A Curvature-Based Three-Dimensional Defect Detection System for Rotational Symmetry Tire

With the rapid development of the global manufacturing industry, an efficient and accurate quality control system has become key to enhancing competitiveness. Ultrasonic Nondestructive Testing (NDT), as an efficient means of quality inspection, plays a crucial role in improving manufacturing quality through the precision of its data analysis. This study aims to explore the application of ultrasonic NDT data in manufacturing quality control by integrating machine learning technologies, with a specific focus on the Wavelet Neural Network optimized by Genetic Algorithms (GA-WNN). This study achieved significant prediction and evaluation results by applying a GA-WNN to quality control in manufacturing. Compared to traditional Wavelet Neural Network (WNN) models, the GA-WNN more effectively identifies and predicts potential quality issues, especially in noisy data and complex production environments, demonstrating higher accuracy and stability. When predicting possible defect types in the manufacturing process, the GA-WNN showed a notable improvement in accuracy over other models. Additionally, in quality stability evaluation, GA-WNN was able to capture production fluctuations more accurately, providing more valuable results for decision-making. The methodologies and discoveries of this study offer new perspectives and tools for quality control in manufacturing and the analysis of ultrasonic NDT data, presenting broad application prospects.

In the process of checking products quality, there are mis-acceptance and mis-rejection that come from random measuring error. By analyzing the relationship between the measuring error and the measuring value, a new method which decreases the misjudgment probability of products is presented. After revealing the distributing law on measuring value of over-tolerance and in-tolerance products under the condition of steady process and measurement, the mathematics model of mis-acceptance and mis-rejection probability function is established and the data table of misjudgment probability function is designed. Using the data table, the misjudgment probability of products can be decreased effectively. Finally, the quality control of products manufacture is analyzed based on the data table of the misjudgment probability for actual application.

An intelligent hybrid approach for industrial quality control combining neural networks, fuzzy logic and fractal theory

Purpose: Targeting the members of manufacturing and Service industry Quality Control team this study is going is to carry out research about whether the outcome of OCB(Organizational Citizenship Behaviors) and organization can be brought about by organizational culture that is suitable for them. this study tries to identify the direct･indirect causal relationship between these variables and OCB by selecting organizational culture as a leading variable and job satisfaction as a parameter.Methods: SPSS 22.0 was used for data analysis and AMOS 18.0 statistical program for structural equation model analysis. For the descriptive statistics this study verified reliability analysis, feasibility analysis, structural equation model analysis, research hypothesis, and mediating effects.Results: As a result of path analysis estimating the regression coefficients for the linear structure analysis of the correlations between variables for the hypothesis verification, the rational culture among the organizational culture types of the manufacturing Quality Control team showed a positive (+) effect on the job satisfaction, and hierarchical culture has negative(-) effect on job satisfaction.Conclusion: This study suggested that the composition and friendly behavior of desirable organizational culture has a very close relationship in connection between job satisfaction and OCB by examining the causal relationship between OCB for improvement activities for organizational culture by establishing the organizational culture and job satisfaction of the manufacturing Quality Control team.

Introduction The extremely small fiber size and low mass of nanofiber composites makes quality control measurement very difficult. Most commercially available contact measurement equipment cannot work with the nanofiber composite without altering its characteristics. Generally, non-contacting optical methods are not effective because the wavelength of the light used is greater than the diameter of the nanofibers. Non-contact Gamma or Beta gauges lack the necessary resolution. Research methods such as Scanning Electron Microscopy (SEM) are not practical in a production setting. Large-scale production of nanofiber composites requires unique quality control (QC) methods. Figures 1a, 1b, and 1c show examples of nanofiber composites manufactured by Donaldson. As Donaldson Company’s nanofiber production has increased to over 10,000 m per day during the last 20 years, QC methods have been developed to address these needs. Four evaluation tools that have been developed for commercial production of nanofibers will be described. These evaluation tools focus on: uniformity of nanofiber distribution, fiber size distribution, durability of fiber layer in the composite, and environmental resistance.

Polygonum multiflorum Thunb. (PM), called Heshouwu in China, is a popular Chinese medicine in clinical practice. Several clinical studies have been conducted to evaluate the traditional therapeutic claims and to study the potential therapeutic activity of PM in dyslipidemia and neurodegenerative diseases, highlighting available clinical evidence. In recent years, reports on clinical adverse reactions of Raw Radix P. multiflorum (RPM) and P. multiflorum Praeparata (PMP) have been on the increase, especially with respect to liver injury. Most liver injury cases had been assessed for causality using RUCAM (Roussel Uclaf Causality Assessment Method) in this paper. However, the components of PM responsible for the reported hepatotoxic effects have not yet been identified. Moreover, many of the reports are contradictory, while studies on the mechanism involved in PM-induced liver damage are not comprehensive. This study was aimed at reviewing the status of research on liver injury due to PM, including clinical characteristics, risk factors, material basis research and mechanism of action, with a view to understanding PM-induced hepatotoxicity, and taking reasonable and effective measures to prevent it. In short, quality control is still one of the major safety problems in TCM drug safety concerns. The model of safety monitoring and risk management of PM drugs is not yet developed. Indeed, the characteristics and risk factors associated with PM require both proper understanding and control of the risk by strengthening standardization of clinical applications, basic science research, quality control in manufacturing, active monitoring methodology and enhancement of international communication and cooperation. Measures should also be encouraged and implemented to promote healthy development of the TCM industry.

In the process of steel production, the defects on the surface of steel will adversely affect the subsequent processing of a product. Accurate detection of such defects is the key to improve production efficiency and economic benefits. In this paper, an end-to-end steel surface defect detection and size measurement system based on the YOLOv5 model is designed. Firstly, in consideration of the defect location and direction correlation in the production process, a coordinate attention mechanism is added at the head of YOLOv5 to strengthen the spatial correlation of the steel surface and an adaptive anchor box generation method based on defect shape difference feature is proposed, which realizes the detection of three main types of defects on the Pytorch deep learning framework. Secondly, BiFPN is used to strengthen the feature fusion and a transformer encoder is added to improve the performance of detecting small defects. Thirdly, calculate the conversion ratio between the pixel and the actual size according to the standard reference specimen and obtain the actual size through the pixel statistics of the defect area to achieve pixel level size measurement. Finally, the steel surface defect detection and size measurement system are designed in this paper, which consist of various hardware, related measurement, and detection algorithms. According to the experimental results, the comprehensive defect detection accuracy of this method reaches 93.6%, of which the scratch detection accuracy reaches 95.7%. The detection speed reaches 133 fps and the defect size measurement accuracy reaches 0.5 mm. Experimental result shows that the defect detection and size measurement system designed in this paper can accurately detect and measure various industrial production defects and can be applied to the actual production process.

Depend on circumstance more complex and changeable, the traditional control chart no longer meets the need of a more and more industrializing and complicated application environment. Therefore, this thesis is to analyze the shortage of traditional process control, and then propose dynamic quality control of multi-variety and small batch manufacturing based on Bayesian Monitor, which is come up with by Bianca. A case of a Chinese electronic chip manufacturer is studied with Bayesian Monitor, which together with the method studied is proved practical and of help to the quality control.

Existing performance standards for low-voltage contacts are too low because they do not ensure a useful operational life. Improvement is both possible and necessary, and requires increased rigour in testing. Structures differ in the mechanical duties which they perform, but are alike in that they are required to transmit similar magnitudes of voltage and current and are susceptible to environmental attack. For a test to be appropriate, the environment and measuring circuit must be relevant to service conditions, and for it to be sufficiently rigorous and brief, the environment must be ‘artificial’, so as to produce accelerated deterioration. The voltage and current employed in the measuring circuit must be limited, so that there is no tendency to obscure the effect of environmental attack. Unification is necessary in the acceptance tests specified for nominally different structures, and, correspondingly, in the specffications issued to industry.The most generally useful artificial environment is probably the ‘industrial’ or ‘sulphur-polluted’ type, and the simplest way of producing it is by the combustion of a fuel similar to those used for industrial or domestic purposes. For semi-permanent structures, comparisons have been made with a variety of test specimens and commercial components of the effects of exposure in three different environments, namely a ‘natural’ one, and two ‘artificial industrial’ ones. With low-level pollution (between ten and twenty times greater than that encountered in a severe natural industrial environment) at room temperature, and a working week of five eight-hour days, exposure times of between two and three months are required. An increase of pollution or temperature, or both, or continuous working, might enable this time to be shortened. For sliding contacts, the effect of exposure during sliding is important at even lower levels of pollution.The preservation of near-metallic contact, with reasonable contact forces in adverse environments, usually necessitates the use of coatings of precious metals such as gold, platinum, palladium or rhodium. For semi-permanent contacts, the coating requirements are determined almost entirely by considerations of environmental attack. For sliding contacts, considerations of mechanical wear are also involved.Such coatings can be applied in several ways: usually, they are deposited from solutions of metallic salts—most commonly, by electrodeposition. Various coatings of different kinds have been examined, and the results show that they are often not good enough for contact use. Thickness, adhesion and freedom from porosity are frequently inadequate. Improvements in basic plating techniques, and more stringent control of production processes, are needed. Thorough quality control in manufacture requires checks occupying, perhaps, only a few minutes. Electrographic tests are very suitable for this purpose, especially when supported by long-term exposure tests on a smaller number of samples. Printed-circuit boards can be plated successfully, provided solutions are used which do not destroy the copper-laminate bond.With sliding contacts, given good mechanical design and manufacture, precious-metal coatings can have much longer lives than are commonly supposed. These lives can be increased, at least threefold, by dry transfer lubrication using a suitable plastic. Laboratory tests, with a variety of coatings of different kinds, and also field experience, show that two-motion switches of the kind used in the Strowger system of automatic telephony can be made suitable for the transmission of low voltages, without ‘wetting’.With plug-socket structures, design experience shows that the use of pre-tensioned buffered springs is worthy of more general application.

Multicore systems are widely used to improve performance and satisfy time and power demands from users. A computer system with more than one core working simultaneously does satisfy such demands. The main importance of the multicore system is to increase the speed of the execution, which enables the algorithm designer to run tasks in parallel. In this work, we use thermal video captured for the purpose of nondestructive testing aiming for real time analysis. Indeed, thermal videos for inspection purposes have been in the forefront of many applications such in quality control in manufacturing, in searching for living bodies in large fires, and in finding lost people in the wilderness. All these applications among many others demand real time processing. The purpose of this work is to use a multicore system to speed up processing operations for thermal video as the data of choice in feature detection and classification. Experimental results show that the processing time for Canny edge detection algorithm is improved 1.93 times using 2 processes, and 2.6 times using 4 processes. Another detection preprocess that was tested is the histogram equalization of the thermal video, which was improved 2.23 times using 2 processes, and 2.73 times using 4 processes.

The automation of quality control in manufacturing has made great strides in recent years, in particular following new developments in machine learning, specifically deep learning, which allow to solve challenging tasks such as visual inspection or quality prediction. Yet, optimum quality control pipelines are often not obvious in specific settings, since they do not necessarily align with (supervised) machine learning tasks. In this contribution, we introduce a new automation pipeline for the quantification of wear on electrical contact pins. More specifically, we propose and test a novel pipeline which combines a deep network for image segmentation with geometric priors of the problem. This task is important for a judgement of the quality of the material and it can serve as a starting point to optimize the choices of materials based on its automated evaluation.

Zippers play a vital role in the functionality and durability of apparel and accessories, yet they are often overlooked in favour of aesthetic considerations such as length and colour. The study employed Total Quality Management (TQM) Theory. TQM emphasizes continuous improvement, customer satisfaction, and quality control in manufacturing and production. This theory is relevant because it highlights the importance of producing zippers that meet industry standards, ensuring durability, strength, and functionality. This study employed a comparative experimental research design to evaluate the mechanical performance, colourfastness, and dimensional stability of five selected zipper brands (A, B, C, D and E) on the Ghanaian Market composed of metal and nylon materials. A total of 72 zippers were procured for the experiment. Mechanical strength was assessed based on top stop strength, puller attachment strength, and lateral strength, while colourfastness was tested under washing and rubbing conditions. Dimensional stability was measured in terms of percentage changes in length and width. The statistical software employed for the analysis of the data collected was the Statistical Package and Service Solution (SPSS) for Windows version 26. The results indicate that metal zippers exhibit superior mechanical strength, higher colourfastness ratings, and better dimensional stability than nylon zippers. ANOVA and t-tests at 0.05 alpha levels confirm that these differences are statistically significant (p < 0.001 and p = 0.002, respectively). Correlation analysis reveals strong positive relationships between colourfastness and mechanical strength, emphasizing the impact of material quality on zipper durability. The findings highlight the importance of selecting high-performance zippers for applications requiring enhanced durability and longevity. Metal zippers consistently outperformed nylon zippers in all measured parameters, showing greater resistance to tensile forces, superior colour retention, and minimal dimensional changes. The statistical analyses confirm these findings, with significant differences observed between the two material types. The correlation analysis further supports the interdependence between colourfastness and mechanical strength, indicating that zippers with better colour retention tend to have higher strength parameters. It was recommended that product designers should prioritize strength and durability by selecting zippers suitable for the intended application, ensuring robustness in high-stress environments. Quality control teams must focus on maintaining high wash and wet colourfastness in zippers, particularly for products exposed to frequent washing or outdoor conditions. To guarantee the selection of high-quality materials, standardized testing protocols should be implemented to evaluate zipper performance in real-world conditions.

Clinical discoveries largely depend on dedicated clinicians and scientists to identify and pursue unique and unusual clinical encounters with patients and communicate these through case reports and case series. This process has remained essentially unchanged throughout the history of modern medicine. However, these traditional methods are inefficient, especially considering the modern-day availability of health-related data and the sophistication of computer processing. Outlier analysis has been used in various fields to uncover unique observations, including fraud detection in finance and quality control in manufacturing. We propose that clinical discovery can be formulated as an outlier problem within an augmented intelligence framework to be implemented on any health-related data. Such an augmented intelligence approach would accelerate the identification and pursuit of clinical discoveries, advancing our medical knowledge and uncovering new therapies and management approaches. We define clinical discoveries as contextual outliers measured through an information-based approach and with a novelty-based root cause. Our augmented intelligence framework has five steps: define a patient population with a desired clinical outcome, build a predictive model, identify outliers through appropriate measures, investigate outliers through domain content experts, and generate scientific hypotheses. Recognizing that the field of obstetrics can particularly benefit from this approach, as it is traditionally neglected in commercial research, we conducted a systematic review to explore how outlier analysis is implemented in obstetric research. We identified two obstetrics-related studies that assessed outliers at an aggregate level for purposes outside of clinical discovery. Our findings indicate that using outlier analysis in clinical research in obstetrics and clinical research, in general, requires further development.

ObjectiveThis study compared the performance of 12 brands of cartomizer style electronic cigarettes (EC) using different puffing protocols and measured the concentrations of nicotine in each product.MethodsAir flow rate, pressure drop, and aerosol absorbance were measured using two different protocols, first 10 puffs and a modified smoke-out protocol.ResultsFirst 10 puff protocol: The air flow rate required to produce aerosol ranged between brands from 4–21 mL/s. Pressure drop was relatively stable within a brand but ranged between brands from 14–71 mmH2O and was much lower than the earlier classic 3-piece models. Absorbance, a measure of aerosol density, was relatively consistent between puffs, but varied between brands. With the modified smoke-out protocol, most brands were puffed until 300 puffs. The pressure drop was relatively stable for all brands except three. Absorbance of the aerosol decreased as the number of puffs increased. Although there was some uniformity in performance within some brands, there was large variation between brands. The labeled and measured nicotine concentrations were within 10% of each other in only 1 out of 10 brands.ConclusionsOver 10 puffs, the cartomizers all perform similarly within a brand but varied between brands. In smoke-out trials, most brands lasted at least 300 puffs, and performed similarly within brands with respect to pressure drop and absorbance. For five brands, products purchased at different times performed differently. These data show some improvement in performance during evolution of these products, but nevertheless indicate problems with quality control in manufacture.

With the advancement of intelligent manufacturing, automated defect detection systems are playing an increasingly vital role in improving product quality. However, complex interference factors in real-world industrial environments pose significant challenges to the stability and reliability of detection algorithms. Addressing this critical issue, this study proposes an interference-resistant defect detection algorithm tailored for complex industrial scenarios. By thoroughly analyzing typical interference characteristics in industrial settings, the study develops a comprehensive solution incorporating adaptive preprocessing, robust feature extraction, and intelligent decision optimization. The algorithm innovatively integrates physical models with deep learning techniques, achieving breakthroughs in key areas such as image enhancement, feature representation, and defect recognition. Experimental results demonstrate that the proposed method exhibits superior detection performance under various interference conditions, significantly improving the environmental adaptability and operational stability of industrial inspection systems. This research provides a practical and effective technological pathway for quality control in intelligent manufacturing.