Quality Assurance Methodology Research Articles

Automation Inspection in Metal Fabrication: Enhancing Sheet Metal Forming Processes with Automation, Machine Vision and Image Correlation for Quality Assurance

This review emphasizes the evolving need for automated inspection in metal fabrication processes due to the increasing complexity of design advancements over the years. The study explores various defect detection algorithms and evaluates their effectiveness in enhancing the accuracy and reliability of the inspection process. Machine vision plays a crucial role in this context, contributing significantly to the precision of the inspection process in metal fabrication. Its ability to handle complex tasks ensures a thorough assessment of manufactured components. The paper also explores the use of digital image correlation (DIC) as a key tool in quality assurance for metal fabricated products. This technique provides detailed insights, enabling a thorough understanding of structural integrity and defect identification. By integrating insights on automated inspection through defect detection algorithms, machine vision and DIC, this review aims to advance quality assurance methodologies in the ever-evolving field of metal fabrication.

Generating synthetic building electrical load profiles using machine learning based on the CRISP-ML(Q) framework

Abstract This study presents a machine learning application for generating synthetic building electrical load profiles. The implementation followed the Cross Industry Standard Process for the development of Machine Learning Applications with Quality assurance methodology, or CRISP-ML(Q) framework, to ensure a systematic machine learning development process. The model training performance was evaluated using the mean absolute error (MAE), root mean squared error (RSME), and coefficient of determination (R2) which were observed to be 0.0739, 0.1119, and 0.5728, respectively. These metrics remained consistent during the model testing phase, suggesting robust model performance. During the initial simulation experiment, the MAE and RMSE of the generated synthetic load profile were found to be 0.014 and 0.016, respectively, underscoring high model accuracy. Additional evaluation experiments showed that the developed machine learning application can generate realistic building load profiles using high-level parameters such as building type, average daily load, and peak demand. This study can aid in the development of demand-side management strategies and building energy management systems by providing realistic building electrical load profiles especially when real-world data is limited. For future work, researchers can consider integrating additional model features, refining data processing methods, and developing an agile version of the CRISP-ML(Q) framework.

Cloud-Based Institutional Quality Assurance Model for a Local Higher Education Institution Using Data Mining Techniques

This study presents a Cloud-Based Institutional Quality Assurance Model for Local Higher Education Institutions (LHEIs). It utilizes advanced data mining techniques and custom data models that enhance the monitoring, assessment, and improvement of quality assurance processes through the utilization of cloud computing infrastructure in a developed system that offers scalability, flexibility, and accessibility, empowering policymakers to make data-driven decisions and to efficiently analyze vast amounts of data collected from various sources and be guided by algorithms. The findings advance quality assurance methodologies and offer valuable insights for practitioners, policymakers, and researchers in higher education.

ORGANIZATION OF INTERNAL QUALITY ASSURANCE SYSTEMS AS A CONSTRUCT OF THE NATIONAL QUALITY ASSURANCE SYSTEM: PRACTICE OF KAZAKHSTANI UNIVERSITIES

The restructuring of the quality assurance system of higher education in Kazakhstan is considered a condition for the formation of competitive human potential. The research interest of the authors of this article is aimed at the practice of organizing the internal quality assurance system as the first component of the three-dimensional model of the National Quality Assurance System, which also includes the external quality assurance system and mechanisms of management and regulation of the quality assurance system. The activity of Kazakhstani HEIs with orientation on the European Standards and Guidelines for Quality Assurance in Higher Education in the European Higher Education Area (ESG EHEA) is carried out through the activity of designing a system of measures, including planning, organization and implementation of the actual internal system of quality assurance, adequate to the features of the educational environment of the HEI. As a result of design activity, "author's" normative, organizational, personnel conditions of internal quality assurance systems are created. The purpose of our research was to analyze the practices of organization of internal quality assurance system by HEIs of Kazakhstan as a construct of the National Quality Assurance System. The study allowed us to identify the common and special in the organization of internal quality assurance systems in the context of the implementation of quality assurance methodology in higher education organizations, in the context of academic freedom.

Technical Notes: Robustness of three-dimensional treatment and imaging isocenter testing using a new gel dosimeter and kilovoltage CBCT.

Coincidence of the treatment and imaging isocenter coordinates is required to safely perform small-margin treatments, such as stereotactic radiosurgery of multiple brain metastases. A comprehensive and direct methodology for verifying concordance of kilovoltage cone-beam computed tomography (kV-CBCT) and treatment coordinates using an x-ray CT-based polymer gel dosimeter (dGEL) and onboard kV-CBCT was previously reported. Using this methodology, we tested the ability of a new commercially available x-ray CT-based polymer dGEL with a rapid response to provide efficient quality assurance (QA). The aim of this study was to evaluate the robustness of the three-dimensional geometric QA methodology using dGEL. The dGEL were commercially manufactured. The prescribed dose for each field was determined by visually identifying the 5, 10, and 20Gy isodose lines. A linear accelerator was used to irradiate the gels with seven non-coplanar beams. An in-house analysis program was used to identify the beam axes and treatment isocenter in kV-CBCT coordinates by processing the pre- and post-irradiation CBCT images. The impact of the radiation dose on the test reproducibility was examined, and the detectability of an intentional geometric error was assessed. The treatment isocenter was within 0.4mm of the imaging isocenter for all radiation doses. The residual error of the test with the intentional error was within 0.2mm. The analysis and image quality variations for a single dGEL introduced displacement errors less than 0.3mm. The test assessed the coincidence of treatment and kV-CBCT isocenter coordinates and detected errors with high robustness. Even for a 10Gy dose, the test yielded results comparable with those obtained using higher radiation doses owing to the rapid response of the dGEL dosimeter.

Quality assurance dysfunctions

This article presents findings from a longitudinal case study of academic audit as enacted at a typical New Zealand university between 1994 and 2019. Academic audit is a higher education quality assurance methodology applied to New Zealand universities. The longitudinal study yielded findings that could not have been detected in a single, five-year audit cycle, in particular, that very similar recommendations were given more than once. Three sets of recurrent recommendations were studied in detail: closing the student feedback loop; monitoring PhD students’ progress and ‘quality assurance infrastructure and leadership’. The issues emerging from these three topics are ‘audit dysfunctions’, some of which are attributable to the institution and some of which belong to the external quality assurance agency. The audit dysfunctions are intended as a heuristic for improving the rigour and effectiveness of higher education quality assurance.

Methodology for Quality Assurance of Educational Software

Evaluating the quality of educational software is a priority given the number of educational systems currently being produced. The article carries out a documentary analysis to search for documents that have addressed these elements. The results address the main documents that have been obtained. Subsequently, a methodology containing a system of metrics to evaluate the quality of educational software is proposed.

Don't judge a book or health app by its cover: User ratings and downloads are not linked to quality.

To analyse the relationship between health app quality with user ratings and the number of downloads of corresponding health apps. Utilising a dataset of 881 Android-based health apps, assessed via the 300-point objective Organisation for the Review of Care and Health Applications (ORCHA) assessment tool, we explored whether subjective user-level indicators of quality (user ratings and downloads) correlate with objective quality scores in the domains of user experience, data privacy and professional/clinical assurance. For this purpose, we applied spearman correlation and multiple linear regression models. For user experience, professional/clinical assurance and data privacy scores, all models had very low adjusted R squared values (< .02). Suggesting that there is no meaningful link between subjective user ratings or the number of health app downloads and objective quality measures. Spearman correlations suggested that prior downloads only had a very weak positive correlation with user experience scores (Spearman = .084, p = .012) and data privacy scores (Spearman = .088, p = .009). There was a very weak negative correlation between downloads and professional/clinical assurance score (Spearman = -.081, p = .016). Additionally, user ratings demonstrated a very weak correlation with no statistically significant correlations observed between user ratings and the scores (all p > 0.05). For ORCHA scores multiple linear regression had adjusted R-squared = -.002. This study highlights that widely available proxies which users may perceive to signify the quality of health apps, namely user ratings and downloads, are inaccurate predictors for estimating quality. This indicates the need for wider use of quality assurance methodologies which can accurately determine the quality, safety, and compliance of health apps. Findings suggest more should be done to enable users to recognise high-quality health apps, including digital health literacy training and the provision of nationally endorsed "libraries".

Conceptual Aspects of Quality Assurance in Higher Education

This article presents the main provisions of the Concept of Quality Assurance of Higher Education, which is considered both from the point of view of the consumer (student, employer) and from the point of view of compliance with the consumption of an educational service (product). The essence of the new paradigm of quality assurance is substantiated, based on the assertion that quality is a relative and multi-level concept. Approaches to quality assurance at different levels of management, differing in the instrumental part, are characterized. The thesis that the quality management system is understood as an organizational structure, policies, processes, procedures and resources necessary for the implementation of quality management is revealed. The authors presented Vision for the formation of a culture of quality. At the same time, the level of quality culture is determined both at the institutional and national levels. The essence of the quality assurance methodology, which is a system of principles and methods of organizing activities to support and improve quality, is revealed. The methodology of the quality assurance system is based on academic values and fundamental principles. Important elements, methods and tools of the system are presented internal quality assurance.

Interprétation de l’hydrogéochimie de la Molasse d’Eau Douce Supérieure (Obere Süßwassermolasse) dans la région de Munich (Bavière, Allemagne) à l’aide d’une analyse multivariée et d’une modélisation géologique en 3D

AbstractIntense use of groundwater in urban areas requires appropriate monitoring, which in turn necessitates proper data management with employment of increasingly sophisticated statistical methods and mapping tools. An example of such an urban area with intensive use of groundwater is the study area of GeoPot Project, namely Munich (Germany) and its surroundings. The aim of the presented study was to provide a description of the hydrogeochemical characteristics of the aquifers occurring in the Quaternary and Upper Freshwater Molasse (German: Obere Süßwassermolasse – OSM) sediments and to further improve the understanding of interactions between the aquifers. The focus was put on the identification of hydrochemical facies, the chemical signatures of different water types, an understanding of occurring processes, and spatial relationships between the aquifers. In order to deal with hydrogeochemical data generated for this study, as well as with data coming from existing external databanks (e.g. BIS-BY), a methodology of quality assurance was developed. The analytical methods focused on multivariate statistics. To enhance the interpretation of the obtained clusters, a recently developed three-dimensional geological model was used for better understanding and presentation. It was found that in the study area, deeper aquifer systems represent the most distinct hydrogeochemical signature of the Na–HCO3 water type. In the remaining clusters, a transition from deeper (alkaline) to shallow (alkaline-earth) groundwater can be observed. The results of the study can be utilized for improved, sustainable groundwater management.

Software Quality Assurace

Abstract: Software Quality Assurance (SQA) plays a pivotal role in the software development lifecycle, ensuring the delivery of high-quality software products. This abstract provides a comprehensive overview of the latest advancements in Software Quality Assurance methodologies, practices, and tools. The evolving landscape of software development, characterized by agile methodologies, DevOps practices, and continuous integration, has necessitated a reevaluation and enhancement of SQA processes. The abstract begins by elucidating the fundamental principles of SQA, emphasizing its critical role in detecting and preventing defects throughout the software development process. It explores the shift-left approach, where quality assurance is integrated early in the development cycle, and the benefits of this approach in terms of cost reduction and accelerated time-tomarket. Furthermore, the abstract delves into the integration of artificial intelligence (AI) and machine learning (ML) techniques in SQA processes. The use of AI-driven testing tools for automated test case generation, anomaly detection, and predictive analysis is explored as a means to enhance the efficiency and effectiveness of quality assurance activities. The abstract also discusses the challenges and ethical considerations associated with the use of AI in SQA. Additionally, the abstract highlights the importance of comprehensive test automation strategies in modern SQA. It outlines the advantages of continuous testing, test-driven development (TDD), and behavior-driven development (BDD) methodologies in ensuring robust software quality. The abstract also addresses the challenges of maintaining test automation suites and advocates for a balanced approach that combines automated and manual testing. The abstract concludes by emphasizing the need for a holistic and adaptive SQA framework that aligns with the dynamic nature of software development. It calls for ongoing research and collaboration in the SQA domain to address emerging challenges and capitalize on new opportunities for improving software quality

On the reproducibility of extrusion-based bioprinting: round robin study on standardization in the field

The outcome of three-dimensional (3D) bioprinting heavily depends, amongst others, on the interaction between the developed bioink, the printing process, and the printing equipment. However, if this interplay is ensured, bioprinting promises unmatched possibilities in the health care area. To pave the way for comparing newly developed biomaterials, clinical studies, and medical applications (i.e. printed organs, patient-specific tissues), there is a great need for standardization of manufacturing methods in order to enable technology transfers. Despite the importance of such standardization, there is currently a tremendous lack of empirical data that examines the reproducibility and robustness of production in more than one location at a time. In this work, we present data derived from a round robin test for extrusion-based 3D printing performance comprising 12 different academic laboratories throughout Germany and analyze the respective prints using automated image analysis (IA) in three independent academic groups. The fabrication of objects from polymer solutions was standardized as much as currently possible to allow studying the comparability of results from different laboratories. This study has led to the conclusion that current standardization conditions still leave room for the intervention of operators due to missing automation of the equipment. This affects significantly the reproducibility and comparability of bioprinting experiments in multiple laboratories. Nevertheless, automated IA proved to be a suitable methodology for quality assurance as three independently developed workflows achieved similar results. Moreover, the extracted data describing geometric features showed how the function of printers affects the quality of the printed object. A significant step toward standardization of the process was made as an infrastructure for distribution of material and methods, as well as for data transfer and storage was successfully established.

Presenteeism Reloaded – We Need a Revised Presenteeism Approach

Presenteeism Reloaded – We Need a Revised Presenteeism Approach

Contouring quality assurance methodology based on multiple geometric features against deep learning auto-segmentation.

Contouring error is one of the top failure modes in radiation treatment. Multiple efforts have been made to develop tools to automatically detect segmentation errors. Deep learning-based auto-segmentation (DLAS) has been used as a baseline for flagging manual segmentation errors, but those efforts are limited to using only one or two contour comparison metrics. The purpose of this research is to develop an improved contouring quality assurance system to identify and flag manual contouring errors. DLAS contours were used as a reference to compare with manually segmented contours. A total of 27 geometric agreement metrics were determined from the comparisons between the two segmentation approaches. Feature selection was performed to optimize the training of a machine learning classification model to identify potential contouring errors. A public dataset with 339 cases was used to train and test the classifier. Four independent classifiers were trained using five-fold cross validation, and the predictions from each classifier were ensembled using soft voting. The trained model was validated on a held-out testing dataset. An additional independent clinical dataset with 60 cases was used to test the generalizability of the model. Model predictions were reviewed by an expert to confirm or reject the findings. The proposed machine learning multiple features (ML-MF) approach outperformed traditional nonmachine-learning-based approaches that are based on only one or two geometric agreement metrics. The machine learning model achieved recall (precision) values of 0.842 (0.899), 0.762 (0.762), 0.727 (0.842), and 0.773 (0.773) for Brainstem, Parotid_L, Parotid_R, and mandible contours, respectively compared to 0.526 (0.909), 0.619 (0.765), 0.682 (0.882), 0.773 (0.568) for an approach based solely on Dice similarity coefficient values. In the external validation dataset, 66.7, 93.3, 94.1, and 58.8% of flagged cases were confirmed to have contouring errors by an expert for Brainstem, Parotid_L, Parotid_R, and mandible contours, respectively. The proposed ML-MF approach, which includes multiple geometric agreement metrics to flag manual contouring errors, demonstrated superior performance in comparison to traditional methods. This method is easy to implement in clinical practice and can help to reduce the significant time and labor costs associated with manual segmentation and review.

Демонстрационный экзамен как средство повышения качества государственной итоговой аттестации выпускников автомобильного колледжа

The results of the state final assessment (SFA) determine the further updating of the educational process. During the integration of federal state educational standards and the new WorldSkills standards in the system of secondary vocational education, a new component - a demo exam - appeared in the structure of the state fi nal assessment. It emphasizes the simulation of real working conditions to solve practical problems of professional activity in accordance with the best world standards and national practices. The article presents the features of conducting a demo exam according to the WorldSkills standards for the majors and professions of the automotive industry. The authors discuss the positive and negative characteristics of this SFA form and consider the possibility of including the independent assessment of qualifi cations (IAQ) in the SFA system. This modifi cation provides conditions for conjugating the education system and the labor market. The state fi nal assessment of the graduates of secondary vocational education institutions has revealed the need to change the content and methodology of the training process and assessment activities. As a result of integrating the traditional system of fi nal assessment with the WorldSkills standards for the training area (major) 23.02.07 “Maintenance and repair of engines, systems and assemblies of motor vehicles” in the Automotive College, the authors have developed and tested a model of the demo exam based on the independent quality assurance methodology. The model provides a comprehensive assessment of the professional competence of graduates.

An approach for semi-automated data quality assurance within BIM models

Abstract Successful Building Information Modelling (BIM) enabled projects that require large volumes of project data to be embedded within BIM models. However, with this wealth of data, relevance and accuracy have been identified as important issues affecting the BIM performance of the project. Currently, Quality Assurance (QA) in the industry has focused on geometric data, including scrutinising physical and spatial clashes. However, as BIM practices progress in the industry, the requirements for nongeometric model data and their quality have become more necessary. This study aimed to ascertain the feasibility of using visual programming for semi-automating the BIM QA process in a practical case study on using BIM in infrastructure projects. This paper outlines a generic semi-automated QA methodology and its application in a construction project case study. The validity of this method was tested and evaluated in practice through (n=2) workshops. The methodology was implemented within an integrated engineering consultancy, employing visual programming methodology to generate QA summaries and additionally highlight model elements with data quality issues based on a defined set of parameters. Based on the evaluation findings, the proposed process was feasible and provided a pathway for low-cost and low-skill QA of BIM model data within the architecture, engineering and construction (AEC) industry. The paper’s main scientific contribution is a conceptual framework for using visual programming to achieve automatic quality assurance.

Efficient dose-volume histogram-based pretreatment patient-specific quality assurance methodology with combined deep learning and machine learning models for volumetric modulated arc radiotherapy.

Weak correlation between gamma passing rates and dose differences in target volumes and organs at risk (OARs) has been reported in several studies. Evaluation on the differences between planned dose-volume histogram (DVH) and reconstructed DVH from measurement was adopted and incorporated into patient-specific quality assurance (PSQA). However, it is difficult to develop a methodology allowing the evaluation of errors on DVHs accurately and quickly. To develop a DVH-based pretreatment PSQA for volumetric modulated arc therapy (VMAT) with combined deep learning (DL) and machine learning models to overcome the limitation of conventional gamma index (GI) and improve the efficiency of DVH-based PSQA. A DL model with a three-dimensional squeeze-and-excitation residual blocks incorporated into a modified U-net was developed to predict the measured PSQA DVHs of 208 head-and-neck (H&N) cancer patients underwent VMAT between 2018 and 2021 from two hospitals, in which 162 cases was randomly selected for training, 18 for validation, and 28 for testing. After evaluating the differences between treatment planning system (TPS) and PSQA DVHs predicted by DL model with multiple metrics, a pass or fail (PoF) classification model was developed using XGBoost algorithm. Evaluation of domain experts on dose errors between TPS and reconstructed PSQA DVHs was taken as ground truth for PoF classification model training. The prediction model was able to achieve a good agreement between predicted, measured, and TPS doses. Quantitative evaluation demonstrated no significant difference between predicted PSQA dose and measured dose for target and OARs, except for Dmean of PTV6900 (p=0.001), D50 of PTV6000 (p=0.014), D2 of PTV5400 (p=0.009), D50 of left parotid (p=0.015), and Dmax of left inner ear (p=0.007). The XGBoost model achieved an area under curves, accuracy, sensitivity, and specificity of 0.89 versus 0.88, 0.89 versus 0.86, 0. 71 versus 0.71, and 0.95 versus 0.91 with measured and predicted PSQA doses, respectively. The agreement between domain experts and the classification model was 86% for 28 test cases. The successful prediction of PSQA doses and classification of PoF for H&N VMAT PSQA indicating that this DVH-based PSQA method is promising to overcome the limitations of GI and to improve the efficiency and accuracy of VMAT delivery.

Determining a methodology of dosimetric quality assurance for commercially available accelerator-based boron neutron capture therapy system.

The irradiation field of boron neutron capture therapy (BNCT) consists of multiple dose components including thermal, epithermal and fast neutron, and gamma. The objective of this work was to establish a methodology of dosimetric quality assurance (QA), using the most standard and reliable measurement methods, and to determine tolerance level for each QA measurement for a commercially available accelerator-based BNCT system. In order to establish a system of dosimetric QA suitable for BNCT, the following steps were taken. First, standard measurement points based on tissue-administered doses in BNCT for brain tumors were defined, and clinical tolerances of dosimetric QA measurements were derived from the contribution to total tissue relative biological effectiveness factor-weighted dose for each dose component. Next, a QA program was proposed based on TG-142 and TG-198, and confirmed that it could be assessed whether constancy of each dose component was assured within the limits of tolerances or not by measurements of the proposed QA program. Finally, the validity of the BNCT QA program as an evaluation system was confirmed in a demonstration experiment for long-term measurement over 1 year. These results offer an easy, reliable QA method that is clinically applicable with dosimetric validity for the mixed irradiation field of accelerator-based BNCT.

A transit portal dosimetry method for respiratory gating quality assurance with a dynamic 3D printed tumor phantom.

BackgroundsRespiratory gating is one of the motion management techniques that is used to deliver radiation dose to a tumor at a specific position under free breathing. However, due to the dynamic feedback process of this approach, regular equipment quality assurance (QA) and patient‐specific QA checks need to be performed. This work proposes a new QA methodology using electronic portal imaging detector (EPID) to determine the target localization accuracy of phase gating.MethodsQA tools comprising 3D printed spherical tumor phantoms, programmable stages, and an EPID detector are characterized and assembled. Algorithms for predicting portal dose (PD) through moving phantoms are developed and verified using gamma analysis for two spherical tumor phantoms (2 cm and 4 cm), two different 6 MV volumetric modulated arc therapy plans, and two different gating windows (30%–70% and 40%–60%). Comparison between the two gating windows is then performed using the Wilcoxon signed‐rank test. An optimizer routine, which is used to determine the optimal window, based on maximal gamma passing rate (GPR), was applied to an actual breathing curve and breathing plan. This was done to ascertain if our method yielded a similar result with the actual gating window.ResultsHigh GPRs of more than 97% and 91% were observed when comparing the predicted PD with the measured PD in moving phantom at 2 mm/2% and 1 mm/1% levels, respectively. Analysis of gamma heatmaps shows an excellent agreement with the tumor phantom.The GPR of 40%–60% PD was significantly lower than that of the 30%–70% PD at the 1 mm/1% level (p = 0.0064). At the 2 mm/2% level, no significant differences were observed. The optimizer routine could accurately predict the center of the gating window to within a 10% range.ConclusionWe have successfully performed and verified a new method for QA with the use of a moving phantom with EPID for phase gating with real‐time position management.

Methodology for Quality Assurance of Groundwater Resources Exploitation Within Belgorod Region

Methodology for Quality Assurance of Groundwater Resources Exploitation Within Belgorod Region