traction transformer to the NSC and EPF of TSSs are developed. These QSMs can accurately estimate the varieties of NSC and EPF with the varieties of the power outputs of the traction transformer. To demonstrate their utility, two application schemes of the proposed QSMs for optimal real-time power quality control in flexible TPSs are explored and verified via hardware-in-the-loop experiments.

Low-interference real-time at-line spectroscopic composition analysis for chemical plants

A novel system for composition analysis in chemical plants at high temperatures and pressures has been developed. The system uses heated capillaries to extract and transport liquid and gaseous samples to a Fourier-transform-infrared-spectrometer (FTIR) for analysis. The samples are extracted from pressure vessels at temperatures ranging from 20 °C to 190 °C and pressures from 5 to 22 bar and analyzed via FTIR with a dead time of 5–10 s. The system was successfully used to monitor the dehydration reaction of methanol to dimethyl ether in a reactive distillation column in a 170 h long-term experiment. The system has a measurement range of 1 × 10−4 mol mol−1 to 1 mol mol−1 and is accurate and fast enough for real-time product quality assessment and control in high purity chemical processes where inline measurements are challenging due to harsh environments.

Particle finder: a simple particle detection tool for continuous-flow systems

We describe a user-friendly, open source software for single-particle detection/counting in a continuous-flow. The tool automatically processes video images of particles, including pre-conditioning, followed by size-based discrimination for independent detection of fluorescent and non-fluorescent particles of different sizes. This is done by interactive tuning of a reduced set of parameters that can be checked with a robust, real-time quality control of the original video files. The software provides a concentration distribution of the particles in the transverse direction of the fluid flow. The software is a versatile tool for many microfluidic applications and does not require expertise in image analysis.

The effectiveness of real-time computer-aided and quality control systems in colorectal adenoma and polyp detection during colonoscopies: a meta-analysis.

This study was conducted adhering to PRISMA guidelines. Studies were searched across PubMed, CINAHL, EMBASE, Scopus, Cochrane, and Web of Science. Keywords including the following 'Artificial Intelligence, Polyp, Adenoma, Detection, Rate, Colonoscopy, Colorectal, Colon, Rectal' were used. Odds ratio (OR) applying 95% CI for PDR and ADR were computed. SMD with 95% CI for withdrawal times were computed using RevMan 5.4.1 (Cochrane). The risk of bias was assessed using the RoB 2 tool. Of 2562 studies identified, 11 trials were included comprising 6856 participants. Of these, 57.4% participants were in the AI group and 42.6% individuals were in in the standard group. ADR was higher in the AI group compared to the standard of care group (OR=1.51, P=0.003). PDR favored the intervened group compared to the standard group (OR=1.89, P<0.0001). A medium measure of effect was found for withdrawal times (SMD=0.25, P<0.0001), therefore with limited practical applications. AI-supported colonoscopies improve PDR and ADR; however, no noticeable worsening of withdrawal times is noted. Colorectal cancers are highly preventable if diagnosed early-on. With AI-assisted tools in clinical practice, there is a strong potential to reduce the incidence rates of cancers in the near future.

The impacts of assimilating CFOSAT scatterometer winds for Typhoon cases based on real-time rain quality control

Rain is the main factor influencing the Ku-band scatterometer wind quality. The operational quality control (QC) scheme of the scatterometer wind retrieval procedure cannot effectively distinguish rain from high winds, limiting the optimal assimilation of Ku-band scatterometer winds in numerical weather prediction. To compensate for the inadequate operational QC of CFOSAT scatterometer (CSCAT) data, a real-time rain quality control (RRQC) method in data assimilation was proposed. Based on real-time rain forecasts, the RRQC method aims to screen and eliminate rain-affected CSCAT data. To begin, the impact of rain on CSCAT data for the entire year 2019 was analyzed. The CSCAT wind speed standard deviation (STD) and bias are found to increase with rain rate, and when the rain rate exceeds 2.5 mm/h, the STD is greater than the CSCAT-designed accuracy (2 m/s). Following that, two Typhoon cases, namely Lekima (2019) and In-fa (2021), were studied to assess the RRQC method's impact on Typhoon forecasting. It shows that assimilating CSCAT data with the RRQC method improves wind analysis and forecast, and reduces the mean track error. Finally, Typhoon Lekima was further diagnosed. It is found that the CSCAT data eliminated by RRQC are mainly distributed around the Lekima rainband with large wind speed observation minus background (OMB) absolute values. The results indicate that RRQC can identify rain contaminations in the CSCAT data and thus improve the analysis and forecast.