Pub Date : 2021-10-21DOI: 10.1080/00224065.2021.1991250
Richard N. McGrath, Baffour Koduah
Abstract A popular approach for estimating location and dispersion effects in replicated designs under the common assumption of normal and independent errors is to use two linked generalized linear models (glms). This approach uses an asymptotic estimate for the variance of dispersion effect estimates and is very sensitive to the normality assumption. It is also possible to identify dispersion effects (after a logarithmic transformation) by using methods developed for identifying location effects in unreplicated designs. One such method is rather robust to the normality assumption but lacks power relative to the glm approach. We introduce a hybrid approach that strikes a balance between power and robustness when used for dispersion effect identification.
{"title":"Powerful and robust dispersion contrasts for replicated orthogonal designs","authors":"Richard N. McGrath, Baffour Koduah","doi":"10.1080/00224065.2021.1991250","DOIUrl":"https://doi.org/10.1080/00224065.2021.1991250","url":null,"abstract":"Abstract A popular approach for estimating location and dispersion effects in replicated designs under the common assumption of normal and independent errors is to use two linked generalized linear models (glms). This approach uses an asymptotic estimate for the variance of dispersion effect estimates and is very sensitive to the normality assumption. It is also possible to identify dispersion effects (after a logarithmic transformation) by using methods developed for identifying location effects in unreplicated designs. One such method is rather robust to the normality assumption but lacks power relative to the glm approach. We introduce a hybrid approach that strikes a balance between power and robustness when used for dispersion effect identification.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":"30 1","pages":"573 - 588"},"PeriodicalIF":2.5,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83323856","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 : 2021-10-20DOI: 10.1080/00224065.2022.2034487
S. Knoth, Nesma A. Saleh, Mahmoud A. Mahmoud, W. Woodall, V. Tercero-Gómez
Abstract Many extensions and modifications have been made to standard process monitoring methods such as the exponentially weighted moving average (EWMA) chart and the cumulative sum (CUSUM) chart. In addition, new schemes have been proposed based on alternative weighting of past data, usually to put greater emphasis on past data and less weight on current and recent data. In other cases, the output of one process monitoring method, such as the EWMA statistic, is used as the input to another method, such as the CUSUM chart. Often the recursive formula for a control chart statistic is itself used recursively to form a new control chart statistic. We find the use of these ad hoc methods to be unjustified. Statistical performance comparisons justifying the use of these methods have been either flawed by focusing only on zero-state run length metrics or by making comparisons to an unnecessarily weak competitor.
{"title":"A critique of a variety of “memory-based” process monitoring methods","authors":"S. Knoth, Nesma A. Saleh, Mahmoud A. Mahmoud, W. Woodall, V. Tercero-Gómez","doi":"10.1080/00224065.2022.2034487","DOIUrl":"https://doi.org/10.1080/00224065.2022.2034487","url":null,"abstract":"Abstract Many extensions and modifications have been made to standard process monitoring methods such as the exponentially weighted moving average (EWMA) chart and the cumulative sum (CUSUM) chart. In addition, new schemes have been proposed based on alternative weighting of past data, usually to put greater emphasis on past data and less weight on current and recent data. In other cases, the output of one process monitoring method, such as the EWMA statistic, is used as the input to another method, such as the CUSUM chart. Often the recursive formula for a control chart statistic is itself used recursively to form a new control chart statistic. We find the use of these ad hoc methods to be unjustified. Statistical performance comparisons justifying the use of these methods have been either flawed by focusing only on zero-state run length metrics or by making comparisons to an unnecessarily weak competitor.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":"165 1","pages":"18 - 42"},"PeriodicalIF":2.5,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76906948","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 : 2021-10-20DOI: 10.1080/00224065.2021.1987815
Fin Rooney Full of helpful questions and thoughtprovoking ideas, this book offers practitioners guidance in having meaningful conversations with their senior managers and influence how they view quality. For senior managers, it offers a framework for devising a coordinated quality strategy, involving every department in quality, and showing how an embedded quality strategy can create virtuous circles of improvement.
{"title":"ASQ Books","authors":"","doi":"10.1080/00224065.2021.1987815","DOIUrl":"https://doi.org/10.1080/00224065.2021.1987815","url":null,"abstract":"Fin Rooney Full of helpful questions and thoughtprovoking ideas, this book offers practitioners guidance in having meaningful conversations with their senior managers and influence how they view quality. For senior managers, it offers a framework for devising a coordinated quality strategy, involving every department in quality, and showing how an embedded quality strategy can create virtuous circles of improvement.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":"64 1","pages":"586 - 586"},"PeriodicalIF":2.5,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85788420","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 : 2021-10-20DOI: 10.1080/00224065.2021.1987806
B. Colosimo, Enrique del Castillo, L. A. Jones‐Farmer, K. Paynabar
Abstract In many applied and industrial settings, the use of Artificial Intelligence (AI) for quality technology is gaining growing attention. AI refers to the broad set of techniques which replicate human cognitive and analytical skills for problem solving, including Machine Learning, Neural Networks and Deep Learning. This paper presents a brief introduction to the special issue, where AI-based solutions are presented to solve problems that are typically faced in the area of quality technology. Limits and advantages of AI-based solutions are briefly discussed to stimulate creative attention to novel solutions and new directions for future research.
{"title":"Artificial intelligence and statistics for quality technology: an introduction to the special issue","authors":"B. Colosimo, Enrique del Castillo, L. A. Jones‐Farmer, K. Paynabar","doi":"10.1080/00224065.2021.1987806","DOIUrl":"https://doi.org/10.1080/00224065.2021.1987806","url":null,"abstract":"Abstract In many applied and industrial settings, the use of Artificial Intelligence (AI) for quality technology is gaining growing attention. AI refers to the broad set of techniques which replicate human cognitive and analytical skills for problem solving, including Machine Learning, Neural Networks and Deep Learning. This paper presents a brief introduction to the special issue, where AI-based solutions are presented to solve problems that are typically faced in the area of quality technology. Limits and advantages of AI-based solutions are briefly discussed to stimulate creative attention to novel solutions and new directions for future research.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":"68 1","pages":"443 - 453"},"PeriodicalIF":2.5,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74036574","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 : 2021-10-20DOI: 10.1080/00224065.2021.1983491
Juan Du, Xi Zhang, Wei Ou
Abstract Solar conversion efficiency (SCE), an important quality metric in solar cell manufacturing processes, is related to chemical vapor deposition in the epitaxy stage based on the photoelectric effect. A large number of solar cell fabrication plants still lack online process monitoring strategies at the epitaxy stage and instead use offline inspections after fabrication is completed. Consequently, production efficiency is reduced due to offline inspections and the quality of wafers in downstream manufacturing stages is uncertain because only a small portion of wafers can be inspected due to random sampling within a single batch. A knowledge-infused monitoring strategy in the epitaxy stage of solar cell manufacturing processes that enables the direct link of online process monitoring to quality SCE is proposed in this study. A customized nonlinear model based on light interference with parameters that can be physically interpreted and largely accepted by practitioners is proposed to capture key information of reflectance signals. Multiple process features are extracted and a general correlation-based variable ranking procedure is adopted in this nonlinear model to rank SCE-correlated key process features. This model enables online process monitoring of key features at the epitaxy stage and allows practitioners to apply timely remedies in case of unexpected conditions. The proposed knowledge-infused process monitoring approach fully considers the physical knowledge from light interference and interpretability of parameters in the established nonlinear model correlated with the quality metric SCE to facilitate the online process monitoring at the epitaxy stage. A real solar cell manufacturing case shows the effectiveness of the proposed monitoring strategy.
{"title":"Knowledge-infused process monitoring for quality improvement in solar cell manufacturing processes","authors":"Juan Du, Xi Zhang, Wei Ou","doi":"10.1080/00224065.2021.1983491","DOIUrl":"https://doi.org/10.1080/00224065.2021.1983491","url":null,"abstract":"Abstract Solar conversion efficiency (SCE), an important quality metric in solar cell manufacturing processes, is related to chemical vapor deposition in the epitaxy stage based on the photoelectric effect. A large number of solar cell fabrication plants still lack online process monitoring strategies at the epitaxy stage and instead use offline inspections after fabrication is completed. Consequently, production efficiency is reduced due to offline inspections and the quality of wafers in downstream manufacturing stages is uncertain because only a small portion of wafers can be inspected due to random sampling within a single batch. A knowledge-infused monitoring strategy in the epitaxy stage of solar cell manufacturing processes that enables the direct link of online process monitoring to quality SCE is proposed in this study. A customized nonlinear model based on light interference with parameters that can be physically interpreted and largely accepted by practitioners is proposed to capture key information of reflectance signals. Multiple process features are extracted and a general correlation-based variable ranking procedure is adopted in this nonlinear model to rank SCE-correlated key process features. This model enables online process monitoring of key features at the epitaxy stage and allows practitioners to apply timely remedies in case of unexpected conditions. The proposed knowledge-infused process monitoring approach fully considers the physical knowledge from light interference and interpretability of parameters in the established nonlinear model correlated with the quality metric SCE to facilitate the online process monitoring at the epitaxy stage. A real solar cell manufacturing case shows the effectiveness of the proposed monitoring strategy.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":"55 1","pages":"561 - 572"},"PeriodicalIF":2.5,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74381137","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 : 2021-09-30DOI: 10.1080/00224065.2021.1973931
Huihui Miao, Andi Wang, Bing Li, Jianjun Shi
Abstract This paper proposes a method of Structural Tensor-On-Tensosr regression considering the Interaction effects (STOTI). To alleviate the curse of dimensionality and resolve computational challenge, the STOTI method describes the specific structure of the main and interaction effect tensors indicated by the prior knowledge of the data using corresponding regularization terms on their appropriate modes. We designed an ADMM consensus algorithm to estimate these coefficient tensors. Extensive simulations and a real case study of the hot rolling process verified the superiority of the proposed method in terms of estimation and prediction accuracy.
{"title":"Structural tensor-on-tensor regression with interaction effects and its application to a hot rolling process","authors":"Huihui Miao, Andi Wang, Bing Li, Jianjun Shi","doi":"10.1080/00224065.2021.1973931","DOIUrl":"https://doi.org/10.1080/00224065.2021.1973931","url":null,"abstract":"Abstract This paper proposes a method of Structural Tensor-On-Tensosr regression considering the Interaction effects (STOTI). To alleviate the curse of dimensionality and resolve computational challenge, the STOTI method describes the specific structure of the main and interaction effect tensors indicated by the prior knowledge of the data using corresponding regularization terms on their appropriate modes. We designed an ADMM consensus algorithm to estimate these coefficient tensors. Extensive simulations and a real case study of the hot rolling process verified the superiority of the proposed method in terms of estimation and prediction accuracy.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":"39 1","pages":"547 - 560"},"PeriodicalIF":2.5,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87233166","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 : 2021-09-21DOI: 10.1080/00224065.2021.1977101
Bing Si
Predictive models aim to guess, a.k.a., predict, values of a variable of interest based on other variables. It has been used throughout the entire human history and many statistical models have been developed for prediction during the last century. This book covers methods for exploration of predictive models from both instance level and dataset level. It would be a valuable addition to the Chapman & Hall/CRC’s Data Science Series. Together with other books that have published in the book series, this book provides a unique perspective into applied data science to guide data science practitioners who are interested in exploring, explaining, and examining data in real-world applications with both R and Python. Predictive models constitute an important component in the big picture of machine learning and data science approaches and require standard analytical steps such as model specification, model estimation, and model fitness diagnosis. Most of published books in this field focus on how to use these statistical methods to make predictions for different types of datasets, while lack of tools for model exploration and, in particular, model explanation (obtaining insights from model-based prediction) and model examination (evaluation of model performance and understanding its weakness). In contrast, this book is a novel effort that provides a deep understanding to all the steps with extensive validation and justification methods, leading to a better and faster interpretable data analysis. The book is well organized with three parts. It starts with an overview of basic concepts in Chapters 1-4 and then presents the instance-level exploration and datasetlevel exploration in Chapters 5-13 and Chapters 14-20, respectively. The overview part introduces basic and essential knowledge on model development process, software installation, and how to perform classic predictive models using software. The instance-level exploration part covers methods to help better understand “how a model yields a prediction for a particular single observation” for predictive models with both a small and a large number of exploratory variables. The last part is about dataset-level exploration that discusses “how do the model predictions perform overall, for an entire set of observations?” Although a basic understanding of programming languages would be beneficial, the coding part in this book is designed to be self-contained and friendly to readers without programming background as well. Additionally, it is worth noting that the readers are expected to have a certain level of knowledge about different types of data science models, such as logistic regression, support vector machine, and gradient boosting, and understand which kind of research questions each model can address. For example, given a research question aiming at predicting patient survival (yes/no) after surgery from other variables, e.g., age, symptoms, and medical history, the reader should be able to identify th
{"title":"Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models","authors":"Bing Si","doi":"10.1080/00224065.2021.1977101","DOIUrl":"https://doi.org/10.1080/00224065.2021.1977101","url":null,"abstract":"Predictive models aim to guess, a.k.a., predict, values of a variable of interest based on other variables. It has been used throughout the entire human history and many statistical models have been developed for prediction during the last century. This book covers methods for exploration of predictive models from both instance level and dataset level. It would be a valuable addition to the Chapman & Hall/CRC’s Data Science Series. Together with other books that have published in the book series, this book provides a unique perspective into applied data science to guide data science practitioners who are interested in exploring, explaining, and examining data in real-world applications with both R and Python. Predictive models constitute an important component in the big picture of machine learning and data science approaches and require standard analytical steps such as model specification, model estimation, and model fitness diagnosis. Most of published books in this field focus on how to use these statistical methods to make predictions for different types of datasets, while lack of tools for model exploration and, in particular, model explanation (obtaining insights from model-based prediction) and model examination (evaluation of model performance and understanding its weakness). In contrast, this book is a novel effort that provides a deep understanding to all the steps with extensive validation and justification methods, leading to a better and faster interpretable data analysis. The book is well organized with three parts. It starts with an overview of basic concepts in Chapters 1-4 and then presents the instance-level exploration and datasetlevel exploration in Chapters 5-13 and Chapters 14-20, respectively. The overview part introduces basic and essential knowledge on model development process, software installation, and how to perform classic predictive models using software. The instance-level exploration part covers methods to help better understand “how a model yields a prediction for a particular single observation” for predictive models with both a small and a large number of exploratory variables. The last part is about dataset-level exploration that discusses “how do the model predictions perform overall, for an entire set of observations?” Although a basic understanding of programming languages would be beneficial, the coding part in this book is designed to be self-contained and friendly to readers without programming background as well. Additionally, it is worth noting that the readers are expected to have a certain level of knowledge about different types of data science models, such as logistic regression, support vector machine, and gradient boosting, and understand which kind of research questions each model can address. For example, given a research question aiming at predicting patient survival (yes/no) after surgery from other variables, e.g., age, symptoms, and medical history, the reader should be able to identify th","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":"26 1","pages":"486 - 486"},"PeriodicalIF":2.5,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89635696","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 : 2021-09-16DOI: 10.1080/00224065.2021.1977100
Bi Si
{"title":"Probability and statistical inference: From basic principles to advanced models","authors":"Bi Si","doi":"10.1080/00224065.2021.1977100","DOIUrl":"https://doi.org/10.1080/00224065.2021.1977100","url":null,"abstract":"","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":"10 1","pages":"485 - 485"},"PeriodicalIF":2.5,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73379453","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 : 2021-08-30DOI: 10.1080/00224065.2021.1963200
J. Lian, Laura J. Freeman, Yili Hong, Xinwei Deng
Abstract Artificial intelligence (AI) algorithms, such as deep learning and XGboost, are used in numerous applications including autonomous driving, manufacturing process optimization and medical diagnostics. The robustness of AI algorithms is of great interest as inaccurate prediction could result in safety concerns and limit the adoption of AI systems. In this paper, we propose a framework based on design of experiments to systematically investigate the robustness of AI classification algorithms. A robust classification algorithm is expected to have high accuracy and low variability under different application scenarios. The robustness can be affected by a wide range of factors such as the imbalance of class labels in the training dataset, the chosen prediction algorithm, the chosen dataset of the application, and a change of distribution in the training and test datasets. To investigate the robustness of AI classification algorithms, we conduct a comprehensive set of mixture experiments to collect prediction performance results. Then statistical analyses are conducted to understand how various factors affect the robustness of AI classification algorithms. We summarize our findings and provide suggestions to practitioners in AI applications.
{"title":"Robustness with respect to class imbalance in artificial intelligence classification algorithms","authors":"J. Lian, Laura J. Freeman, Yili Hong, Xinwei Deng","doi":"10.1080/00224065.2021.1963200","DOIUrl":"https://doi.org/10.1080/00224065.2021.1963200","url":null,"abstract":"Abstract Artificial intelligence (AI) algorithms, such as deep learning and XGboost, are used in numerous applications including autonomous driving, manufacturing process optimization and medical diagnostics. The robustness of AI algorithms is of great interest as inaccurate prediction could result in safety concerns and limit the adoption of AI systems. In this paper, we propose a framework based on design of experiments to systematically investigate the robustness of AI classification algorithms. A robust classification algorithm is expected to have high accuracy and low variability under different application scenarios. The robustness can be affected by a wide range of factors such as the imbalance of class labels in the training dataset, the chosen prediction algorithm, the chosen dataset of the application, and a change of distribution in the training and test datasets. To investigate the robustness of AI classification algorithms, we conduct a comprehensive set of mixture experiments to collect prediction performance results. Then statistical analyses are conducted to understand how various factors affect the robustness of AI classification algorithms. We summarize our findings and provide suggestions to practitioners in AI applications.","PeriodicalId":54769,"journal":{"name":"Journal of Quality Technology","volume":"303 1","pages":"505 - 525"},"PeriodicalIF":2.5,"publicationDate":"2021-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77936639","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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