Quality Evaluation Process Research Articles

Fuzzy Evaluation of Process Quality with Process Yield Index

With the rapid development and evolution of the Internet-of-Things (IoT) and big-data analysis technologies, faster and more accurate production data analysis and process capability evaluation models will bring industries closer to the goal of smart manufacturing. Small sample sizes are also common, due to destructive testing, the high costs of detection, and insufficient technological capacity, and these undermine the reliability of the statistical method. Many studies have pointed out that a confidence-interval-based fuzzy decision model can incorporate accumulated data and expert experiences to increase testing accuracy for small samples. Therefore, this study came up with a confidence-interval-based fuzzy decision model based on a process yield index. The index not only reflects process capability but also has a one-to-one mathematical relation with the process yield so that it is convenient to apply in practice. The proposed model not only diminishes the probability of misjudgment resulting from sampling error but also improves the accuracy of testing under the situation of small sample sizes, thereby contributing to the development of smart manufacturing.

Online abnormal interval detection and classification of industrial time series data based on multi-scale deep learning

BackgroundThe prosperity of industrial big data promotes the development of anomaly detection in the production process. Abnormal production process is a crucial factor affecting product quality. Current online anomaly detection focuses on individual outliers, obtained via rules based on statistical process control. It is insufficient for the detection of multi-scale anomalies in non-stationary processes. MethodsAn online detection method for abnormal interval based on multi-scale deep learning is proposed. A scale conversion layer containing identity mapping, time domain down-sampling and low-pass filtering is fused with a multi-channel convolution structure to extract multi-scale features of abnormal intervals. Besides, a long short-term memory network is presented to improve the response to long-term features. The proposed network realizes online monitoring of production process via sliding window. Significant findingsThe proposed method has been verified on the UCR benchmark dataset to be superior to comparison methods. It is further applied to the online anomaly detection of the casting speed in continuous casting process. Multi-scale abnormal intervals are accurately detected and classified, and the obtained information including location, magnitude and category can be further employed for process optimization and quality evaluation. The proposed automatic detection method contributes to replace costly manual visual inspections, realizing intelligent production.

P140 Use of the Nutrition Care Process Quality Evaluation and Standardization Tool to Inform Training in a Student Cohort

<h3>Objective</h3> To assess changes in quality of nutrition care documentation as students progress through a Future Education Model dietetics program. <h3>Use of Theory or Research</h3> Adult Learning Theory guided design and delivery of courses within the Future Education Model dietetics program at The Ohio State University. <h3>Target Audience</h3> First-year students enrolled in the masters-level Future Education Model dietetics program during the fall semester of 2021 (n = 31). <h3>Curriculum Description</h3> Lecture, peer-to-peer activities, case studies, and standardized patient experiences are used to develop student competence in nutrition care over the course of the semester. <h3>Evaluation Methods</h3> Two case study notes written in the eighth and fifteenth weeks of the course were assessed for quality using the validated NCP Quality Evaluation and Standardization Tool (NCP-QUEST). Interrater reliability was established on 10% of the notes. Differences in mean scores between the first and second notes were assessed for changes with a paired samples t-test. <h3>Results</h3> Mean NCP-QUEST scores on the first and second case study notes were 7.2 (2.8 SD) and 8.7 (2.7 SD) out of a total of 18, respectively. A significant improvement was observed between scores of the two notes (<i>P =</i> 0.013, Cohen's D = 3.3). In both notes, all students received a point for writing a nutrition prescription (NI2). Documenting succinct and relevant assessment data (NA4), noting the etiology matrix category (ND3), and utilizing standardized terminology in the intervention (NI5) were areas where students were challenged, with <1 student receiving a point in these categories. The greatest improvement between the two notes was observed in NA3: measurable assessment data provides evidence that a nutrition diagnosis is present. <h3>Conclusions</h3> Students showed significant improvement between writing their first and second nutrition notes, indicating gains in knowledge and skill as they progressed through the program. This study elucidates areas of focus for educators to improve quality of student nutrition notes. <h3>Funding</h3> None

Factors of Quality Assessment in Higher Education and Its Impact on Business Students’ Development and Interest in University Education

Human resource development at universities is one of the crucial aspects that forms an innovative and growing society. Therefore, it is crucial to continuously manage and develop factors of quality evaluation process in all universities to develop also human resources for labor market. Therefore, the aim of this paper is to identify factors of education quality which impact students´ interest in attending lessons and courses. Data were collected by students’ questionnaire at case business university. Two dimensional statistical methods were used to evaluate the results. Totally, 1,607 students were questioned. The outputs show link towards students’ interest in lessons and courses based on the quality of human resource development. There were four factors determining quality of human development found: practically oriented lessons; open and discussing teachers; subject extent and difficulty; and newcomers. The limitations of this study may be seen in the collection of data based on self-reports of students only and may therefore be subject to common-method bias. To minimize this limitation, the survey was assuring students that there was no right or wrong answer and their contribution towards higher quality was highly appreciated.

Material characterisation of additive manufacturing titanium alloy (Titanium 6Al-4V) for quality control and properties evaluations

ABSTRACT Current additive manufacturing (AM) technologies use various raw materials such as plastics and metals to fabricate parts. Moreover, the increasing use of metallic parts makes AM applicable to commercial use such as aeronautical, and medical field. For this reason, manufacturing process quality evaluation becomes essential to achieve higher quality products. Quality control and material property assessment for AM parts is a major challenge due to the complexity of the materials and geometry of the parts. Non-Destructive Testing (NDT) is being proposed as a cost-effective way to evaluate the quality of the product. For this reason, the nanoindentation, scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS) techniques are being used as useful forms of NDT techniques to assess the quality and material characteristics in metal AM samples. The material properties of additively manufactured Titanium 6Al-4 V alloy are studied to verify the in-situ process monitoring published results at different manufacturing process conditions. The comparison of material properties of three different process conditions made by direct energy deposition (DED) metal additive manufacturing is discussed to understand the variation. The results show that the characteristics of materials are a function of process parameters and have a strong correlation to the expected in-situ monitoring indicators.

RESEARCH ON QUALITY EVALUATION TECHNOLOGY OF BASIC SURVEY RESULTS OF NATURAL RESOURCES

Abstract. To execute the overall protection and comprehensive treatment of natural resources, Chinese government has earnestly fulfilled its duties of centralized management and announced the overall plan for the construction of a survey and monitoring system for natural resources. Survey and monitoring of natural resources is the basic work of management of natural resources. Survey and monitoring of natural resources is a survey project of geospatial information for natural resources based on 3S spatial information technology, massive data resources and technical means. Survey of natural resources is classified into basic survey and special survey. Therein, the basic survey, based on the surface coverage, is principally to figure out the scope, area and ownership of all sorts of natural resources projected on the earth's surface. Data quality evaluation is significant means to ensure the authenticity and accuracy of the quality and results of the survey project for natural resources. This thesis studies the inspection and evaluation technology of quality and results, designs the quality model of basic survey for natural resources, including quality characteristics, quality requirements, and quality evaluation process. Apart from that, it selects the results of land survey in some regions of the country and conducts quality evaluation experiments of basic survey data, which verifies the applicability of the quality evaluation methods.

Real-time detection method for bulk bubbles in optics based on deep learning.

The existence of bulk bubbles could decrease the laser-induced damage threshold of optics and affect the beam quality, so the detection of bulk bubbles is an essential step for quality assurance. Currently, the inspection of bubbles in optics relies on manual work, which is not recommended because of the low precision and inconsistency. To improve the quality evaluation process, a real-time detection method for bubbles inside the optics based on deep learning is proposed. Our method can implement bubble detection at 67 fps with a recall of 0.836. As for retrieval of the radius, it costs 58.8 ms on each bubble, and the absolute deviation is 3.73% on average. Our method conducts real-time and accurate detection of the positions and radii of the bubbles in the optics, thus, having significant potential for the manufacturing process.

Research on English Teaching Reading Quality Evaluation Method Based on Cognitive Diagnostic Evaluation

In order to improve the evaluation effect of English teaching reading quality, this paper applies the cognitive diagnosis evaluation algorithm to the teaching reading quality evaluation process. Moreover, this paper compares the verification effects of Q-matrix from the perspective of nonparameterization and parameterization and considers their performance through simulation research. In addition, this paper applies the method to multilevel scoring empirical data to verify the effectiveness of the cognitive diagnostic evaluation algorithm. Finally, this paper combines cognitive diagnosis and evaluation algorithms to construct an English teaching reading quality evaluation system. The experimental research results show that the English teaching reading quality evaluation method based on cognitive diagnostic evaluation proposed in this paper can effectively improve the reading quality of English teaching.

Quality Assessment Method of Information Model Reform of Higher Mathematics Education Based on Big Data

The quality of higher mathematics teaching in colleges and universities is related to a variety of influencing factors, and the changing laws are very complex, which makes the current model unable to accurately evaluate the quality of higher mathematics teaching in colleges and universities. In order to solve the deficiencies in the current teaching quality evaluation process in colleges and universities and improve the correct rate of college teaching quality evaluation, a quality evaluation method based on big data is proposed, which reforms the information model of college teaching quality evaluation. The literature related to mathematics teaching quality evaluation is researched and analyzed, and the influencing factors of higher mathematics teaching quality evaluation in colleges and universities are established. Then, collect the data of the influencing factors of higher mathematics teaching quality in colleges and universities, and establish a learning sample of college teaching quality evaluation through the star rating of the teaching quality of colleges and universities by experts. Finally, the neural network and decision tree of big data technology are introduced to train the learning samples to form an evaluation model of higher mathematics teaching quality in colleges and universities. The results show that our method can achieve high accuracy.

An Intelligent Optimization for Building Design Based on BP Neural Network and SPEA-II Multiobjective Algorithm.

With the continuous development of the field of building optimization, more and more optimization methods have sprung up, among which there are many kinds of intelligent optimization algorithms. This kind of intelligent optimization algorithm usually relies on the traditional building performance simulation method to obtain the building performance index for optimization. However, intelligent optimization algorithms generally require large-scale calculations. At the same time, the time required for building performance simulation is often limited by the complexity of building models and the configuration of computers, which leads to a long time for performance optimization, which cannot give efficient and accurate feedback to designers in engineering. Building performance optimization methods based on intelligent optimization algorithms are mainly used in scientific research and are difficult to put into practical projects. Therefore, this paper builds an accurate and efficient platform for building performance prediction and optimization to help designers make decisions combined with BP neural network and the SPEA-II multiobjective optimization algorithm. Besides, the optimization results of the case are quantitatively and qualitatively analyzed and presented in visual form based on the BP neural network prediction model. Quantitative analysis includes the evolution process of solution set, convergence process, and comprehensive quality evaluation of solution set. Qualitative analysis includes Pareto frontier and optimal architectural scheme analysis. Finally, the conclusion shows that the platform prediction and optimization can give accurate and reliable optimal solution, and the optimal building scheme is reasonable and has high engineering application value.

Nutritional Components of Beverage Granules by Near-Infrared Spectroscopy Based on PLS Model

The nutritional content of instant beverages sold in the market is uneven. The existing standards and regulatory system are not sound enough, and the phenomenon of the unknown nutritional content of ingredients frequently occurs, which makes the beverage lose its nutritional characteristics, and long-term drinking may cause harm to consumers’ health. Therefore, it is necessary to strengthen the quality and safety supervision of beverage granules and carry out quality and authenticity inspections and evaluations. The purpose of this study is to collect samples of milk tea powder and to detect its protein, total sugar, ash, fat, and moisture content. By analyzing its near-infrared spectral characteristics, the paper establishes the NIR (near-infrared spectrum) rapid detection model and the PLS (partial least square) model of protein and fat in milk powder. The study uses external inspection and cross-validation to judge the reliability of the model. The results showed that the relative error of protein was less than 1.9%, and the relative error of fat was less than 1.5%, which indicated that the model had good predictability. It provides a new strategy for the quality evaluation and formula process identification of milk tea powder. It also provides a strong reference for the improvement of local standards and the formulation of national standards for milk tea powder. The study provides strong data support for illegal businesses to take effective actions to abuse consumer sentiment and take advantage of the regulatory vacuum period.

Exploration on the Reform Strategy of Teaching Quality Evaluation System in Chinese Vocational Colleges

The teaching quality evaluation of vocational education is not only an important way to meet the national identification of technical talents, but also an objective reflection of the comprehensive management level and college running quality of vocational colleges. However, the teaching evaluation of vocational education in China has not formed a unified and standardized management system framework. Therefore, in the new era of Xi Jinping, China's research on the evaluation of teaching quality of occupation technology education is more important. PDCA cycle is Deming Cycle, which is a model of continuous quality improvement, including four repeated steps: Plan, Do, Check and Act. This is exactly the same as the process of teaching quality evaluation in vocational colleges. Based on the concept of PDCA cycle theory, this paper attempts to use PDCA method to increase the innovative potential of teaching quality evaluation, and construct a set of closed-loop evaluation management scheme of teaching quality in the whole process and all-round spiral dynamic cycle mode-a five in one pluralistic structural teaching quality evaluation and management system of vocational colleges, families, enterprises, third-party institutions and society. This paper also discusses how PDCA method will affect the design and reform of teaching quality evaluation of vocational education in China, and deeply analyzes the problems and countermeasures that vocational colleges may face after trying to implement the pluralistic structural teaching quality evaluation and management system.

Application of Edge Computing in the Quality Control of Cable Production Process

Given the lack of quantifiable evaluation criteria for the quality supervision and control in the existing power cable industry, insufficient research on the correlation of production indexes in the production process, and low timeliness in the analysis and processing of massive multi-source production data, the video monitoring model and data-driven process quality evaluation strategy model under the edge computing framework were constructed. Next, all the production data and test data in the cable production procedures were captured using edge nodes, and the factory-side data was cleansed, processed, and analyzed. The cable manufacturing quality was evaluated through edge computing, and the business process was arranged based on the edge computing results, so as to improve the cable product quality and finally optimize the total value chain of production in the whole cable industry.

Deep Probabilistic Learning for Process Quality Evaluation With a Case Study of Gear Hobbing Process

Conventional semisupervised learning approaches are limited by the reliance on assumptions of samples (manifold assumption, cluster assumption, and smoothness assumption) and may fail to reach their full potential when it comes to industrial scenarios with complicated data characteristics. In this article, a semisupervised process quality evaluation framework is proposed based on conditional variational auto-encoder. Label inference process is utilized to handle unlabeled samples during training stage and to directly predict the label during the evaluation stage. Real-valued nonvolume preserving (Real NVP) is introduced to optimize the posterior distribution in order to increase flexibility. The proposed framework does not rely on assumptions of data distributions and it learns the hidden distributions from samples. Gear hobbing simulation and experiment investigations are conducted to verify the effectiveness of the proposed framework. The results indicate that the proposed method can achieve remarkable classification performance compared with other state-of-the-art approaches.

Data and Process Quality Evaluation in a Textual Big Data Archiving System

The article presents a textual Big Data analytics solution developed in a real setting as a part of a high-capacity document digitization and storage system. A software based on machine learning techniques performs automated extraction and processing of textual contents. The work focuses on performance and data confidence evaluation and describes the approach to computing a set of indicators for textual data quality. It then presents experimental results.

Changes in the Chemical Components of Processed Rehmanniae Radix Distillate during Different Steaming Times.

Distillate was obtained in different processing cycles of processed Rehmanniae Radix (PRR). In this study, we investigated the chemical compositions of distillates 1 (Dis1) to 9 (Dis9) via GC-MS and LC-MS. Differences between Dis1–Dis9 were noticeable. A total of 13 and 21 compounds were detected via GC-MS and LC-MS, respectively, including organic acids, furans, alcohols, iridoid glycosides, phenylpropanoid glycosides, and saccharides. The relative contents of compound 2,5-hydroxymethylfurfural and furans all gradually increased with steaming time. Other compounds, however, exhibited a negative trend or fluctuated. Of these compounds, iridoid glycosides and phenylpropanoid glycosides were unstable and easily degraded, which led to a gradually decreasing concentration with increased steaming times. In addition, the degradation products were mainly derived from catalpol and acteoside, among which catalpol mainly existed as aglycone and its rearranged products. However, acteoside was converted into verbasoside through the removal of caffeoyl. Some volatile alcohols, such as phenylethyl alcohol, hydroxyphenyl ethanol, and 3-hydroxy-4-methoxybenzoic acid, were also likely from the degradation of acteoside and its homologs. These results provide an important reference basis for the processing methods, quality evaluation, and rational clinical application of PRR and its distillate.

Study on the mathematical model related to the connection properties of cold pressing joining technology * *SRAC: steel reinforced aluminum conductor.

Cold pressing joining is an important mechanical joining technology in the application of wire rope. In this paper, a new simplified equivalent model is established to describe cold pressing joining process of multilayer structures and corresponding mathematical equations describing the equivalent model are derived based on the elastic-plastic theory. In addition, the cold pressing joining experiments are carried out using the compression strain clamp (NY-300/40) and the steel reinforced aluminum conductor (SRAC - JL/G3A-300/40) for verifying the rationality of the mathematical model. Results show that the trend of the experimental results is in good agreement with the theoretical results. Finally, the critical processing parameters and quality evaluation parameters of pressed strain clamp NY-300/40 have been calculated based on the mathematical model built in this work. The universal mathematical model can be extended to other types of wire rope.

IMPLEMENTASI EVALUASI MODEL KUALITAS PROSES DAN OUTPUT PEMBELAJARAN PADA MATA KULIAH MAGANG I

This research aims to obtain an overview of the implementation of evaluation of process quality models and output quality in Internship learning.I. This research is an evaluative research approach using a model developed by Widoyoko, Which includes two main components, namely: process quality and quality of learning output. The sample in this study was a student of the Islamic Religious Education study program of the Faculty of Tarbiyah Science and Iain Ambon Teacher Training numbering 32 students. Data collection is obtained through document studies, interviews, questionnaires and observations. The data is then classified into very less, less, sufficient, good and excellent categories, based on the standards of assessment evaluation made at each stage of evaluation, and decision making. The findings in the evaluation of the Internship I Learning Program of Islamic Religious Education Faculty of Tarbiyah Science and IAIN Ambon Teacher Training, showed that the quality of the learning program process, including the good category, and the quality of internship learning output I also belonged to the excellent category. Taking into account the previous facts there are several aspects in the evaluation of the quality of the process and the quality of learning output that need to be improved so that internship learning I will be more effective and achieve the competencies that have been planned.Keywords: evaluation of process quality and quality of learning output, internship I

Efficacy and safety of Daoyin and massage for lumbar disc herniation: A protocol for overview of systematic reviews.

Background:Lumbar disc herniation (LDH) is a common disease, which can cause low back pain, sciatica, and even disability. The treatment of LDH is a global challenge. Conservative therapy with non-drugs is considered to be the first choice for patients with LDH. In recent years, an increasing number of systematic reviews and meta analyses on Daoyin and massage interventions in lumbar disc herniation have been implemented. However, the evidence quality and methodological quality of these systematic reviews/meta analyses are unknown and need to be systematically evaluated. This overview aims to systematically summarize and critically appraise the current evidence on Daoyin and massage for LDH.Methods:Eight electronic data will be retrieved, including China National Knowledge Infrastructure (CNKI), Wanfang database (WF), China Biomedical database (CBM), Chinese Scientific Journals Database (VIP), PubMed, Cochrane Library, Web of Science (WOS), and EMBASE from their inception to March 1, 2022. The reporting quality, methodological quality, risk of bias, quality of evidence will be assessed by using The Preferred Reporting Items for Systematic Reviews and Meta-analyses 2020 (PRISMA 2020), the Assessment of Multiple Systematic Reviews 2 (AMSTAR-2), the Risk of Bias in Systematic Review (ROBIS), and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE). Two independent researchers conducted literature screening, data extraction, and quality evaluation process. In addition, we will establish an overlap matrix and calculate the corrected covered area to evaluate the impact of overlapping areas on conclusions.Results:The results will be published in a peer-reviewed journal.Conclusion:This overview will provide comprehensive evidence of Daoyin and massage for treating lumbar disc herniation.Systematic review registration:INPLASY202210019.

Process Development and Quality Evaluation of Fruit Leather Enriched with Iron and Vitamin C

Fruit leather, a preserved dehydrated confectionary product, can be conveniently value added towards the ever existing iron deficiency anemia and increasing prevalence of oxidative stress related diseases. The objectives of the study was to process fruit leather with the fruits rich in iron and vitamin C with and without preservatives and evaluate physiochemical and sensory quality of fruit leathers. Fruit leathers were prepared out of the pulp of guava mixed with each of pomegranate, dates and watermelon along with sugar and pectin, totally three types of mixture of pulp in two variations with and without citric acid. The mixture of fruit pulp was smeared in to thin layers and dried at 60°C for 5 to 6 hours. Sensory evaluation was carried out for the storage life and the fruit leather with best acceptability was analysed for physiochemical and microbial analysis. Results showed that the sensory quality of fruit leathers with the significant amount of iron and vitamin C levels. Microbial analysis showed the absence of coliforms and presence of bacterial count was 20 cfu/gm, within the microbial limits of the product. FESEM showed the significant structural difference among types of fruit leathers along with the presence of Mg, K and Ca. EDAX and FTIR revealed the presence of functional components belonging to fruits added in fruit leathers. Hence fruit leathers with guava pulp mixed with pomegranate, dates and watermelon showed the significant amount of iron and vitamin C with the presence of antioxidative compounds.