Pub Date : 2023-02-28DOI: 10.1080/00224065.2023.2170839
Sebastian Ottenstreuer, C. Weiß, M. Testik
Abstract Qualitative, more specifically, ordinal data generating processes are common in real-world process control implementations. In this study, a survey of control charts for the sample-based monitoring of independent and identically distributed ordinal data is provided together with critical comparisons of the control statistics, for memory-less Shewhart-type and for memory-utilizing exponentially weighted moving average (EWMA) and cumulative-sum types of control charts. New results and proposals are also provided for process monitoring. Using some real-world quality scenarios from the literature, a simulation study for performance comparisons is conducted, covering sixteen different types of control chart. It is shown that demerit-type charts used in combination with EWMA smoothing generally perform better than the other charts, which may rely on quite sophisticated derivations. A real-world data example for monitoring flashes in electric toothbrush manufacturing is discussed to illustrate the application and interpretation of the control charts in the study.
{"title":"A review and comparison of control charts for ordinal samples","authors":"Sebastian Ottenstreuer, C. Weiß, M. Testik","doi":"10.1080/00224065.2023.2170839","DOIUrl":"https://doi.org/10.1080/00224065.2023.2170839","url":null,"abstract":"Abstract Qualitative, more specifically, ordinal data generating processes are common in real-world process control implementations. In this study, a survey of control charts for the sample-based monitoring of independent and identically distributed ordinal data is provided together with critical comparisons of the control statistics, for memory-less Shewhart-type and for memory-utilizing exponentially weighted moving average (EWMA) and cumulative-sum types of control charts. New results and proposals are also provided for process monitoring. Using some real-world quality scenarios from the literature, a simulation study for performance comparisons is conducted, covering sixteen different types of control chart. It is shown that demerit-type charts used in combination with EWMA smoothing generally perform better than the other charts, which may rely on quite sophisticated derivations. A real-world data example for monitoring flashes in electric toothbrush manufacturing is discussed to illustrate the application and interpretation of the control charts in the study.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77637769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-22DOI: 10.1080/00224065.2023.2171320
Zhanpan Zhang
This clear and well-structured book is the second edition of the same authors’ 2014 book, Bayesian Networks With Examples in R. In addition to the core material covered in the first edition, the new edition expands several topics such as conditional Gaussian Bayesian networks, dynamic Bayesian networks, and general Bayesian networks, etc. It’s worth mentioning that there is a website for this book and the related R package, “bnlearn”, in which the R code used in the book can be downloaded and additional examples are provided. This book can be suitable for an introductory Bayesian network course at the MS or PhD level. Chapters 1 to 4 follow a similar structure to introduce several types of Bayesian network. Each chapter presents graphical and probabilistic representation of Bayesian network, parameter estimation, structure learning, inference, and Bayesian network plotting. Chapter 1 focuses on multinomial Bayesian network for discrete data, whereas Chapter 2 on Gaussian Bayesian network for continuous data. In these two chapters, all the variables follow probability distributions belonging to the same family, either multinomial or normal. Chapter 3 introduces conditional Gaussian Bayesian network that is a “mixture of normals” model in which continuous nodes can have both continuous and discrete parents while discrete nodes can only have discrete parents. This chapter demonstrates an initial step to combine different families of probability distributions in building a Bayesian network. Chapter 4 discusses dynamic Bayesian network for dynamic problems in which some variables can evolve over time, therefore a variable measured at different times can be treated as different nodes in Bayesian network. Chapter 5 presents general Bayesian network in which each variable is modeled by its most suitable distribution rather than limited to follow multinomial or normal distribution. Since this is a more general case for Bayesian network building, Stan (an open source software for Bayesian statistical inference using Markov chain Monte Carlo sampling) and its R interface, “rstan”, are adopted to perform random sampling and parameter estimation. Chapter 6 covers theoretical foundations for Bayesian network, in which the formal definition of a Bayesian network and its properties are introduced, and the algorithms for Bayesian network learning and inference are included. This chapter also discusses two important topics: what are the assumptions and challenges in learning a causal Bayesian network; and what considerations are needed to evaluate a Bayesian network. Chapter 7 provides an overview of software packages for Bayesian network development. A number of R packages are listed in a table, along with information on each package’s capability to handle discrete and/or continuous data, as well as its support for structure learning, parameter learning, and inference. Stan and its features are discussed, and several commercial software packages are briefly menti
{"title":"Bayesian networks with examples in R","authors":"Zhanpan Zhang","doi":"10.1080/00224065.2023.2171320","DOIUrl":"https://doi.org/10.1080/00224065.2023.2171320","url":null,"abstract":"This clear and well-structured book is the second edition of the same authors’ 2014 book, Bayesian Networks With Examples in R. In addition to the core material covered in the first edition, the new edition expands several topics such as conditional Gaussian Bayesian networks, dynamic Bayesian networks, and general Bayesian networks, etc. It’s worth mentioning that there is a website for this book and the related R package, “bnlearn”, in which the R code used in the book can be downloaded and additional examples are provided. This book can be suitable for an introductory Bayesian network course at the MS or PhD level. Chapters 1 to 4 follow a similar structure to introduce several types of Bayesian network. Each chapter presents graphical and probabilistic representation of Bayesian network, parameter estimation, structure learning, inference, and Bayesian network plotting. Chapter 1 focuses on multinomial Bayesian network for discrete data, whereas Chapter 2 on Gaussian Bayesian network for continuous data. In these two chapters, all the variables follow probability distributions belonging to the same family, either multinomial or normal. Chapter 3 introduces conditional Gaussian Bayesian network that is a “mixture of normals” model in which continuous nodes can have both continuous and discrete parents while discrete nodes can only have discrete parents. This chapter demonstrates an initial step to combine different families of probability distributions in building a Bayesian network. Chapter 4 discusses dynamic Bayesian network for dynamic problems in which some variables can evolve over time, therefore a variable measured at different times can be treated as different nodes in Bayesian network. Chapter 5 presents general Bayesian network in which each variable is modeled by its most suitable distribution rather than limited to follow multinomial or normal distribution. Since this is a more general case for Bayesian network building, Stan (an open source software for Bayesian statistical inference using Markov chain Monte Carlo sampling) and its R interface, “rstan”, are adopted to perform random sampling and parameter estimation. Chapter 6 covers theoretical foundations for Bayesian network, in which the formal definition of a Bayesian network and its properties are introduced, and the algorithms for Bayesian network learning and inference are included. This chapter also discusses two important topics: what are the assumptions and challenges in learning a causal Bayesian network; and what considerations are needed to evaluate a Bayesian network. Chapter 7 provides an overview of software packages for Bayesian network development. A number of R packages are listed in a table, along with information on each package’s capability to handle discrete and/or continuous data, as well as its support for structure learning, parameter learning, and inference. Stan and its features are discussed, and several commercial software packages are briefly menti","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83561003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-27DOI: 10.1080/00224065.2022.2161434
Konstantinos Bourazas, F. Sobas, P. Tsiamyrtzis
Abstract The online quality monitoring of a process with low volume data is a very challenging task and the attention is most often placed in detecting when some of the underline (unknown) process parameter(s) experience a persistent shift. Self-starting methods, both in the frequentist and the Bayesian domain aim to offer a solution. Adopting the latter perspective, we propose a general closed-form Bayesian scheme, where the testing procedure is built on a memory-based control chart that relies on the cumulative ratios of sequentially updated predictive distributions. The theoretic framework can accommodate any likelihood from the regular exponential family and the use of conjugate analysis allows closed form modeling. Power priors will offer the axiomatic framework to incorporate into the model different sources of information, when available. A simulation study evaluates the performance against competitors and examines aspects of prior sensitivity. Technical details and algorithms are provided as supplementary material.
{"title":"Predictive ratio CUSUM (PRC): A Bayesian approach in online change point detection of short runs","authors":"Konstantinos Bourazas, F. Sobas, P. Tsiamyrtzis","doi":"10.1080/00224065.2022.2161434","DOIUrl":"https://doi.org/10.1080/00224065.2022.2161434","url":null,"abstract":"Abstract The online quality monitoring of a process with low volume data is a very challenging task and the attention is most often placed in detecting when some of the underline (unknown) process parameter(s) experience a persistent shift. Self-starting methods, both in the frequentist and the Bayesian domain aim to offer a solution. Adopting the latter perspective, we propose a general closed-form Bayesian scheme, where the testing procedure is built on a memory-based control chart that relies on the cumulative ratios of sequentially updated predictive distributions. The theoretic framework can accommodate any likelihood from the regular exponential family and the use of conjugate analysis allows closed form modeling. Power priors will offer the axiomatic framework to incorporate into the model different sources of information, when available. A simulation study evaluates the performance against competitors and examines aspects of prior sensitivity. Technical details and algorithms are provided as supplementary material.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86077818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-27DOI: 10.1080/00224065.2023.2165463
Hon Keung Tony Ng
"Statistical Methods for Reliability Data." Journal of Quality Technology, ahead-of-print(ahead-of-print), pp. 1–3
可靠性数据的统计方法。质量技术杂志，印刷前(印刷前)，第1-3页
{"title":"Statistical Methods for Reliability Data","authors":"Hon Keung Tony Ng","doi":"10.1080/00224065.2023.2165463","DOIUrl":"https://doi.org/10.1080/00224065.2023.2165463","url":null,"abstract":"\"Statistical Methods for Reliability Data.\" Journal of Quality Technology, ahead-of-print(ahead-of-print), pp. 1–3","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135838066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-27DOI: 10.1080/00224065.2022.2161435
Konstantinos Bourazas, F. Sobas, P. Tsiamyrtzis
Abstract In statistical process control/monitoring (SPC/M), memory-based control charts aim to detect small/medium persistent parameter shifts. When a phase I calibration is not feasible, self-starting methods have been proposed, with the predictive ratio cusum (PRC) being one of them. To apply such methods in practice, one needs to derive the decision limit threshold that will guarantee a preset false alarm tolerance, a very difficult task when the process parameters are unknown and their estimate is sequentially updated. Utilizing the Bayesian framework in PRC, we will provide the theoretic framework that will allow to derive a decision-making threshold, based on false alarm tolerance, which along with the PRC closed-form monitoring scheme will permit its straightforward application in real-life practice. An enhancement of PRC is proposed, and a simulation study evaluates its robustness against competitors for various model type misspecifications. Finally, three real data sets (normal, Poisson, and binomial) illustrate its implementation in practice. Technical details, algorithms, and R-codes reproducing the illustrations are provided as supplementary material.
{"title":"Design and properties of the predictive ratio cusum (PRC) control charts","authors":"Konstantinos Bourazas, F. Sobas, P. Tsiamyrtzis","doi":"10.1080/00224065.2022.2161435","DOIUrl":"https://doi.org/10.1080/00224065.2022.2161435","url":null,"abstract":"Abstract In statistical process control/monitoring (SPC/M), memory-based control charts aim to detect small/medium persistent parameter shifts. When a phase I calibration is not feasible, self-starting methods have been proposed, with the predictive ratio cusum (PRC) being one of them. To apply such methods in practice, one needs to derive the decision limit threshold that will guarantee a preset false alarm tolerance, a very difficult task when the process parameters are unknown and their estimate is sequentially updated. Utilizing the Bayesian framework in PRC, we will provide the theoretic framework that will allow to derive a decision-making threshold, based on false alarm tolerance, which along with the PRC closed-form monitoring scheme will permit its straightforward application in real-life practice. An enhancement of PRC is proposed, and a simulation study evaluates its robustness against competitors for various model type misspecifications. Finally, three real data sets (normal, Poisson, and binomial) illustrate its implementation in practice. Technical details, algorithms, and R-codes reproducing the illustrations are provided as supplementary material.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81607131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-27DOI: 10.1080/00224065.2023.2167674
Novita Pratiwi Lembang
{"title":"Optimization of Pharmaceutical Processes","authors":"Novita Pratiwi Lembang","doi":"10.1080/00224065.2023.2167674","DOIUrl":"https://doi.org/10.1080/00224065.2023.2167674","url":null,"abstract":"","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90937036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-27DOI: 10.1080/00224065.2023.2171319
Joseph D. Conklin
The ability to harness data for actionable insight is increasingly essential for nearly every sector of the economy. We are awash in data, yet companies and organizations don’t always know how best to leverage their data to meet strategic goals, improve outcomes, or simply gain deeper understanding of their operations. This stackable graduate certificate in analytics and modeling focuses on foundational skills and knowledge for those working in or hoping to work in data science and analytics in any industry.
{"title":"Data Science: A First Introduction","authors":"Joseph D. Conklin","doi":"10.1080/00224065.2023.2171319","DOIUrl":"https://doi.org/10.1080/00224065.2023.2171319","url":null,"abstract":"The ability to harness data for actionable insight is increasingly essential for nearly every sector of the economy. We are awash in data, yet companies and organizations don’t always know how best to leverage their data to meet strategic goals, improve outcomes, or simply gain deeper understanding of their operations. This stackable graduate certificate in analytics and modeling focuses on foundational skills and knowledge for those working in or hoping to work in data science and analytics in any industry.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80512385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-23DOI: 10.1080/00224065.2022.2139783
Yuhui Yao, S. Chakraborti, X. Yang, J. Parton, Dwight Lewis, M. Hudnall
Abstract Phase I or retrospective process monitoring plays a key part in an overall statistical process monitoring (SPM) regime and is increasingly emphasized in the recent literature. At present, a lot of the data in a variety of settings (public and private sector organizations) are collected individually and sequentially and thus are serially correlated (or autocorrelated). Though a reasonable amount of work is available in the control charting literature for prospective (Phase II) autocorrelated data monitoring, very little work exists for the retrospective phase (Phase I). In this article, we present a Shewhart-type control chart for Phase I monitoring of individual autocorrelated data, assuming normality, with estimated parameters. The methodology, while developed and presented for the first-order autoregressive (AR(1)) model for simplicity, may be adapted to more general time series models. The correct charting constants, adjusted for autocorrelation and parameter estimation, are derived, and tabulated for a nominal in-control (IC) false alarm probability (FAP). Simulation results show that the proposed chart is favorably IC FAP robust and effective for reasonably small sample sizes, moderate autocorrelation, and some model miss-specifications, compared to other approaches. An illustration using some public health data involving prescription fentanyl transactions is provided to show the potential for broader areas of applications of the proposed methodology. Along with a summary and recommendations, some future research areas are indicated. An R package is developed and made available for implementing the proposed methodology on demand.
{"title":"Phase I control chart for individual autocorrelated data: application to prescription opioid monitoring","authors":"Yuhui Yao, S. Chakraborti, X. Yang, J. Parton, Dwight Lewis, M. Hudnall","doi":"10.1080/00224065.2022.2139783","DOIUrl":"https://doi.org/10.1080/00224065.2022.2139783","url":null,"abstract":"Abstract Phase I or retrospective process monitoring plays a key part in an overall statistical process monitoring (SPM) regime and is increasingly emphasized in the recent literature. At present, a lot of the data in a variety of settings (public and private sector organizations) are collected individually and sequentially and thus are serially correlated (or autocorrelated). Though a reasonable amount of work is available in the control charting literature for prospective (Phase II) autocorrelated data monitoring, very little work exists for the retrospective phase (Phase I). In this article, we present a Shewhart-type control chart for Phase I monitoring of individual autocorrelated data, assuming normality, with estimated parameters. The methodology, while developed and presented for the first-order autoregressive (AR(1)) model for simplicity, may be adapted to more general time series models. The correct charting constants, adjusted for autocorrelation and parameter estimation, are derived, and tabulated for a nominal in-control (IC) false alarm probability (FAP). Simulation results show that the proposed chart is favorably IC FAP robust and effective for reasonably small sample sizes, moderate autocorrelation, and some model miss-specifications, compared to other approaches. An illustration using some public health data involving prescription fentanyl transactions is provided to show the potential for broader areas of applications of the proposed methodology. Along with a summary and recommendations, some future research areas are indicated. An R package is developed and made available for implementing the proposed methodology on demand.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78769800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-23DOI: 10.1080/00224065.2022.2151530
Mohammed Saif Ismail Hameed, José Núñez Ares, P. Goos
Abstract Experimental data are often highly structured due to the use of experimental designs. This does not only simplify the analysis, but it allows for tailored methods of analysis that extract more information from the data than generic methods. One group of three-level experimental designs that are suitable for such tailored methods are orthogonal minimally aliased response surface (OMARS) designs (Núñez Ares and Goos 2020), where all main effects are orthogonal to each other and to all second-order effects. The design based analysis method of Jones and Nachtsheim (2017) has shown significant improvement over existing methods in terms of powers to detect active effects. However, the application of their method is limited to only a small subgroup of OMARS designs known as definitive screening designs (DSDs). In our work, we not only improve upon the Jones and Nachtsheim method for DSDs, but we also generalize their analysis framework to the entire family of OMARS designs. Using extensive simulations, we show that our customized method for analyzing data from OMARS designs is highly effective in identifying active effects when compared to other modern (non-design based) analysis methods, especially in cases where the true model is complex and involves many second-order effects.
{"title":"Analysis of data from orthogonal minimally aliased response surface designs","authors":"Mohammed Saif Ismail Hameed, José Núñez Ares, P. Goos","doi":"10.1080/00224065.2022.2151530","DOIUrl":"https://doi.org/10.1080/00224065.2022.2151530","url":null,"abstract":"Abstract Experimental data are often highly structured due to the use of experimental designs. This does not only simplify the analysis, but it allows for tailored methods of analysis that extract more information from the data than generic methods. One group of three-level experimental designs that are suitable for such tailored methods are orthogonal minimally aliased response surface (OMARS) designs (Núñez Ares and Goos 2020), where all main effects are orthogonal to each other and to all second-order effects. The design based analysis method of Jones and Nachtsheim (2017) has shown significant improvement over existing methods in terms of powers to detect active effects. However, the application of their method is limited to only a small subgroup of OMARS designs known as definitive screening designs (DSDs). In our work, we not only improve upon the Jones and Nachtsheim method for DSDs, but we also generalize their analysis framework to the entire family of OMARS designs. Using extensive simulations, we show that our customized method for analyzing data from OMARS designs is highly effective in identifying active effects when compared to other modern (non-design based) analysis methods, especially in cases where the true model is complex and involves many second-order effects.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76126148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-20DOI: 10.1080/00224065.2022.2147884
S. Kloppenborg
Abstract In the aerospace composites industry, new material lots are tested to determine if they are suitable for use. It is common to accept or reject the material lot by comparing the sample mean and lower extremum to reference values that are established based on an initial (qualification) sample of material property measurements. Current industry practices assume that the samples are drawn from a normal distribution with known parameters equal to the mean and standard deviation of the qualification sample: this assumption yields a producer’s risk that is too high. This article presents a two-sample method of setting these reference values, considering the sampling distribution of the qualification sample. This new method is validated through simulation which shows that it produces the correct probability of Type I error. Simulation is also used to investigate the statistical power of the new method and it is compared to others commonly used. A case study is presented to demonstrate the use of the new method using composite material data from industry.
{"title":"Lot acceptance testing using sample mean and extremum with finite qualification samples","authors":"S. Kloppenborg","doi":"10.1080/00224065.2022.2147884","DOIUrl":"https://doi.org/10.1080/00224065.2022.2147884","url":null,"abstract":"Abstract In the aerospace composites industry, new material lots are tested to determine if they are suitable for use. It is common to accept or reject the material lot by comparing the sample mean and lower extremum to reference values that are established based on an initial (qualification) sample of material property measurements. Current industry practices assume that the samples are drawn from a normal distribution with known parameters equal to the mean and standard deviation of the qualification sample: this assumption yields a producer’s risk that is too high. This article presents a two-sample method of setting these reference values, considering the sampling distribution of the qualification sample. This new method is validated through simulation which shows that it produces the correct probability of Type I error. Simulation is also used to investigate the statistical power of the new method and it is compared to others commonly used. A case study is presented to demonstrate the use of the new method using composite material data from industry.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79728458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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