Quality Assurance In Industry Research Articles

Chemical analysis and antibacterial susceptibility of some brands of amoxicillin suspension marketed in Sagamu, Ogun, Nigeria

AbstractPoor quality medicines present a serious public health concern and have a significant impact on national clinical and economic burden. This study was carried out for comparative quality assessment of different brands of amoxicillin suspension in Sagamu community in Nigeria to determine their suitability for the therapeutic purpose. Ten brands of 125 mg/5 mL amoxicillin suspension, marketed in Sagamu community pharmacies were procured and subjected to chemical analysis using the high‐performance liquid chromatography method according to the official procedures in United State Pharmacopoeia (USP) and the microbiological analysis on different clinical isolates which include Pseudomonas aeruginosa, Escherichia coli, Streptococcus pyogene, and Staphylococcus aureus using agar disc diffusion antimicrobial susceptibility testing. The results of the high‐performance liquid chromatography revealed the percentage content of the brands A, B, C, D, E, F, G, H, I, and J to be 97.39%, 57.078%, 91.565%, 98.137%, 61.694%, 103.337%, 91.454%, 106.683%, 75.109%, and 96.895% w/v, respectively. The result of the high‐performance liquid chromatography reveals the brands (A, C, D, F, G, H, and J) had a percentage within the required range and were deemed unsuitable for therapeutic use. In the microbiological analysis, all the brands passed the antibacterial susceptibility testing except for brand (B, E, and I) which were not susceptible to S. aureus due to their zone of inhibition being less than 15 mm. Overall, this study emphasizes the importance of quality assurance in the pharmaceutical industry to ensure that only high‐quality drugs are made available to the population for optimal therapeutic benefit.

Electro-Optics Properties of Intact Cocoa Beans based on Near Infrared Technology

This study encapsulates the efficient prediction of moisture content in cocoa beans through Near Infrared Spectroscopy (NIRS) and Partial Least Squares (PLS) regression, showcasing a strong model fit with a high R square value of 0.92 and low Root Mean Square Error (RMSE) of 0.36% in calibration; these values underscore the model's accurate estimation of moisture levels. In the realm of electro-optics properties, this success highlights NIRS's capability in assessing key attributes like moisture content in cocoa beans based on their unique spectral signatures, emphasizing the technology's role in quality control for chocolate production. Furthermore, the precise predictions align with the broader objective of leveraging NIRS to evaluate and optimize the electro- optics properties of cocoa beans, fostering informed decision-making for enhanced processing and quality assurance in the cocoa industry.

Enhancing low‐temperature long‐time milk pasteurization process with a C4.5 algorithm‐driven AIoT system for real‐time decision‐making

AbstractThis research is driven by the imperative to improve the milk pasteurization process's efficiency and safety within the Industry 5.0 framework. It introduces the artificial intelligence of things (AIoT) into the pasteurization system and focuses on deploying the C4.5 algorithm, anticipated to render precise real‐time decisions by analyzing critical temperature and duration data essential for the low‐temperature long‐time (LTLT) pasteurization technique. Test results indicate that the C4.5 model is highly effective, demonstrating nearly perfect metrics with a precision of .78, a recall of .77, and an F1‐score of .73, while achieving an overall algorithm accuracy of .76. In terms of heating control, the double jacket method tested on a 300‐L system proved the control design's capability to quickly reach and maintain desired temperatures, hitting the set‐point within 70 min. However, it was observed that systems with larger capacities took longer to reach the set‐point, suggesting a potential for further optimization. The findings affirm that integrating AIoT into control systems can significantly enhance process efficiency and effectiveness, consistent with the goal of incorporating sophisticated technological solutions in the food sector.Practical ApplicationsThe article “Enhancing Low‐Temperature Long‐Time Milk Pasteurization Process with a C4.5 Algorithm‐Driven AIoT System for Real‐Time Decision‐Making” explores an AIoT system's potential to revolutionize the dairy industry's pasteurization methods. This system promises enhanced efficiency by optimizing energy and time, thereby reducing costs. It ensures superior quality control through continuous monitoring, maintaining nutritional integrity and safety. The high automation level minimizes human error and operational demands, while predictive maintenance forecasts prevent downtime. The system's scalability supports production expansion with minimal resource increase. Moreover, it guarantees product traceability and compliance with food safety regulations. The generated data offers insights for further supply chain optimizations. This integration of AI and IoT thus presents significant advancements in process management, quality assurance, and scalable operations for the dairy industry.

Review—Nanosystems-Enhanced Electrochemical Biosensors for Precision in One Health Management

One Health is a comprehensive and cohesive strategy that seeks to effectively manage and enhance the well-being of people, animals, and ecosystems in a sustainable manner. Electrochemical biosensors are indispensable vectors for One Health management, with diverse applications across industrial process control, environmental monitoring, healthcare, microbiology, and quality assurance. The integration of nanotechnology has propelled innovative sensing algorithms, enhancing the efficiency and precision of biosensors. Nanoscale material integration amplifies their ability to detect molecules and single biomolecules with nano-biosensors utilizing nanoparticles, nanowires, carbon nanotubes (CNTs), nanorods, and quantum dots. Nanomaterials, distinguished by surface functionalities, tunability of physical/chemical properties, high detection sensitivity, large surface area, stability, and thermal/electrical conductivity, are instrumental in architecting nano-biosensors. The main limitations in current times are scalability, cross-sensitivity, complex immobilization pathways, and the toxicity factor of nanosensors, which are hazardous for the ecosystem as well as humans. This review explores the fundamentals of nanotechnology-enabled electrochemical biosensors, encompassing their composition, manufacturing materials, and diverse applications, highlighting their necessity in advancing biosensor technology to control environmental contamination/human diseases in order to manage One Health.

Digital Technologies for Quality Assurance in the Construction Industry: Current Trend and Future Research Directions towards Industry 4.0

Despite the growing rich and fragmented literature focusing on quality assurance (QA) and Industry 4.0, the implementation of associated individual digital technologies has not been fully evaluated and synthesised to achieve adequate QA in the construction industry; hence, it has received limited focus. This study, thus, aimed to organise, evaluate, and synthesise the current literature on individual digital technology applications in QA in the construction industry and propose future research directions. A literature review approach was adopted for this study along with Deming’s cycle framework to address four research questions: (1) What is the status of the state-of-the-art in the literature? (2) What digital technologies have been applied for QA in the construction industry? (3) Which areas in QA processes have experienced digital technology applications, and what are the applications? (4) What are the limitations of the existing studies and future research directions of digital technologies for QA in the construction industry? The findings showed an increasing trend of research on digital technology for QA in construction since 2017. This cuts across 23 countries with six different research methods published across 18 different publication sources. Four categories of digital technologies were revealed to have been adopted for QA in construction based on the functionality of the technologies: data collection technologies, decision-oriented technologies, collaborative technologies, and transparency and security-related technologies. Evaluation with Deming’s cycle framework revealed that digital technologies have a high level of application at the “do” phase, improving the quality management process during construction towards achieving pre-stated quality requirements. This includes mostly collaborative technologies, consisting of BIM technologies. Limitations of the existing studies were further identified, and this led to five research directions: interoperability of technology development, integrated digital technologies for QA of prefabricated and modular construction, integrated digital technologies for QA of cross-border construction logistics and supply chain, digital innovation for sustainable QA, and moving beyond the technical solution. The study showed a significant contribution to both academia and the industry in the built environment.

Efficient testing of weld seam models with radii of curvature in the millimeter range using laser ultrasound

Laser ultrasound is a widely used tool for industrial quality assurance when a contactless and fast method is required. In this work, we used a laboratory setup based on a confocal Fabry–Perot interferometer to examine weld seam models. The focus was placed on small samples with curved surfaces (small in the sense that the radius of curvature is comparable to the largest ultrasonic wavelength) and on efficient ways to detect the presence and volume of process pores, with the goal to transfer this method to industrial applications. In addition to this experimental method for investigating welds, a numerical method that models the experimental setup was implemented in MATLAB. For this purpose, first the thermal effects of the excitation process were taken into account by solving the thermal diffusion equation with an explicit scheme. Then, the elastodynamic equations were solved using the Elastodynamic Finite Integration Technique, taking into account the stresses induced by the excitation process. The B-Scans generated with this numerical model were compared with experimental B-Scans for simple test cases and good agreement was found. In a next step, the additional structures in the B-Scans resulting from air inclusions were identified and investigated with both methods using flat test specimens at first. Besides the direct echoes, structures from skimming surface waves and multiple reflections were visible. These additional structures are unwanted in defect reconstruction methods like the Synthetic Aperture Focusing Technique (SAFT) as they would lead to artifacts. In samples much larger than the largest ultrasound wavelength, however, these unwanted structures are still negligible in amplitude or can be well separated temporally, but for small samples this is no longer the case. As a result, reconstruction methods based on direct echoes like SAFT are difficult to apply. For many industrial applications, the reconstruction is not decisive at all, but only the knowledge of the total volume of process pores (TVPP). It is shown with both experimental and numerical methods, that this TVPP can be estimated from the variation in the B-Scans from various small weld seam models.

Comparing the Performance of Raman and Near-Infrared Imaging in the Prediction of the In Vitro Dissolution Profile of Extended-Release Tablets Based on Artificial Neural Networks.

In this work, the performance of two fast chemical imaging techniques, Raman and near-infrared (NIR) imaging is compared by utilizing these methods to predict the rate of drug release from sustained-release tablets. Sustained release is provided by adding hydroxypropyl methylcellulose (HPMC), as its concentration and particle size determine the dissolution rate of the drug. The chemical images were processed using classical least squares; afterwards, a convolutional neural network was applied to extract information regarding the particle size of HPMC. The chemical images were reduced to an average HPMC concentration and a predicted particle size value; these were used as inputs in an artificial neural network with a single hidden layer to predict the dissolution profile of the tablets. Both NIR and Raman imaging yielded accurate predictions. As the instrumentation of NIR imaging allows faster measurements than Raman imaging, this technique is a better candidate for implementing a real-time technique. The introduction of chemical imaging in the routine quality control of pharmaceutical products would profoundly change quality assurance in the pharmaceutical industry.

Research on target defect detection algorithm based on improved YOLO-V7

The goal of this study is to increase target identification accuracy and defect detection performance using the enhanced YOLO-V7 target defect detection algorithm. You Only Look Once version 7 is referred to as YOLO-V7 and is a well-liked real-time target identification technique. To make high-quality goods, however, it is essential to find flaws, and the traditional YOLO-V7 has certain restrictions when it comes to addressing specific flaws. To get around these restrictions, we implemented a number of changes to YOLO-V7. The study's enhanced YOLO-V7 target defect detection algorithm may find use in the areas of industrial automation, quality assurance, and safety monitoring.

Comparison of Optical and Stylus Methods for Surface Texture Characterisation in Industrial Quality Assurance of Post-Processed Laser Metal Additive Ti-6Al-4V.

Additive manufacturing technologies enable lightweight, functionally integrated designs and development of biomimetic structures. They contribute to the reduction in material waste and decrease in overall process duration. A major challenge for the qualification for aerospace applications is the surface quality. Considering Ti-64 laser powder bed fusion (LPBF) parts, particle agglomerations and resulting re-entrant features are characteristic of the upper surface layer. Wet-chemical post-processing of the components ensures reproducible surface quality for improved fatigue behaviour and application of functional coatings. The 3D SurFin® and chemical milling treatments result in smoother surface finishes with characteristic properties. In order to characterise these surfaces, three methods for surface texture measurement (contact and non-contact) were applied, namely confocal microscopy, fringe projection and stylus profilometry. The aim of this work was to show their suitability for measurement of laser powder bed fusion as-built and post-processed surfaces and compare results across the evaluated surface conditions. A user-oriented rating of the methods, summarising advantages and disadvantages of the used instruments specifically and the methods in general, is provided. Confocal microscopy reaches the highest resolution amongst the methods, but measurements take a long time. The raw data exhibit large measurement artefacts for as-built and chemically milled conditions, requiring proper data post-processing. The stylus method can only capture 2D profiles and the measurement was restricted by particle agglomerations and craters. However, the method (process and instrument) is entirely standardised and handheld devices are inexpensive, making it accessible for a large group of users. The fringe projection method was the quickest and easiest regarding measurement and data post-processing. Due to large areal coverage, reproduction of location when performing repeat measurements is possible. The spatial resolution is lower than for confocal microscopy but is still considered sufficiently high to characterise the investigated surface conditions.

Towards understanding students’ sensemaking of test case design

Context:Software testing is the most used technique for quality assurance in industry. However, in computer science education software testing is still treated as a second-class citizen and students are unable to test their software well enough. One reason for this is that teaching the subject of software testing is difficult as it is a complex intellectual activity for which students need to allocate multiple cognitive resources at the same time. A myriad of primary and secondary studies have tried to solve this problem in education, however still with very limited results. Objective:Before we can design interventions to improve our pedagogical approaches, we need to gain more in-depth understanding and recognition of sensemaking as it is happening when students design test cases. Method:An initial exploratory study identified four different sensemaking approaches used by students while creating test models. In this paper we present a follow-up study with 50 students from a large university in Spain. The used methodology was based on the previous study with the improvements that originated from its evaluation. We asked the participants to create a test model based on a description of a test problem using a specialized web-based tool for modeling test cases. We measured how well these models fit the test problem, the sensemaking process that students went through when creating the models, and the students’ perception of the modeling task. The participants received no compensation for their efforts, and we scheduled the experiment during a regular class. Apart from the created models and their metadata, we also collected recordings of the students’ computer screens made during the experiment and used a questionnaire to study their perspectives on the assignment. All the collected textual, graphical, and video data was analyzed using an iterative inductive analysis process to allow new information about the different sensemaking approaches to emerge. Results:We gained better insights into the sensemaking processes of students while modeling test cases for a problem. The results enabled us to refine our previous findings, and we identified new sensemaking approaches. Conclusions:Based on these results, we can further investigate ways to influence the sensemaking process in education, the possible misconceptions that have a negative influence on it, and the desired mental model we want our students to have to design test cases.

Quality Control and Quality Assurance in Pharmaceutical Industry

Quality assurance can be defined as “the part of quality management aimed at ensuring confidence that quality must be performed”. The trust provided by quality assurance is dual internal to management and external to clients, government agencies, regulators, certification bodies and third parties. An alternative definition is “the complete performance of targeted and organized activities within the framework of a quality method that can be documented to provide assurance that the commodity or service will meet the required quality. Quality assurance is comprehensive and does not have to do with the specific necessity of the product being developed. Quality Assurance (QA), Quality Control (QC), and Good Manufacturing Practice (GMP) are major considerations in the manufacturing, distribution, and marketing of pharmaceutical products to ensure their identification, potency, purity, pharmacological safety, and efficacy and effectiveness. (8) The terms Quality Assurance, Quality Control and Good Manufacturing Practices are defined in most international regulatory documents including WHO, USFDA, MHRA,TGA, MCC, etc. The quality of a pharmaceutical manufacturer's products depends on the fact that up to what satisfactory level of QA, QC and GMP system has been adopted in the process of production, distribution and marketing of products during their total shelf life. The main objective of this article is to demonstrate the fundamental difference between quality assurance, quality control and good manufacturing practice (GMP) and to emphasize their necessity for a pharmaceutical product. (8) This overview describes quality by design and identifies some of its elements. Process parameters and quality attributes are identified for every unit operation. The advantages, opportunities and steps involved in Quality by Design for pharmaceutical products are described. It is based on ICH guidelines Q8 for pharmaceutical development, Q9 for quality risk management and Q10 for pharmaceutical quality systems. It also provides the application of Quality by Design in pharmaceutical drug development and manufacturing.

Anomaly detection for industrial quality assurance: A comparative evaluation of unsupervised deep learning models

Across many industries, visual quality assurance has transitioned from a manual, labor-intensive, and error-prone task to a fully automated and precise assessment of industrial quality. This transition has been made possible due to advances in machine learning in general, and supervised learning in particular. However, the majority of supervised learning approaches only allow to identify pre-defined categories, such as certain error types on manufactured objects. New, unseen error types are unlikely to be detected by supervised models. As a remedy, this work studies unsupervised models based on deep neural networks which are not limited to a fixed set of categories but can generally assess the overall quality of objects. More specifically, we use a quality inspection case from a European car manufacturer and assess the detection performance of three unsupervised models (i.e., Skip-GANomaly, PaDiM, PatchCore). Based on an in-depth evaluation study, we demonstrate that reliable results can be achieved with fully unsupervised approaches that are even competitive with those of a supervised counterpart.

Quality Insight: Application of DEJI Systems Model for Quality Assurance in Industry 4.0

Quality Insight: Application of DEJI Systems Model for Quality Assurance in Industry 4.0

Quality insight: application of DEJI systems model for quality assurance in Industry 4.0

Industry 4.0 is now sweeping through business and industry. It is expected that other enterprises, including government, academia, and the military will be adopting and leveraging Industry 4.0 in the coming years. It is, thus, essential that product quality assurance under Industry 4.0 be fully understood. This paper adds to the growing knowledge base of Industry 4.0 tools and techniques. The specific methodology of the paper focuses on using the systems approach of DEJI systems model in coordination with other project implementation techniques to expatiate the efficacy of Industry 4.0 as well as quality 4.0 in diverse enterprises.

Leveraging digitilisation and machine learning for improved railway operations and maintenance

The efficient and safe movement of goods and people require reliable railway systems. Quality assurance of manufactured and assembled systems and correct maintenance of such systems are required to keep rolling stock in good operational condition. Quality assurance and maintenance in the railway industry can be costly and time-consuming, but the expansive growth of data due to smart sensors and monitoring technologies makes it possible to leverage the potential of machine learning to reduce cost and labour. Improved reliability and safety, and reduced costs are benefits that the use of “Big Data” and machine learning techniques can realise. However, despite these potential benefits for manufacturers, rail operators, and passengers, the rail industry is still labelled for its lack of innovation, while in most other industries, data is regarded as a strategic asset for competitive advantage.This paper demonstrates how machine learning and data analysis can be used to benefit railway industry manufacturers and operators when applied to rolling stock data. It also illustrates the lost opportunity in the rail industry for not applying data-driven solutions to their full potential. The paper also discusses the current applications of machine learning in the railway industry and provides the requirements for the implementation of machine learning techniques. Machine learning is applied to pantograph data of a South African railway operator's rolling stock. Classification – a machine learning technique – is used to identify and categorise events within the dataset to discover whether pantograph bounce occurs due to faulty sensors, faulty pantographs, or defective infrastructure. In this paper it is demonstrated how machine learning can benefit rail manufacturers and operators to improve manufacturing and assembly processes, as well as maintenance practices. It is concluded that railways should treat data similarly to other railway assets, with suitable management and governance practices.

Automobile windshield assembly workstation based on ABB industrial robot

Traditional automotive companies’ manual assembly line operation with higher labor intensity, lower efficiency, and product quality is difficult to ensure and other shortcomings have been unable to meet the growing demand for industry development and quality assurance. This project with the IRB- 1410 model robot as the core robot, combined with an industrial vision system and gluing system, completed a vehicle windshield assembly workstation based on ABB industrial robots, to improve the degree of automation while greatly improving the efficiency and degree of operational safety, and can both improve the visibility of companies while establishing a good image of the enterprise.

Defect-based fatigue model for additive manufacturing

A novel defect-based fatigue model for the prediction of S–N (stress versus number of cycles) data points and curves is proposed in this paper. The model is capable of predicting the material fatigue performance based on defect size and location from the surface. A defect factor was introduced and obtained based on notch theory, which considers the notch sensitivity of the material as well as the stress concentration obtained using the finite element method. A newly developed equation was applied to represent the relationship between the defect factor, defect size and defect location from the surface. AlSi10Mg samples were manufactured using laser powder bed fusion, and then machined. The samples were tested under rotational bending cyclic loading until failure. The failed samples were analysed using scanning electron microscopy and it was found that cracks initiated from defects located at the surface. The measured defect size and location were used to predict the number of cycles for an applied stress using the proposed defect-based fatigue model. This model was validated by comparing the predicted and experimentally obtained S–N data. The proposed model has the potential to be applied to component-level fatigue assessment and integrated into industrial quality assurance workflows. For instance, defects can be measured for each produced industrial component and directly assessed against fatigue performance using the developed defect-based fatigue model. This could enable the rapid approval and certification of future additively manufactured industrial components, which can unleash the commercial potential of additive manufacturing for light-weight multi-functional component designs.

Selective breeding and characterization of a black mealworm strain of Tenebrio molitor Linnaeus (Coleoptera: Tenebrionidae)

• Black larvae found in the yellow mealworm ( Tenebrio molitor Linnaeus) farms. • The black larvae were identified as T. molitor. • Black larvae of T. molitor were completely fixed by selective breeding. • Yellow and black larvae had no significant differences in growth traits and fecundity. Larvae of Tenebrio molitor Linnaeus are edible insects and are approved as a food ingredient in Korea. They are typically yellow; however, rare black larvae have been found in breeding boxes at insect farms. It is not clear whether black larvae represent a different species that invaded and hybridized with the yellow larvae of T . molitor or whether T . molitor shows intraspecific color variation. In this study, we characterized and identified black larvae for applications in industrial fields as well as accurate breeding and management. First, in a comparative analysis, we did not detect differences in the morphological characteristics of yellow and black larvae and adults, with the exception of larval body color. For accurate species identification, molecular analyses ( p -distances and neighbor-joining) were performed based on partial COI sequences of 33 yellow and seven black larvae. Genetic divergence between yellow and black larvae ranged from 0.0% to 2.1%, revealing intraspecific variation. A neighbor-joining analysis strongly supported the classification of the two morphs as a single species. Black larvae were separated from yellow larvae and maintained by selective breeding. As a result, black larvae were completely fixed in the F2 generation (F1 = 96% and F2 = 100%). Yellow and black larvae showed no significant differences in developmental characteristics and fecundity. These findings improve our understanding of diversity within an important edible insect species and contribute to quality assurance in the food industry based on clear species identification.

Non-contact surface roughness evaluation of milling surface using CNN-deep learning models

ABSTRACT Machining quality control is a bottleneck operation as human inspectors and expensive equipment is needed in most operations. Automated quality assurance in the manufacturing industry has the potential to replace humans and lower the cost of the machined product. This paper presents the analysis of end-milled machined surfaces backed with experimental and deep learning model investigations. The effects of machining parameters like spindle speed, feed rate (table feed), depth of cut, cutting speed, and machining duration were investigated to find machined surface roughness using Taguchi orthogonal array. Following standard DOE, surface roughness and machined surface image were recorded for each machining experiment and categorized into four classes, viz. fine, smooth, rough, and coarse, based on the roughness value (Ra). The machined surface images were used to develop CNN models for surface roughness class prediction. Further, comparative studies among the five popular optimizers were performed. The results showed that the CNN model with the ‘Rectified Adam’ optimizer performed better amongst the optimizer pool, with the training and test accuracy of 96.30% and 92.91%, respectively. The proposed CNN model features a highly accurate and slim structure, potentially substituting human quality control procedures that employ expensive surface roughness measuring devices.

New trends in the ultrasonic imaging of concrete structures by means of 3D-FMC technology

The novel technologies in ultrasound generation and data processing offer new opportunities for three-dimensional imaging of concrete structures. Especially the Full-Matrix-Capture (FMC) technique with its real-time imaging capability can be successfully utilized for quality assurance in the construction industry. Although, the available testing instruments on the market implement the principle of linear transducer array with its two-dimensional reconstruction of B-Scan images based on the Synthetic Aperture Focusing Technique (SAFT) principle. The Dry-Point-Contact (DPC) transducers utilized in commercially available instruments for concrete testing with their matrix-like layout offer a direct opportunity to implement a three-dimensional Full-Matrix-Capture (FMC) data acquisition cycle. The obtained data can be in real-time processed by 3D-TFM reconstruction and visualized and evaluated three-dimensionally. In the present contribution both state-of-the-art and novel true-3D imaging approaches and data assessment are considered on real concrete inspection objects and the advantages of enhanced data processing with respect to improved information content and easiness of result, interpretation is discussed.