Quality Engineering最新文献

{"title":"System reliability optimization with two-sided power distributed component failure times","authors":"B. Maneckshaw, Kash Barker, G. S. Mahapatra","doi":"10.1080/08982112.2023.2222324","DOIUrl":"https://doi.org/10.1080/08982112.2023.2222324","url":null,"abstract":"AbstractMany systems connected in series are subject to failure earlier in their design life. The failure rate of such systems has been described by a two-sided power distribution, which enables modeling the entirety of the bathtub curve failure modes with a single distribution. We propose a time-dependent reliability optimization model for improving a system’s reliability through the optimal allocation of redundant components whose failure distribution follows a two-sided power distribution. To solve the multi-criteria optimization model, we propose an algorithm that (i) identifies the scale tolerance at which a selected component will have a higher mean residual life, (ii) identifies the level of redundancy for enhancing system reliability, and (iii) identifies other optimal system characteristics, including minimized cost and minimized volume.HighlightsTwo sided power (TSP) distribution with four parameters (a, b, c, η) models time-to-failure.We consider an n-stage series system and assign redundancy within the stages.We propose a multi-objective optimization problem to assign redundancy, accounting for reliability, cost, and system volume.An illustrative example is solved using the NSGA-II algorithm.Keywords: two-sided power distributionsystem reliabilityredundancy allocationtime-dependent reliabilitymean residual life AcknowledgementWe are grateful to Editors, and anonymous referees for their valuable comments and helpful suggestions, which have helped us to improve this work significantly.Additional informationNotes on contributorsB. ManeckshawManeckshaw Balakrishnan is a research scholar in the Department of Mathematics at National Institute of Technology, Puducherry in India. He is also working as Lecturer in Mathematics and attached to institutions run by Pondicherry Institute of Post Matric Technical Education (PIPMATE), as well as at Indira Gandhi Polytechnic College, Mahe. He completed his M.S. at St. Joseph's College (Autonomous) in Thiruchirapalli, India and an M.Phil. in Graph Theory at Pondicherry Central University. He is presently pursuing research in reliability engineering.Kash BarkerDr. Kash Barker is the John A. Myers Professor and a David L. Boren Professor in the School of Industrial and Systems Engineering at the University of Oklahoma. He earned B.S. and M.S. degrees in industrial engineering from the University of Oklahoma and a Ph.D. in systems engineering from the University of Virginia. His primary research interests lie in the reliability, resilience, and economic impacts of infrastructure and community networks.G. S. MahapatraDr. G.S. Mahapatra holds M.Sc. and Ph.D. degrees in Applied Mathematics from Indian Institute of Engineering Science and Technology, Shibpur, India. He is an Associate Professor in the Department of Mathematics in National Institute of Technology, Puducherry, India. He has been involved in teaching and research for more than 17 years. He has published more than hundred research papers in topic","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135883978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal design of step-stress accelerated degradation tests based on genetic algorithm and neural network","authors":"Gen Liu, Zhihua Wang, Rui Bao, Zelong Mao, Kunpeng Ren","doi":"10.1080/08982112.2023.2225583","DOIUrl":"https://doi.org/10.1080/08982112.2023.2225583","url":null,"abstract":"AbstractIn this study, the optimal design of step-stress accelerated degradation tests is focused. An optimization model is proposed where an improved accelerated degradation model is involved to comprehensively consider the influence of accelerated stress and the measurement error. Then, a novel optimal design method is constructed, where multiple decision variables can be simultaneously optimized based on neural network and genetic algorithm. An effective sensitivity analysis method is further established to quantitively illustrate the influence of the predetermined model parameters on the optimal results. Finally, a case study is implemented, and a series of comparisons are implemented to demonstrate the effectiveness and rationality of the proposed method.Keywords: genetic algorithmmultiple decision variablesproxy modeloptimal designstep-stress accelerated degradation test AcknowledgmentThe authors are grateful to the editor and the anonymous reviewers for their critical and constructive review of the manuscript.Additional informationFundingThis study was supported by the National Natural Science Foundation of China (Grant No. 11872085).Notes on contributorsGen LiuGen Liu is currently a PhD candidate at School of Aeronautics Sciences and Engineering, Beihang University (Beijing, China). His research interests are optimal design of accelerated degradation tests and reliability evaluation of small sample life test.Zhihua WangZhihua Wang received the B.S. degree in mechanical engineering from the Dalian University of Technology, Dalian, China, and the Ph.D. degree in mechanical engineering from Beihang University, Beijing, China. She is currently an Associate Professor with the School of Aeronautics Sciences and Engineering, Beihang University. Her research interests include degradation modeling, life test optimal design, and small sample reliability assessment via multi-source information fusion.Rui BaoRui Bao is currently a Full Professor in structural integrity and durability in Solid Mechanics. Her teaching duties include under-graduate and graduate courses in material mechanics, fatigue reliability and structural fatigue life evaluation methods, and providing supervision to MSc and Ph.D students.Zelong MaoZelong Mao received the master degree from the Beijing University of Aeronautics and Astronautics, Beijing, China, in 2021. He is currently an Engineer with the Tianjin Navigation Instrument Research Institute, Tianjin, China. His research interests include electric component quality control and reliability design of ship equipment.Kunpeng RenKunpeng Ren received the master degree from the Beijing University of Aeronautics and Astronautics, Beijing, China, in 2013. He is currently a Senior Engineer with the Tianjin Navigation Instrument Research Institute, Tianjin, China. His research interests include electric component failure analysis, electric component quality control and accelerated life test design of ship equipment.","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136079237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remaining useful life prediction using the similarity-based integrations of multi-sensors data","authors":"Mohammad Baharshahi, S. Mohammad Seyedhosseini, Shah M Limon","doi":"10.1080/08982112.2023.2218923","DOIUrl":"https://doi.org/10.1080/08982112.2023.2218923","url":null,"abstract":"AbstractIn prognostics and health management, the system’s degradation condition assessment and corresponding remaining useful life prediction are the most important tasks. Both of these processes are heavily dependent on information gathered by multiple sensors, which eventually causes data fusion-related complex problems. Typically, sensor information contains the speed, pressure, temperature, and similar other types of various system data. These systems’ data obtained through sensors can be utilized as a part of the evidence in the evidence-based estimation method. In this work, an artificial intelligence-based novel framework for estimating the remaining useful life using data fusion has been presented. The Dempster–Shafer extended theory is adopted for sensor information modeling and data fusion. Besides, two different scenarios are introduced to determine the similarity between the studied system and the available evidence. As a case study, the turbofan dataset is demonstrated to assess the proposed method. Based on the results, our integrated proposed method performs very competitively compared with the existing methods based on standard scores and performance criteria.Keywords: Artificial neural networksDempster–Shafer theoryinformation integrationK-means clusteringprognostics & health managementremaining useful lifesensor data Additional informationNotes on contributorsMohammad BaharshahiMohammad Baharshahi is a PhD candidate in the Department of Industrial Engineering at Iran University of Science and Technology. His research interest is condition based maintenence, reliability centered maintenance and data mining.S. Mohammad SeyedhosseiniS. Mohammad Seyedhosseini is a professor in Department of Industrial Engineering at Iran University of Science and Technology. His focuses on maintenance planning, production management and supply chain management.Shah M LimonShah M Limon is an Assistant Professor of Industrial & System Engineering at Slippery Rock University of Pennsylvania, USA. He received his M.Sc. and Ph.D. from the Department of Industrial and Manufacturing Engineering at North Dakota State University, Fargo, USA, and B.Sc. degree in Industrial and Production Engineering from Bangladesh University of Engineering & Technology, Dhaka, Bangladesh. His research interest includes but is not limited to reliability-based design, accelerated product testing, stochastic modeling, prognostics with machine learning, network reliability optimization, additive manufacturing, and lean process improvement. Shah’s research work has been published in Quality Technology and Quantitative Management, Quality & Reliability Engineering International, Quality Engineering, Journal of Risk & Reliability, International Journal of Quality & Reliability Management, and International Journal of Quality Engineering and Technology. He is a member of IISE.","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136114736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal test design for reliability demonstration under multi-stage acceptance uncertainties","authors":"Bingjie Wang, Lu Lu, Suiyao Chen, Mingyang Li","doi":"10.1080/08982112.2023.2249188","DOIUrl":"https://doi.org/10.1080/08982112.2023.2249188","url":null,"abstract":"AbstractA reliability demonstration test (RDT) plays a critical role in safeguarding product reliability and making sure it meets the target requirement. When planning an RDT, the test planning parameters are determined before executing the RDT. There is uncertainty associated with the test result and whether the product will be acceptable and released into the market with additional costs resulting from the warranty service or whether a reliability growth process is needed to further improve the product’s reliability. Potentially, such a process could be repeated multiple times depending on how quickly the reliability growth process can improve product reliability. Existing RDT designs primarily consider the cost of RDT itself or over a single demonstration stage before the next possible RDT, and hence fail to fully address the uncertainty of all possible future RDTs and various pathways a product may go through in a multi-stage demonstration process. By focusing on binomial RDT (BRDT) plans based on failure count data, this paper proposes an optimal Bayesian BRDT design framework by explicitly quantifying the multi-stage acceptance uncertainties resulting from current and subsequent BRDTs. It allows the BRDT planning decision to be determined more holistically by anticipating the costs of warranty service and reliability growth along different pathways over multiple stages. A recursive information propagation algorithm is proposed to incorporate the prior belief of product reliability and allow it to evolve and update over multiple stages of BRDT. A case study is presented to illustrate the proposed multi-stage Bayesian BRDT design framework and demonstrate its cost-efficiency compared to existing strategies. A comprehensive sensitivity analysis is also provided to demonstrate the impact of the relative size of different cost components, reliability growth rate, and prior setting on the performance of the proposed method.Keywords: bayesian reliabilityinformation propagationmulti-stage uncertaintiesoptimal test designreliability demonstration test Additional informationNotes on contributorsBingjie WangBingjie Wang is a PhD student in the Department of Industrial & Management Systems Engineering at the University of South Florida. She received her MS in Industrial Engineering from the State University of New York at Buffalo. Her research interests include decision science, data science and AI techniques.Lu LuLu Lu is an Associate Professor of Statistics in the Department of Mathematics and Statistics at the University of South Florida. She was a postdoctoral research associated in the Statistics Sciences Group at Los Alamos National Laboratory. She earned a doctorate in Statistics from Iowa State University. Her research interests include reliability analysis, design of experiments, response surface methodology, survey sampling, multiple objective/response optimization. She is a member of the American Statistical Association and the American Soci","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135855225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust confidence intervals for the process capability index <i> C _pk </i> with bootstrap improvement","authors":"Linhan Ouyang, Sanku Dey, Chanseok Park","doi":"10.1080/08982112.2023.2263523","DOIUrl":"https://doi.org/10.1080/08982112.2023.2263523","url":null,"abstract":"AbstractThe process capability index (PCI), Cpk, one of the widely used tools for assessing the capability of a manufacturing process, expresses the deviation of the process mean from the midpoint of the specification limits. The Cpk is known to perform well under the general assumption that the experimental data are normally distributed without contamination. Under this assumption, the sample mean and sample standard deviation are used for the estimation of the PCI. However, the sample mean and sample standard deviation are quite sensitive to data contamination and this will result in underperformance of Cpk. Therefore, in this article, we propose alternatives to the conventional method by replacing the sample mean and sample standard deviation with robust location and scale estimators. We also propose a method for constructing a robust PCI Cpk confidence interval which lends itself to robust statistical hypothesis testing. The robust hypothesis testing methods based on this confidence interval are shown to be quite efficient when the data are normally distributed yet also outperform the conventional method when data contamination exists.Keywords: bootstrapconfidence intervalprocess capability indexrobustnessROC AcknowledgmentsThe authors are grateful to the anonymous referees for their helpful comments and suggestions, particularly for enhancing the concluding remarks.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work of Dr. Ouyang was supported by the National Natural Science Foundation of China (No. 72072089) and the Fundamental Research Funds for the Central Universities (Grant NE2023004). The work of Professor Park was supported by the National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) (Nos. 2022R1A2C1091319 and RS-2023-00242528).Notes on contributorsLinhan OuyangLinhan Ouyang is an associate professor in the College of Economics and Management at Nanjing University of Aeronautics and Astronautics, China. He holds a BEng degree in industrial engineering from Nanchang University, P.R. China, and a PhD degree in management science and engineering from Nanjing University of Science and Technology, P.R. China. His research interests are process modeling and design of experiments.Sanku DeySanku Dey is currently working as an associate professor in the Department of Statistics, St. Anthony’s College, Shillong, Meghalaya, India. He did his MSc in Statistics in the year of 1991 from Gauhati University, Guwahati, India and PhD in Statistics (reliability theory) in the year 1998 from the same university. He has published more than 270 research articles in journals of repute. He is an associate editor of American Journal of Mathematical and Management Sciences and also the member of editorial board of several journals of repute. He is a researcher and has a good number of contributions in almost all fields of Statistics viz., distribution theo","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135347708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Alternative parameter learning schemes for monitoring process stability","authors":"Daniele Zago, Giovanna Capizzi","doi":"10.1080/08982112.2023.2253891","DOIUrl":"https://doi.org/10.1080/08982112.2023.2253891","url":null,"abstract":"AbstractIn statistical process control, accurately estimating in-control (IC) parameters is crucial for effective monitoring. This typically requires a Phase I analysis to obtain estimates before monitoring commences. The traditional “fixed” estimate (FE) approach uses these estimates exclusively, while the “adaptive” estimate (AE) approach updates the estimates with each new observation. Such extreme criteria reflect the traditional bias-variance tradeoff in the framework of the sequential parameter learning schemes. This paper proposes an intermediate update rule that generalizes two ad hoc criteria for monitoring univariate Gaussian data, by giving a lower probability to parameter updates when an out-of-control (OC) situation is likely, therefore updating more frequently when there is no evidence of an OC scenario. The simulation study shows that this approach improves the detection power for small and early shifts, which are commonly regarded as a weakness of control charts based on fully online adaptive estimation. The paper also shows that the proposed method performs similarly to the fully adaptive procedure for larger or later shifts. The proposed method is illustrated by monitoring the sudden increase in ICU counts during the 2020 COVID outbreak in New York.Keywords: Adaptive estimationwindow of opportunityestimation effectsstatistical process control AcknowledgmentsThe authors thank the editor and the reviewers for their constructive comments and suggestions, which improved the quality of the paper.Disclosure statementThe authors report there are no competing interests to declare.Additional informationFundingThis work was supported by UNIPD under Grant DOR2021.Notes on contributorsDaniele ZagoDaniele Zago is a current Ph.D. student in Statistics at the University of Padua since 2021. He obtained his bachelor's degree in Statistics for Technology and Science and his master's degrees in Statistical Sciences from the University of Padua. His main research interests revolve around fundamental issues in practical applications of statistical process control and optimization.Giovanna CapizziDr. Giovanna Capizzi is a full Professor of Statistics at the University of Padua. She earned her Ph.D. in Statistics from the University of Padua in 1992. Dr. Capizzi's main research interest is in statistical process monitoring, and she has made significant contributions to the field. she has published extensively in several international peer-reviewed journals, including Statistics and Computing, Technometrics, Journal of Quality Technology, IIE Transactions, and Quality Engineering. Dr. Capizzi serves as an associate editor of Technometrics since 2013, and she is a member of the editorial board of the Journal of Quality Technology since 2014.","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136307467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-branch convolutional attention network for multi-sensor feature fusion in intelligent fault diagnosis of rotating machinery","authors":"Ke Wu, Zirui Li, Chong Chen, Zhenguo Song, Jun Wu","doi":"10.1080/08982112.2023.2257762","DOIUrl":"https://doi.org/10.1080/08982112.2023.2257762","url":null,"abstract":"AbstractMulti-sensor data fusion approaches based on deep learning are widely used for fault diagnosis of rotating machinery. Massive sensor data bring not only abundant information but also technical challenges for the fault diagnosis. The main challenge is how to discriminate the discrepancies between multi-sensor data and efficiently fuze these sensor data to improve diagnostic performance. To overcome the challenge, a novel multi-branch convolutional attention network (MBCAN) is proposed for fault diagnosis of the rotating machinery. In this method, a feature extractor with attention mechanism is established to extract fault-related features from the sensor data and reduce irrelevant noise interference hidden in the sensor data. Meanwhile, a multi-branch convolutional pathway is designed to enrich the features. Furthermore, a feature fusion module is constructed by defining an adaptive weighted fusion rule to fuze the extracted features. The performance of the MBCAN is verified on gearbox and centrifugal pump under different noise environments. The experimental results show that the proposed MBCAN has more excellent anti-noise ability and reliable diagnostic performance than other existing approaches.Keywords: convolutional neural networkfault diagnosisfeature fusionrotating machinery Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported in part by the National Natural Science Foundation of China under Grant No. 51875225, in part by the Ministry of Industry and Information Technology of China under Grant No. TC210804R-1, and in part by Hubei Provincial Natural Science Foundation for Innovation Groups under Grant No. 2021CFA026.Notes on contributorsKe WuKe Wu received his B.S. and M.S. degrees in mechanical engineering from Hunan University of Science and Technology, in 2017 and 2020, respectively. He is currently a Ph.D candidate at marine engineering with the School of Naval Architecture and Ocean Engineering from Huazhong University of Science and Technology, China. His main research interests include big data analytics, health monitoring and fault diagnosis for equipment.Zirui LiZirui Li received the B.S. degree in marine engineering from the Huazhong University of Science and Technology (HUST), China, in 2020, where he is currently pursuing the master’s degree with the School of Naval Architecture and Ocean Engineering at HUST. His main research interests include health monitoring for equipment, deep learning and reinforcement learning.Chong ChenChong Chen received his B.S. degree in Nuclear science and technology from Harbin Engineering University, China, in 2011, and received his M.S. degrees in Nuclear science and technology from Harbin Engineering University, in 2013. His Ph.D degree in Nuclear science and technology with the school of Fundamental Science on Nuclear Safety and Simulation Technology Laboratory from Harbin Engineering University. His main","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135059153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian analysis of accelerated life test under constrained randomization","authors":"Shanshan Lv, Fan Li, Guodong Wang, Sen Li","doi":"10.1080/08982112.2023.2255899","DOIUrl":"https://doi.org/10.1080/08982112.2023.2255899","url":null,"abstract":"AbstractReliability engineers typically prioritize the lower percentiles. Accurately assessing these lower percentiles enables engineers to delve deeper into early product failures, paving the way for enhanced product reliability. In the manufacturing realm, many accelerated life tests (ALTs) veer away from completely randomized designs (CRDs) due to constraints in time and budget. Within ALTs, alterations in stress can lead to shifts in the failure mechanism of products. To accurately discern product lifetime percentiles, there is an imperative need to account for these varying failure mechanisms and random effects. Our approach introduces a re-parameterization model encapsulating random effects and disparate failure mechanisms. In this model, a specific percentile is employed as a stand-in for the scale parameter, laying the groundwork for a regression model interlinking the percentile, acceleration stress, and random effect. Concurrently, a separate regression model is designed for shape parameters in relation to acceleration stresses. Leveraging the Bayesian method, we ascertain the estimated values for the model parameters. The model is applied to an ALT example focusing on glass capacitors. The simulations underline the model’s prowess in delivering a more precise estimation of lower lifetime percentiles. Additionally, the Bayesian method further refines the accuracy of the lifetime percentile estimations.Keywords: Accelerated life testre-parameterization modelrandom effectsnonconstant shape parametersWeibull distribution Additional informationFundingThis work was supported by the National Natural Science Foundation of China under Grants [numbers 72002066, 71871204, and 71902138]; the Humanity and Social Science Youth Foundation of Ministry of Education of China under Grant [number 19YJC630181].Notes on contributorsShanshan LvGuodong Wang is an associate professor in the Department of management engineering at Zhengzhou University of Aeronautics, Zhengzhou, China. He received his BS Applied Mathematics from Nanchang University of Aeronautics, Nanchang, China, an MS degree in Reliability Engineering from Beihang University, Beijing, China, and a PhD in Quality Engineering from Tianjin University, Tianjin, China. His research interests focus on design of experiments and reliability improvement.Fan LiShanshan Lv is a lecturer in the School of Economics and Management at Hebei University of Technology. She received her B.S. degree from Zhengzhou University in 2012, M.S. and Ph.D. degree from Tianjin University, Tianjin, China, 2018, respectively. Her research interests include design of experiments, reliability analysis and improvement, and multi-response optimization.Guodong WangFan Li received the B.S. degree in economics from Hebei University of Science and Technology in 2020. She is currently a master degree candidate in the School of Economics and Management at the Hebei University of Technology, Tianjin. Her fields of interest are quality","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135149742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal diagnosis interval for online quality control methods","authors":"None Sandeep, Arup Ranjan Mukhopadhyay","doi":"10.1080/08982112.2023.2256372","DOIUrl":"https://doi.org/10.1080/08982112.2023.2256372","url":null,"abstract":"AbstractOnline quality control methods emphasize manufacturing processes to attain maximum conformance with respect to the specifications of the concerned quality characteristics of a product. One key factor that affects the effectiveness of these methods is the diagnosis interval. In this paper, the existing cost model along with its cost components for online quality control methods has been revisited and modified by incorporating new variables like the rate of production, the loss due to false alarm, the loss due to non-detection of process abnormalities, and considering a workable break-up of diagnosis cost for finding the optimal diagnosis interval from the perspective of present-day manufacturing engineering. As already mentioned, the proposed cost model has not ignored the loss due to the generation of defective items as well as the adjustment cost available in the pertinent literature. The modified cost function thus proposed has been appropriately minimized to obtain the corresponding optimal diagnosis interval. The proposed methodology has been compared numerically with other methodologies to establish its effectiveness. The cornerstone of the proposed methodology lies in reinforcing its effectiveness through a real-life case example in manufacturing. Sensitivity analysis has also been carried out for the real-life case example to fortify the proposed methodology.Keywords: Optimal diagnosis intervalloss functiontotal costadjustment costdiagnosis costtime lag AcknowledgmentsThe authors would like to appreciate the editor and the anonymous referees for their constructive comments on the previous version of this work, which improved the content substantially.Disclosure statementNo potential conflict of interest was reported by the author(s).Author contributionsBoth authors contributed equally to this work.Data availability statementThe authors declare that no data is used in this manuscript.Correction StatementThis article has been corrected with minor changes. These changes do not impact the academic content of the article.Additional informationFundingThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.Notes on contributors SandeepSandeep joined as a Junior Research Fellow in the SQC and OR Division of the Indian Statistical Institute on July 17, 2019. On December 1, 2021, he was promoted to the position of senior research fellow. At present, he is pursuing his PhD work in quality, reliability, and operations research from ISI. Before joining ISI as a research fellow, he completed his MSc in Mathematics in 2018 from the Central University of Haryana in India.Arup Ranjan MukhopadhyayDr. Arup Ranjan Mukhopadhyay has been working as a faculty member [at present, Senior Technical Officer (Professor Grade)] in the Statistical Quality Control and Operations Research Division of the Indian Statistical Institute for more than three decades, which involves applied research, teaching, ","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134910950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hierarchical model-based method for wafer level virtual metrology under process information deficiency","authors":"Yu-Jun Liu, Dong Ni, Xiong Shao, Dan-Li Gong, Jin-Jin Li","doi":"10.1080/08982112.2023.2252891","DOIUrl":"https://doi.org/10.1080/08982112.2023.2252891","url":null,"abstract":"Online inspection is one of the most critical processes of quality control in semiconductor manufacturing. The physical inspection methods for wafers are time-consuming and unable to achieve wafer level metrology. In order to improve production efficiency and expand inspection coverage, virtual metrology (VM) methods have recently received widespread attention; they utilize process parameters to estimate wafer metrology results. However, due to process drift and other reasons, the process information contained in real-time signal data (RTS data) used for VM modeling in industrial production is insufficient. This work proposed a hierarchical modeling method for machine learning-based virtual wafer metrology, leveraging RTS and post-process quality characteristics. The hierarchical model consists of an multiway principle analysis (MPCA) sub-model for RTS feature extracting and two separate long short-term memory (LSTM) networks for wafer-to-wafer dynamics in RTS and quality characteristics, respectively. A case study on the thickness VM of chemical vapor deposition thin film is conducted, and the proposed method has achieved better results than other methods in comparison.","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135734744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}