Process Quality Index Research Articles

A Soft Sensor Model of Sintering Process Quality Index Based on Multi-Source Data Fusion

In complex industrial processes such as sintering, key quality variables are difficult to measure online and it takes a long time to obtain quality variables through offline testing. Moreover, due to the limitations of testing frequency, quality variable data are too scarce. To solve this problem, this paper proposes a sintering quality prediction model based on multi-source data fusion and introduces video data collected by industrial cameras. Firstly, video information of the end of the sintering machine is obtained via the keyframe extraction method based on the feature height. Secondly, using the shallow layer feature construction method based on sinter stratification and the deep layer feature extraction method based on ResNet, the feature information of the image is extracted at multi-scale of the deep layer and the shallow layer. Then, combining industrial time series data, a sintering quality soft sensor model based on multi-source data fusion is proposed, which makes full use of multi-source data from various sources. The experimental results show that the method effectively improves the accuracy of the sinter quality prediction model.

Quality of emergency obstetric and newborn care services in Wolaita Zone, Southern Ethiopia

BackgroundGlobally, nearly 295,000 women die every year during and following pregnancy and childbirth. Emergency obstetric and newborn care (EmONC) can avert 75% of maternal mortality if all mothers get quality healthcare. Improving maternal health needs identification and addressing of barriers that limit access to quality maternal health services. Hence, this study aimed to assess the quality of EmONC service and its predictors in Wolaita Zone, southern Ethiopia.MethodologyA facility-based cross-sectional study was conducted in 14 health facilities. A facility audit was conducted on 14 health facilities, and 423 women were randomly selected to participate in observation of care and exit interview. The Open Data Kit (ODK) platform and Stata version 17 were used for data entry and analysis, respectively. Frequencies and summary statistics were used to describe the study population. Simple and multiple linear regressions were done to identify candidate and predictor variables of service quality. Coefficients with 95% confidence intervals were used to declare the significance and strength of association. Input, process, and output quality indices were created by calculating the means of standard items available or actions performed by each category and were used to describe the quality of EmONC.ResultThe mean input, process, and output EmONC services qualities were 74.2, 69.4, and 79.6%, respectively. Of the study participants, 59.2% received below 75% of the standard clinical actions (observed quality) of EmONC services. Women’s educational status (B = 5.35, 95% C.I: 0.56, 10.14), and (B = 8.38, 95% C.I: 2.92, 13.85), age (B = 3.86, 95% C.I: 0.39, 7.33), duration of stay at the facility (B = 3.58, 95% C.I: 2.66, 4.9), number of patients in the delivery room (B = − 4.14, 95% C.I: − 6.14, − 2.13), and care provider’s experience (B = 1.26, 95% C.I: 0.83, 1.69) were independent predictors of observed service quality.ConclusionThe EmONC services quality was suboptimal in Wolaita Zone. Every three-in-five women received less than three-fourths of the standard clinical actions. The health system, care providers, and other stakeholders should emphasize improving the quality of care by availing medical infrastructure, adhering to standard procedures, enhancing human resources for health, and providing standard care regardless of women’s characteristics.

Dynamic Time Features Expanding and Extracting Method for Prediction Model of Sintering Process Quality Index

In complex industrial processes, it is difficult to measure the key quality variables online. It takes a long time to obtain quality variables through offline testing, which makes it difficult to get timely information to guide the production process. Therefore, this article proposes a novel dynamic time feature expanding and extracting framework for the sinter quality prediction. First, the original data are differentiated, compensated for time delay, expanded, and serialized by using time characteristics, and the input time series is reconstructed. Second, the integrated time features extractor is used to obtain the process information. Then, the recurrent neural network regression is applied to obtain the prediction of key quality variables. Finally, the effectiveness of the proposed method is verified by a numerical example, the actual data of sintering process and various comparative experiments, and the prediction effect of FeO content in sintering process is improved.

Design and evaluation of energy-saving total distillation separation of N,N-Dimethylacetamide/acetic acid/water

• Propose an evaluation index for the distillation process with multiple factors. • Propose three total distillation schemes for separating DMA/HAc/water. • Three heat integrated configurations further improve energy efficiency. Industrial wastewater containing N,N-Dimethylacetamide (DMA) and acetic acid (HAC) will be produced in the production of DMA by acetic acid method. The binary mixture of HAc and DMA has maximum-boiling azeotropic point, and the relative volatility of HAc and water is small. Therefore, high purity separation cannot be realized by traditional distillation. By analyzing the separation methods of binary mixture, three processes of extractive-pressure swing distillation, azeotropic-pressure swing distillation, and azeotropic-extractive distillation are proposed to separate DMA-HAc-water ternary mixtures. The heat integration enhancement configuration is completed according to the process characteristics. The results show that compared with the conventional processes, the TAC of the three heat integration processes is reduced by more than 25%, and the energy consumption is reduced by more than 40%. The process quality index (PQI), which integrates TAC, energy consumption, environment and thermodynamic efficiency, is proposed and applied to the evaluation of all processes. The results indicate that the heat integrated azeotropic-extractive distillation (HIAED) process shows more significant industrial application potential.

Interpretation of Rehabilitation Medicine Specialty Indicators in National Report on the Services， Quality and Safety in Medical Care System

Since 2016, Peking University Third hospital has been entrusted by the National Health Commission to undertake the work of National Center for Healthcare Quality Management in Rehabilitation Medicine. For five consecutive years, it has compiled the National Report on the Services, Quality and Safety in Medical Care System (the section of rehabilitation medicine) (hereinafter referred to as the report), making up the gap in relevant data of rehabilitation medicine in China. This paper aims to interpret the indicators extracted and analyzed in the report, in order to continuously improve the integrity rate and accuracy rate of rehabilitation medical service and quality and safety investigation, and promote the continuous improvement of the quality of rehabilitation medical care in China. The index is composed of two kinds of indexes: quality control index and resource consumption index. The quality control index includes structure index, process quality index and result quality index, and consists of 16 major items and 149 sub-items. The report is carried out by the National Clinical Improvement System (NCIS), and the quality, accuracy and completeness of the data report will be used as the reference for hospital evaluation and key specialty setting. In NCIS indicators, data collection can be divided into the following four forms: automatic extraction of home page, extraction of medical records, manual reading of medical records and provision of relevant departments. This paper focuses on the early rehabilitation intervention, ADL improvement rate, complications and other key indicators, structural indicators and other process quality indicators. In recent years, National Center for Healthcare Quality Management in Rehabilitation Medicine has found that whether the rehabilitation medicine department has established an information system is an important factor determining the accuracy and convenience of quality control data extraction. Rehabilitation medical treatment and quality control information system is changing from information to intelligent, information management will combine with artificial intelligence natural language analysis and other emerging technologies, it can simplify and accurately the extraction process of quality control data, constantly improve the efficiency of rehabilitation medical quality control.

Fuzzy Quality Evaluation Model Constructed by Process Quality Index

Many studies have pointed out that the-smaller-the-better quality characteristics (QC) can be found in many important components of machine tools, such as roundness, verticality, and surface roughness of axes, bearings, and gears. This paper applied a process quality index that is capable of measuring the level of process quality. Meanwhile, a model of fuzzy quality evaluation was developed by the process quality index as having a one-to-one mathematical relationship with the process yield. In addition to assessing the level of process quality, the model can also be employed as a basis for determining whether to improve the process quality at the same time. This model can cope with the problem of small sample sizes arising from the need for enterprises’ quick response, which means that the accuracy of the evaluation can still be maintained in the case of small sample sizes. Moreover, this fuzzy quality evaluation model is built on the confidence interval, enabling a decline in the probability of misjudgment incurred by sampling errors.

A Fuzzy Evaluation Model Aimed at Smaller-the-Better-Type Quality Characteristics

Numerous key components of tool machines possess critical smaller-the-better-type quality characteristics. Under the assumption of normality, a one-to-one mathematical relationship exists between the process quality index and the process yield. Therefore, this paper utilized the index to produce a quality fuzzy evaluation model aimed at the small-the-better-type quality characteristics and adopted the model as a decision-making basis for improvement. First, we derived the 100(1 −α)% confidence region of the process mean and process standard deviation. Next, we obtained the 100(1 −α)% confidence interval of the quality index using the mathematical programming method. Furthermore, a one-tailed fuzzy testing method based on this confidence interval was proposed, aiming to assess the process quality. In addition, enterprises’ pursuit of rapid response often results in small sample sizes. Since the evaluation model is built on the basis of the confidence interval, not only can it diminish the risk of wrong judgment due to sampling errors, but it also can enhance the accuracy of evaluations for small sample sizes.

Process quality indices: new metrics for process quality capability with zero-loss baseline

The traditional process capability indices Cp /Cpk for evaluating process capability used in the current industry are based on the probability model of a process distribution, which makes it difficult for many processes whose actual output with non-normal distribution to apply these indices. In this paper, new process quality indices (PQI) Qp /Qpk are proposed for measuring the quality capability of the process. The new PQI are derived from Taguchi's quality loss function model and do not need to estimate the probability distribution of the process output. The traditional process capability indices Cp /Cpk are zero-defect indices, it focus on meeting the process specification requirements, while the new PQI Qp /Qpk are zero-loss indices, it focus on approaching the process target value. The construction of the new PQI is logical, simple, and practical. The PQI approach is more suitable for quality management in the Era of big data.

Improving of preventive management for flat rolling products quality indices

Based on the combination of ISO and Lean system tools, the methodology of continuous optimization of technology and quality improvement of rolled steel has been improved. To assess the condition and determine the production level to set limits to process indices the use of colored markers was suggested. The improved flat rolling quality control methodology is based on the use of Deming cycle and includes the use of such analysis tools as Fishbone (Ishikawa) diagram, Pareto principle, distribution diagrams, 3σ rules and Shewhart control charts. For the first time, differentiated corrective solutions for plate steel which is produced with different levels of process stability and quality indices are suggested. The inspection was carried out with the data from the mechanical properties of flat steel products 14 mm thick from K60 steel, produced by thermo-mechanical controlled process on a plate mill 3600. Introduction of the methodology allowed to reduce the standard deviation of yield strength, tensile strength and percentage elongation after fracture of the studied rolled products by 44%, 31% and 46%, respectively, indicating an overall increase in technology stability and quality indices and implementation of an effective tool for their preventive control.

Dual dimensional fuzzy testing based on upper confidence limits for supplier selection

Many corporations purchase components from suppliers, which can reduce operating costs and enable firms to focus their resources on core advantages. Studies have indicated that process quality and manufacturing time performance are two crucial indicators for supplier selection. We used the process quality index and a manufacturing time performance index to create a dual dimensional fuzzy supplier selection model. First, the upper confidence limits of these two indices were derived, and a fuzzy membership function based on these limits was constructed. Based on the fuzzy test rules for process quality and manufacturing time performance, we divided the fuzzy supplier selection matrix into nine evaluation zones. Using the upper confidence limits of these two indices, we created evaluation coordinates and assigned weights based on the location of the coordinates. Then, the total of all the weights was employed to form a supplier selection index for which a higher value means a higher ranking. The use of confidence limits decreased the chance of misjudgment resulting from sampling errors while the fuzzy test rules increased the applicability of the model. Consequently, the proposed model can be used to select suppliers efficiently so as to form partnerships in which corporations and suppliers can grow together.

Two-Tailed Fuzzy Hypothesis Testing for Unilateral Specification Process Quality Index

The quality characteristics with unilateral specifications include the smaller-the-better (STB) and larger-the-better (LTB) quality characteristics. Roundness, verticality, and concentricity are categorized into the STB quality characteristics, while the wire pull and the ball shear of gold wire bonding are categorized into the LTB quality characteristics. In terms of the tolerance, zero and infinity (∞) can be viewed as the target values in line with the STB and LTB quality characteristics, respectively. However, cost and timeliness considerations, or the restrictions of practical technical capabilities in the industry, mean that the process mean is generally far more than 1.5 standard deviations away from the target value. Researchers have accordingly proposed a process quality index conforming to the STB quality characteristics. In this study, we come up with a process quality index conforming to the LTB quality characteristics. We refer to these two types of indices as the unilateral specification process quality indices. These indices and the process yield have a one-to-one mathematical relationship. Besides, the process quality levels can be completely reflected as well. These indices possess unknown parameters. Therefore, sample data are required for calculation. Nevertheless, interval estimates can lower the misjudgment risk resulting from sampling errors more than point estimates can. In addition, considering cost and timeliness in the industry, samples are generally small, which lowers estimation accuracy. In an attempt to increase the accuracy of estimation as well as overcome the uncertainty of measured data, we first derive the confidence interval for unilateral specification process quality indices, and then propose a fuzzy membership function on the basis of the confidence interval to establish the two-tailed fuzzy testing rules for a single indicator. Lastly, we determine whether the process quality has improved.

Multi-objective Optimization of Injection Process Parameters Based on EBFNN and NSGA-II

Taking the process parameters and quality index data obtained from the optimal Latin hypercube test of a front hood inner panel as sample data, this paper constructs an EBFNN approximate model by combining the NSGA-II multi-objective optimization algorithm to perform multi-objective optimization of injection molding process parameters, obtained Pareto solution set for volume shrinkage and warp during ejection. Comparing the Moldflow numerical simulation results of the four relatively optimized Pareto solutions, the optimal combination of process parameters is obtained. The optimized volume shrinkage and warp deformation reduced by 50.518% and 39.845% respectively, which verified the reliability and practicability of this method, and had certain practical reference significance for the optimization of injection molding process of other plastic parts.

Process-Quality Evaluation for Wire Bonding With Multiple Gold Wires

Taiwan is a world-leader in wafer foundry services and IC packaging and testing. Wire bonding is a crucial process in the overall IC-packaging industry chain. Thus, this paper proposes a process-quality evaluation model for wire bonding with multiple gold wires. We chose process quality indices as a tool of evaluation fully mirroring process yield and quality levels. These indices contain unknown parameters and thus require sample data to estimate. We first derived the uniformly minimum variance unbiased estimator of the indices and calculated the upper confidence limits of the indices based on DeMorgan's theorem and Boole's inequality. The upper confidence limits of the indices were then employed to create a confidence interval-based fuzzy membership function, in order to improve the accuracy of estimation as well as solve the problem of uncertainty of the measured data. Next, we obtained the fuzzy critical value and used index estimates and the fuzzy critical value to establish fuzzy test rules. Next, we marked the fuzzy critical value on the axes of a radar chart, which is a visualization evaluation tool, and connected neighboring critical points to create a critical region in the form of a regular polygon. The observed values of the indices were then marked on the axes to produce a visualized fuzzy radar evaluation chart. This fuzzy radar evaluation chart has a solid foundation in statistical inference, and evaluation rules were established using precise fuzzy test methods. Not only is this fuzzy radar evaluation chart easy to use, but it also reduces the chance of misinterpretations made by sampling errors, so that the accuracy of evaluation can be enhanced.

Fuzzy testing of operating performance index based on confidence intervals

The operating performance index (OPI) was developed by Chen and Yang (J Comput Appl Math 343:737–747, 2018) from the Six Sigma process quality index. The fact that OPIs include unknown parameters means that they must be formulated using estimates based on sample data. Unfortunately, cost and effectiveness considerations in practice have led to sample size limitation and measurement uncertainty. In this study, we sought to enhance testing accuracy and overcome the uncertainties in measurement by applying confidence intervals of OPI to derive a fuzzy number and membership function for OPI. We developed a one-tailed fuzzy test method to determine whether performance reaches the required level. We also developed a two-tailed fuzzy testing method based on two OPIs to serve as a verification model for the effectiveness of improvement measures. Both fuzzy testing methods are proposed based on confidence intervals of the indices to reduce the risk of misjudgment caused by sampling errors and enhance testing accuracy.

Two-tailed Buckley fuzzy testing for operating performance index

This paper proposes an operating performance index (OPI) based directly on the Six Sigma process quality index. We considered the following case: the number of customers arriving at a store follows a Poisson process of rate λ and the sequence of inter-arrival times is an exponential random variable with mean τ , where τ = 1/ λ . In operating performance assessments, using the statistical testing to evaluate the OPI can lower the risk of misjudgment caused by sampling error. A smaller sample size entails a larger sampling error, while a larger sample size entails a smaller sampling error. Thus, using critical value for statistical testing can result in inconsistencies for different sample size. We therefore referred to the one-tailed fuzzy method proposed by Buckley (2005) for the parameters of population distributions and developed a two-tailed fuzzy test for the OPI to assess operating performance. The advantage of this approach is that conventional measurement methods can be used during data collection, and then the confidence interval of the OPI can be used to construct a fuzzy membership function for fuzzy testing. This not only lowers the risk of misjudgment caused by sampling error, but also enhances testing accuracy.

Measurement of healthcare quality in atopic dermatitis - development and application of a set of quality indicators.

Quality indicators are essential tools for the assessment of health care, in particular for guideline-based procedures. Development of a set of indicators for the evaluation of process and outcomes quality in atopic dermatitis (AD) care. Application of the indicators to a cross-sectional study and creation of a global process quality index. An expert committee consisting of 10 members of the German guideline group on AD condensed potential quality indicators to a final set of 5 outcomes quality and 12 process quality indicators using a Delphi panel. The outcomes quality and 7 resp. 8 process quality indicators were retrospectively applied to a nationwide study on 1678 patients with atopic dermatitis (AtopicHealth). Each individual process quality indicator score was then summed up to a global index (ranges from 0 [no quality achieved] to 100 [full quality achieved]) displaying the quality of health care. In total, the global process quality index revealed a median value of 62.5 and did not or only slightly correlate to outcomes quality indicators like the median SCORing Atopic Dermatitis (SCORAD; rp = 0.08), Dermatology Life Quality Index (DLQI; rp = 0.256) and Patient Benefit Index (PBI; rp = -0.151). Process quality of AD care is moderate to good. The healthcare process quality index does not substantially correlate to the health status of AD patients measured by 5 different outcomes quality indicators. Further research should include the investigation of reliability, responsiveness and feasibility of the proposed quality indicators for AD.

A Modified Dynamic PLS for Quality Related Monitoring of Fractionation Processes

The fractionation process is a typical dynamic process, and practitioners highly pay attention to the quality-related abnormal in the real refining processes. In this paper, a modified dynamic PLS (MDPLS) modeling method and the corresponding process monitoring strategy are proposed. The main contributions of the proposed method are in the following. First, a clear dynamic relation is captured between process data and quality indices. Moreover, the process and quality space are comprehensively divided into dynamic quality-related subspace, static quality-unrelated subspace as well as the residual space for improving the performance of monitoring. Finally, the effectiveness of the proposed algorithm is demonstrated with the data from a real fractionation process.

Process Quality Assessment Model of Hand Tools: A Case Study on the Handle of Ratchet Torque Wrench

The growing popularity of do-it-yourself (DIY) projects has increased the penetration of hand tools devices into the market. This paper thus conducts a case study on ratchet torque wrenches and develops an assessment and verification model of process quality as a means of assisting hand tool manufacturers in managing the quality of ratchet torque wrench products and promoting the on-going development in the hand tool industry. We first propose the process quality index (PQI) that can directly determine a precise level of quality based on the relationship between various process capability indices and Six Sigma quality levels. Using inference, we further develop a testing model of PQI to evaluate the quality level reached of process performance. The present case study is investigated to illustrate the practical applicability of the proposed method. We believe that the proposed model will facilitate the production of high-value products and thereby enhance the competitive advantage of firms in Taiwan.

Development and Standardization of Quality-oriented Statistical Tolerancing in China

The development of the quality-oriented statistical tolerancing (ST) technique and its standardization in China are briefly introduced in this paper. Based on the new development in the academic area of geometrical product specifications (GPS) and statistical process control (SPC), it is out that the ST technique must be innovated and improved to face the challenges of the modern manufacturing environment. The principle of quality-oriented ST and the mathematical models are given. A set of standardized process quality indices (PQIs) for evaluating the nonconforming of process, process centring and quality loss has been presented. This evaluation system based on PQIs and the standardized two-dimensional interface relative to the process mean and the standard deviation of process has relatively strong compatibility and adaptability with the process capability analysis and SPC that are widely applied in the modern manufacturing environment. The standardized PQIs may also be used as a part of population specifications. The serial ST standards published by the standardization committee of China and their main applications with a case study are introduced, and are expected to play important roles for assuring the designated dimensional specifications and population specifications with preset confidence interval.

Experimental Analysis of the Process Performance and Quality Index in the Waste Gas Recovery Iron Ore Sintering—Based on a Plant Test

Effects of different waste gas recovery sintering (WGRS) methods on the quality index and process performance of the iron ore sinter were studied by a plant test, compared to the conventional sintering (CS). Two key operational parameters, namely, the temperature and oxygen content of the recycling gas, were selectively discussed. The values of these two parameters were measured in the gas‐mixing chamber. The sintering mixture with a constant proportion was obtained ensuring the similar mass averaged particle sizes. The changes in the sintering process performance, such as the exhaust emission regulation, productivity, energy consumption, and sintering efficiency of the system, were analyzed. The quality indexes including the yield, chemical composition and size distribution of the sinter cake, the tumbler index (TI), and the reducibility (RI) in different methods of WGRS were also analyzed. Systematic analyses demonstrate the feasibility of replacing the CS production with the WGRS technology. The optimal temperature range of the recycling gas was 200 °C∼250 °C, and the suggested values of oxygen content was closed to 21%.