Manufacturing Quality Control Research Articles

Nondestructive Quantitative Measurement for Precision Quality Control in Additive Manufacturing Using Hyperspectral Imagery and Machine Learning

Nondestructive Quantitative Measurement for Precision Quality Control in Additive Manufacturing Using Hyperspectral Imagery and Machine Learning

Enhancing Fault Detection in Image Analysis: A Combined Wavelet-Fourier Technique for Advancing Manufacturing Quality Control

Enhancing Fault Detection in Image Analysis: A Combined Wavelet-Fourier Technique for Advancing Manufacturing Quality Control

Mathematical modeling of the technological process of twisting a waveguide

Twisted waveguides are part of waveguide paths of antenna- feeder devices of spacecraft. They have high requirements for accuracy and reliability. They are made of profiled rectangular pipes. The twisting of waveguide pipes with a cross- sectional size of less than 11x5.5 mm is carried out in the device using a rigid calibration mandrel. The calculation method determined the main parameters of the technological process of twisting the waveguide tube (torque M, drawing force Pпр). When twisting workpieces with cross sections according to GOST, the torques (M) and drawing forces (Pпр) from the cross- section sizes vary according to a nonlinear relationship. Twisting the workpiece from M4 requires less torque (M) and drawing force (Pпр) than for the workpiece from M1. By mathematical modeling, it is possible to select the optimal technological mode in advance, thereby ensuring high quality of waveguide twisting manufacturing. To substantiate the calculation results, the methods of control of technological parameters and quality control of the product manufacturing were considered. A problem has been found in measuring the torque and pulling force when twisting the workpiece. Therefore, the pulling force should be measured from the machine. A method for measuring torque using a non- contact torque sensor is proposed. The manufacturing quality is supposed to be carried out by the STRESSVISION mechanical stress scanner, and electrical tests.

Atomic Force Microscopy Characterization of Electrochemically Polished Nickel-Titanium Memory Alloy

Atomic force microscopy (AFM) is a powerful characterization technique used for monitoring metals or metal alloys surfaces changes [1-14]. AFM works by using a small probe, typically with a sharp tip, to scan the surface of a sample. As the probe moves over the surface, it measures the height and other properties of the surface topography, allowing for detailed analysis of the surface characteristics. Using AFM, it is possible to obtain high-resolution images in 2-dimintinal (2-D) or 3-dimintinal (3-D), allowing for detailed analysis of the surface morphology, roughness, and other properties. AFM can also be used to measure surface features, such as the height and depth of surface defects, or the thickness of surface coatings.Smooth surfaces of metals or metal alloys are needed for many biomedical applications such as implants and stents. Electrochemical polishing (EP) is a process used to smooth and improve the surface finish of metals or alloys by selectively removing surface roughness using an optimum electric current. The resulting surface can have a highly polished and uniform finish. In this study, AFM is used to monitor the surface changed before and after EP of memory alloy (Nickel-Titanium, Ni-Ti Alloy). AFM image of a Ni-Ti Alloy’s surface is polished using a current density ranging from 0 to 0.147 A/cm2, which shows a highly polished surface after for 15 minutes. Surface roughness of the Ni-Ti alloy in ionic liquid as an acid free EP resulted in surface roughness significantly is reduced from 64 nm (before EP) to 11 nm without significantly altering the alloy elemental composition. This study provides an electrochemical acid free EP method to effectively control the relative smoothness of Ni-Ti implants at the nanoscale (less than 12 nm) which could offer many potential biomedical applications including controlling cell interactions with Ni-Ti alloy at bio-interfaces. Overall, AFM is a powerful tool for characterizing metal surfaces after electrochemical polishing, providing detailed information about the surface morphology and chemical composition that can be useful for a range of applications, from materials science research to quality control in manufacturing. References Mahapatro, T. D. Matos Negrón, C. Bonner, and T. M. Abdel-Fattah, J. Biomater. Tissue Eng., 3, 196-204 (2013).Bhure, A. Mahapatro, C. Bonner, T. M. Abdel-Fattah, Materials Science and Engineering: C, 33(4), 2050-2058 (2013).Bhure, T. M. Abdel-Fattah, C. Bonner, J. C. Hall, and A. Mahapatro, Applied Surface Science, 257(13), 5605-5612 (2011).M. Abdel-Fattah, D. Loftis and A.Mahapatro, Journal of Biomedical Nanotechnology, 7(6) 794-800 (2011).M. Abdel-Fattah, D. Loftis and A. Mahapatro, Journal of Surfaces and Interfaces of Materials, 3, 67-74 (2015)JD Loftis, TM Abdel-Fattah, Colloids and Surfaces A: Physicochemical and Engineering Aspects 511, 113-119 (2016)M. Abdel-Fattah, D. Loftis and A. Mahapatro, Nanoscience and Nanotechnology, 5(2), 36-44 (2015)Bhure, T. M. Abdel-Fattah, C. Bonner1, J. C. Hall, and A. Mahapatro, Journal of Biomedical Nanotechnology, 6(2), 117-128 (2010).M. Abdel-Fattah, D. Loftis, ECS Transactions, 25, (39), 57-61 (2010).M. Abdel-Fattah, D. Loftis, A. Mahapatro, ECS Transactions, 25(19), 57-61 (2010).M. Abdel-Fattah, Derek Loftis, ECS Transactions, 25(7), 327-332 (2009).AI Wixtrom, JE Buhler, CE Reece, TM Abdel-Fattah, Journal of The Electrochemical Society 160 (3), E22 (2013)AI Wixtrom, JE Buhler, CE Reece, TM Abdel-Fattah, Journal of Environmental Chemical Engineering 1 (1-2), 18-22 (2013)M. Abdel-Fattah and J, D. Loftis, Sustainable Chemistry, 3(2), 238-247 (2022)M. Abdel-Fattah and J. D. Loftis, Molecules, 25, 23, 5712 (2020)

Review on Biological Indicator

Biological indicators (BIs) serve as crucial tools in assessing the efficacy of sterilization and disinfection processes across diverse industries, including healthcare, pharmaceuticals, and food. This review examines key features and the significance of BIs, emphasizing their microbial sensitivity, reproducibility, resistance to specific sterilization methods, and distinctive cultural characteristics. The ability of BIs to withstand stringent conditions ensures they accurately reflect the most resistant microorganisms, providing a reliable measure of sterilization effectiveness. In applications, BIs play a pivotal role in ensuring sterilization assurance in medical settings, contributing to quality control in pharmaceutical manufacturing, and safeguarding food safety in the food industry. Their importance in preventing healthcare-associated infections and maintaining product quality underscores their indispensable role in various sectors.[1] The review also highlights emerging trends in BI technology, including integration with sensors for real-time monitoring, the adoption of single-use BIs for convenience and reduced cross-contamination risks, and the customization of BIs for specific sterilization processes. These trends indicate a continual evolution in BI capabilities, enhancing their precision and utility in assessing and validating sterilization procedures. In conclusion, biological indicators are fundamental components of quality assurance programs, offering a reliable means to validate sterilization processes. As technology advances, the integration of BIs with monitoring tools is expected to further refine their accuracy and broaden their applicability across industries.[2].

Advancing chipboard milling process monitoring through spectrogram-based time series analysis with Convolutional Neural Network using pretrained networks

This paper presents a novel approach to enhance chipboard milling process monitoring in the furniture manufacturing sector using Convolutional Neural Networks (CNNs) with pretrained architectures like VGG16, VGG19, and RESNET34. The study leverages spectrogram representations of time-series data obtained during the milling process, providing a unique perspective on tool condition monitoring. The efficiency of the CNN models in accurately classifying tool conditions into distinct states (`Green', `Yellow', and `Red') based on wear levels is thoroughly evaluated. Experimental results demonstrate that VGG16 and VGG19 achieve high accuracy, however with longer training times, while RESNET34 offers faster training at the cost of reduced precision. This research not only highlights the potential of pretrained CNNs in industrial applications but also opens new avenues for predictive maintenance and quality control in manufacturing, underscoring the broader applicability of AI in industrial automation and monitoring systems.

Analisis Pemanfaatan Artificial Intelligence Dalam Membantu Proses Perekrutan Karyawan Perusahaan

This article delves deep into the role of Artificial Intelligence (AI) in assisting human resources professionals in the recruitment and selection process. AI is a result of the emergence of machine learning, which can mimic human thought patterns and working mechanisms. AI, or artificial intelligence, has been applied in various fields, including quality control in manufacturing, the entertainment industry, autonomous vehicles, and is increasingly making inroads into the realm of recruitment. With the support of algorithms, AI plays a vital role in helping HR professionals filter applicant portfolios, fundamentally transforming the traditional recruitment process. Through a comprehensive analysis drawing from various literature sources, this article seeks to uncover the wide-ranging changes and profound impacts brought by AI into the world of recruitment. The conclusion drawn from this exploration is that AI has the potential to expedite the acquisition of high-quality applicants more efficiently and cost-effectively while also reducing subjectivity in human assessments. However, this transformation is not without concerns about ethics and data security, which require careful consideration.

Accurate color characterization of solar photovoltaic modules for building integration

Accurate and reproducible color characterization is essential for colored building integrated photovoltaic products, both for manufacturing quality control and assessing long-term color stability. However, existing characterization techniques struggle to accurately determine color when a surface is behind a transparent layer like a solar PV laminate. In this study, we compare different colorimetric techniques and propose an innovative colorimeter based on a fiber optic spectrometer and large area illumination to address this issue. Samples with varying transparent glass thicknesses and underlying colors are laminated and characterized using a scanner, an integrated sphere spectrometer, a commercial portable colorimeter, and the proposed large area illumination colorimeter. Results show that common scanners produce darker images and inaccurate color determination due to light losses in the glass. As glass thickness increases, reflectance decreases with the integrated sphere spectrometer and portable colorimeter. However, the large area illumination colorimeter exhibits only minimal signal reduction. High reflective foils experience more reflectance reduction with thicker glass than low reflective ones. All devices yield comparable results without the glass layer. The large area illumination colorimeter, compensating for light losses, proves to be a suitable solution for accurately measuring color under glass laminates using reflected light. For example, it reduces the color change from 57 (commercial portable colorimeter) to only 3 for an ivory colored glass laminate. This innovative tool has the potential to improve color characterization in building integrated photovoltaic products, enabling better manufacturing quality control and assessment of long-term color stability.

Machine Learning in Precision Manufacturing: A Collaborative Computer and Mechanical Engineering Perspective

Precision manufacturing, characterized by the production of intricate components with stringent tolerances, has witnessed a paradigm shift with the integration of machine learning (ML) techniques. This research paper delves into the collaborative endeavors between computer and mechanical engineering disciplines, exploring how the fusion of expertise from both domains contributes to the optimization and advancement of precision manufacturing processes. [1] The motivation behind this research stems from the recognition of ML's transformative potential in revolutionizing precision manufacturing. This paper reviews the application of ML in key areas such as predictive maintenance, process optimization, quality control, and adaptive manufacturing strategies. It emphasizes the synergy between computer and mechanical engineers in designing and implementing intelligent algorithms that harness data from various sensors, enabling real-time adjustments to manufacturing parameters. Predictive maintenance emerges as a critical aspect of the collaborative efforts, where ML algorithms analyze sensor data to predict equipment failures, thereby reducing downtime and maintenance costs. Process optimization is explored through the integration of ML in Computer Numerical Control (CNC) machining, highlighting how algorithms optimize tool paths and enhance machining precision. Quality control in precision manufacturing is addressed by intelligent systems that utilize computer vision and sensor technologies, providing real-time defect detection. [2] The collaborative approach presents challenges, including data security and privacy concerns and the need for skillset integration between computer and mechanical engineering disciplines. The paper concludes by exploring future directions, including the integration of emerging technologies such as edge computing and the Internet of Things (IoT), underscoring the importance of interdisciplinary collaboration for the successful implementation of ML techniques in achieving heightened precision, efficiency, and quality in precision manufacturing.

Improvement of the qmosRT-PCR Assay and Its Application for the Detection and Quantitation of the Three Serotypes of the Novel Oral Polio Vaccine in Stool Samples.

Recently, genetically stable novel OPVs (nOPV) were developed by modifying the genomes of Sabin viruses of conventional OPVs to reduce the risk of reversion to neurovirulence and therefore the risk of generating circulating vaccine-derived polioviruses. There is a need for specific and sensitive methods for the identification and quantification of nOPV viruses individually and in mixtures for clinical trials and potentially for manufacturing quality control and environmental surveillance. In this communication, we evaluated and improved the quantitative multiplex one-step reverse transcriptase polymerase chain reaction (qmosRT-PCR) assay for the identification and quantification of nOPV viruses in samples with different formulations and virus concentrations and in virus-spiked stool samples. The assay was able to specifically identify at least 1 log10 CCID50/mL of each serotype in the presence of the two other serotypes at high concentrations (6-7 log10 CCID50/mL) in the same sample. In addition, the lowest viral concentration that the assay was able to detect in stool samples was 17 CCID50/mL for nOPV1 and nOPV2 viruses and 6 CCID50/mL for nOPV3. We also found high correlation between the expected and observed (by qmosRT-PCR) concentrations of spiked viruses in stool samples for all three nOPV viruses, with R-squared values above 0.95. The analysis of samples collected from an nOPV2 clinical trial showed that 100% of poliovirus type 2 was detected and few samples showed the presence of type 1 and 3 residuals from previous vaccinations with bOPV (at least 4 weeks prior vaccination with nOPV2), confirming the high sensitivity of the method. The qmosRT-PCR was specific and sensitive for the simultaneous identification and quantification of all three nOPV viruses. It can be used as an identity test during the nOPV manufacturing process and in evaluation of virus excretion in nOPV clinical trials.

Quality Management of Probiotics: Ensuring Safety and Maximizing Health Benefits.

Consumption of probiotics, which are beneficial live microorganisms, has received a lot of attention because of their potential to improve health and wellness. Robust quality control measures are necessary to ensure the safety of probiotics and maximize their health effects. This review delves into the topic of quality management in probiotics, highlighting the significance of sticking to strict guidelines from manufacture to storage to distribution. Probiotic quality standards, Good Manufacturing Practices (GMP) implementation, quality control and testing techniques, and documentation and traceability systems are all discussed in detail. The importance of taking precautions to avoid microbial contamination, meeting all applicable regulations, and clearly marking and packaging probiotic products is also emphasized. In addition, it reviews the clinical evidence supporting the possible health advantages of probiotics and investigates the processes through which probiotics enhance health. The review continues by stressing the significance of educating and informing consumers about probiotics and their proper use in order to maximize health benefits. Probiotics' potential health benefits can be maximized and consumer faith in these helpful microbes can be bolstered by adopting thorough quality management measures to ensure their safety, efficacy, and consistency.

Design, Deployment, and Evaluation of an Industrial AIoT System for Quality Control at HP Factories

Enabled by the increasingly available embedded hardware accelerators, the capability of executing advanced machine learning models at the edge of the Internet of Things (IoT) triggers interest of applying Artificial Intelligence of Things (AIoT) systems for industrial applications. The in situ inference and decision made based on the sensor data allow the industrial system to address a variety of heterogeneous, local-area non-trivial problems in the last hop of the IoT networks. Such a scheme avoids the wireless bandwidth bottleneck and unreliability issues, as well as the cumbersome cloud. However, the literature still lacks presentations of industrial AIoT system developments that provide insights into the challenges and offer lessons for the relevant research and industry communities. In light of this, we present the design, deployment, and evaluation of an industrial AIoT system for improving the quality control of HP Inc.’s ink cartridge manufacturing lines. While our development has obtained promising results, we also discuss the lessons learned from the whole course of the work, which could be useful to the development of other industrial AIoT systems for quality control in manufacturing.

A lattice structure coupon sample for build quality control in metal additive manufacturing

Quality control in Additive Manufacturing (AM) is vital, asprocess parameters can lead to defects, impacting mechanical properties. The present study proposes the use of alattice structure coupon sample as a proxy for build quality control. Lattices aresensitive to variations in process parameters,impacting the resulting mechanical properties, more so than bulk mechanical test specimens. Thesensitivity of lattice structures allows for the detection ofsubtle changes in process parameters, which can be valuable for quality control efforts, i.e. maintaining constant manufacturing quality. In this study, a series of lattice coupon samples were manufactured with varying factors as a proof of concept to demonstrate this concept. Non-destructive micro-computed tomography (CT) wasused to quantify the manufacturing variations/defects. The lattice coupons are subjected to quasi-static compression testing to evaluate theirmechanical properties, with differences shown indicative of changes in the manufacturing process. The work reported here acts as a proof of concept for a low-cost build quality evaluation methodology using lattice coupons and compression testing alone. When the build quality deteriorates or parameters change without the knowledge of this by the operator, this will be observed in changes in the compressive test results for lattice coupons.

Intelligent surface roughness measurement using deep learning and computer vision: a promising approach for manufacturing quality control

Intelligent surface roughness measurement using deep learning and computer vision: a promising approach for manufacturing quality control

Automated Interlayer Wall Height Compensation for Wire Based Directed Energy Deposition Additive Manufacturing.

Part quality monitoring and control in wire-based directed energy deposition additive manufacturing (w-DEDAM) processes has been garnering continuous interest from both the academic and industrial sectors. However, maintaining a consistent layer height and ensuring that the wall height aligns closely with the design, as depicted in computer-aided design (CAD) models, pose significant challenges. These challenges arise due to the uncertainties associated with the manufacturing process and the working environment, particularly with extended processing times. To achieve these goals in an industrial scenario, the deposition geometry must be measured with precision and efficiency throughout the part-building process. Moreover, it is essential to comprehend the changes in the interlayer deposition height based on various process parameters. This paper first examines the behaviour of interlayer deposition height when process parameters change within different wall regions, with a particular focus on the transition areas. In addition, this paper explores the potential of geometry monitoring information in implementing interlayer wall height compensation during w-DEDAM part-building. The in-process layer height was monitored using a coherent range-resolved interferometry (RRI) sensor, and the accuracy and efficiency of this measurement were carefully studied. Leveraging this information and understanding of deposition geometry, the control points of the process parameters were identified. Subsequently, appropriate and varied process parameters were applied to each wall region to gradually compensate for wall height. The wall height discrepancies were generally compensated for in two to three layers.

The Transformative Potential of Lipid Nanoparticle-Protein Corona for Next-Generation Vaccines and Therapeutics.

The integration of the lipid nanoparticle (LNP)-protein corona as a pioneering approach for the development of vaccines against the present and future SARS-CoV-2 variants of concern marks a significant shift in the field. This concept holds great promise, offering a universal platform that can be adaptable to combat future pandemics caused by unknown viruses. Understanding the complex interactions among the protein corona, LNPs, and receptors is crucial for harnessing its potential. This knowledge will allow optimal vaccine formulations and improve their effectiveness. Safety assessments are essential to ensure suitability for human use, compliance with regulatory standards, and rigorous quality control in manufacturing. This transformative workflow requires collaborative efforts, expanding our foundational knowledge and translating advancements from the laboratory to clinical reality. The LNP-protein corona approach represents a paradigmatic shift with far-reaching implications. Its principles and insights can be leveraged beyond specific applications against SARS-CoV-2, enabling a universal platform for addressing viral threats, cancer, and genetic diseases.

Recurrence Network-Based 3D Geometry Representation Learning for Quality Control in Additive Manufacturing of Metamaterials

Abstract Metamaterials are designed with intricate geometries to deliver unique properties, and recent years have witnessed an upsurge in leveraging additive manufacturing (AM) to produce metamaterials. However, the frequent occurrence of geometric defects in AM poses a critical obstacle to realizing the desired properties of fabricated metamaterials. Advances in three-dimensional (3D) scanning technologies enable the capture of fine-grained 3D geometric patterns, thereby providing a great opportunity for detecting geometric defects in fabricated metamaterials for property-oriented quality assurance. Realizing the full potential of 3D scanning-based quality control hinges largely on devising effective approaches to process scanned point clouds and extract geometric-pertinent information. In this study, a novel framework is developed to integrate recurrence network-based 3D geometry profiling with deep one-class learning for geometric defect detection in AM of metamaterials. First, we extend existing recurrence network models that focus on image data to represent 3D point clouds, by designing a new mechanism that characterizes points’ geometric pattern affinities and spatial proximities. Then, a one-class graph neural network (GNN) approach is tailored to uncover topological variations of the recurrence network and detect anomalies associated with geometric defects in the fabricated metamaterial. The developed methodology is evaluated through comprehensive simulated and real-world case studies. Experimental results have highlighted the efficacy of the developed methodology in identifying both global and local geometric defects in AM-fabricated metamaterials.

Bipolar picture fuzzy hypersoft set-based performance analysis of abrasive textiles for enhanced quality control

Abrasive textiles have widespread industrial applications in the fields of polishing, finishing, deburring, and cleaning of various surfaces. Effective decision making and performance analysis are crucial in the development and manufacturing of abrasive textiles, as it enables manufacturers to evaluate and optimize the performance of these materials for specific applications and to make informed decisions about their production processes. For that purpose, this work aims to introduce an innovative bipolar picture fuzzy hypersoft set (BPFHSS) which is composed of two picture fuzzy hyper soft sets; one of them gives us the positive information, and the other gives us the negative information, for each membership degree, neutral membership, and non-membership degree. The properties of the designed structure and discussed alongside a thorough discussion on the De-Morgan's laws. Also, the bipolar picture fuzzy hypersoft weighted geometric (BPFHSWG) operator is defined for the BPFHSS framework to aggregate bipolar picture fuzzy hypersoft numbers (BPFHSN) information. This research highlights the importance of considering inconsistent, bipolar, and multiple sub-attribute information in decision-making processes by using the defined operators to develop an algorithm for a multi-attribute analysis for quality control of manufacture of abrasive textiles.

A hierarchical model-based method for wafer level virtual metrology under process information deficiency

Online inspection is one of the most critical processes of quality control in semiconductor manufacturing. The physical inspection methods for wafers are time-consuming and unable to achieve wafer level metrology. In order to improve production efficiency and expand inspection coverage, virtual metrology (VM) methods have recently received widespread attention; they utilize process parameters to estimate wafer metrology results. However, due to process drift and other reasons, the process information contained in real-time signal data (RTS data) used for VM modeling in industrial production is insufficient. This work proposed a hierarchical modeling method for machine learning-based virtual wafer metrology, leveraging RTS and post-process quality characteristics. The hierarchical model consists of an multiway principle analysis (MPCA) sub-model for RTS feature extracting and two separate long short-term memory (LSTM) networks for wafer-to-wafer dynamics in RTS and quality characteristics, respectively. A case study on the thickness VM of chemical vapor deposition thin film is conducted, and the proposed method has achieved better results than other methods in comparison.

Quality Control Optimization for Minimizing Security Risks Associated with Mesenchymal Stromal Cell-Based Product Development.

Mesenchymal stromal cells (MSCs) have been considered a therapeutic strategy in regenerative medicine because of their regenerative and immunomodulatory properties. The translation of MSC-based products has some challenges, such as regulatory and scientific issues. Quality control should be standardized and optimized to guarantee the reproducibility, safety, and efficacy of MSC-based products to be administered to patients. The aim of this study was to develop MSC-based products for use in clinical practice. Quality control assays include cell characterization, cell viability, immunogenicity, and cell differentiation; safety tests such as procoagulant tissue factor (TF), microbiological, mycoplasma, endotoxin, genomic stability, and tumorigenicity tests; and potency tests. The results confirm that the cells express MSC markers; an average cell viability of 96.9%; a low expression of HLA-DR and costimulatory molecules; differentiation potential; a high expression of TF/CD142; an absence of pathogenic microorganisms; negative endotoxins; an absence of chromosomal abnormalities; an absence of genotoxicity and tumorigenicity; and T-lymphocyte proliferation inhibition potential. This study shows the relevance of standardizing the manufacturing process and quality controls to reduce variability due to the heterogeneity between donors. The results might also be useful for the implementation and optimization of new analytical techniques and automated methods to improve safety, which are the major concerns related to MSC-based therapy.