Integration of multivariate statistical process control and engineering process control: a novel framework

Siddiqui, Yasir A.; Saif, Abdul-Wahid A.; Cheded, Lahouari; Elshafei, Moustafa; Rahim, Abdur

doi:10.1007/s00170-014-6641-6

Cited by 21 publications

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“…Las aplicaciones del EPC se orientan fundamentalmente al diseño de controladores eficientes (Montogomery et al, 1994) o hacia la integración con herramientas del control estadístico de procesos. Algunos trabajos relevantes corresponden a Park et al (2012) quienes desarrollaron un modelo económico de costos para la integración de SPC (Statistical Process Control) y EPC; Aljebory y Alshebeb (2014) presentaron una solución integrada SPC/EPC para el mejoramiento de los procesos en la industria del cloro; Siddiqui et al (2015) propusieron una integración MSPC/EPC para la detección y control de fallos.…”

Section: Problemas De Variabilidad Funcionalunclassified

“…En contraste con el anterior, el EPC aborda las causas comunes y descuida las causas asignables (Hachicha et al, 2012). Dadas las falencias y potencialidades que entrañan estos enfoques, se han detectado diversos intentos por generar soluciones híbridas que minimicen las falencias señaladas (Duffua et al, 2004;Hachicha et al, 2012;Siddiqui et al, 2015;Aljebory y Alshebeb, 2014). Sin embargo, y a pesar de los avances de estas investigaciones, tales autores han destacado la necesidad de seguir avanzando en la temática.…”

Section: Introductionunclassified

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Metodología Integral para el Mejoramiento de la Calidad Mediante la Reducción de la Variabilidad Funcional: Un Caso de Estudio

2015

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ResumenEl presente artículo presenta el diseño y aplicación de una metodología que integra y aprovecha las ventajas de tres enfoques, usualmente aplicados en forma independiente, para mejorar la calidad de los productos reduciendo su variabilidad funcional. Los enfoques son, la función de pérdida de la calidad (QLF), el control estadístico multivariante de procesos (MSPC) y el control ingenieril de procesos (EPC). La mejora de la calidad de los productos se puede lograr mediante la detección e intervención de las causas comunes y asignables de variación que afectan el proceso. La metodología usada permite integrar perspectivas cualitativas y cuantitativas en la selección de las características críticas de calidad del producto. Se exponen los resultados de su aplicación en una empresa piloto del sector de alimentos, en la cual se logró una reducción del 52.49% en la variabilidad. Palabras AbstractThe present paper presents the design and application of a methodology that integrates three approaches, usually applied in an independent way, to enhance the quality of products by reducing their functional variability. The three methods are Quality Loss Function (QLF), Multivariate Statistical Process Control (MSPC) and Engineering Process Control (EPC). Improving product quality is achieved by the detection and control of the common and assignable causes of variation affecting the process. The methodology also allows integrating qualitative and quantitative perspectives for the selection of the critical-to-quality characteristics. By applying the methodology in a food company, a variability reduction of 52.49% was achieved.

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Section: Problemas De Variabilidad Funcionalunclassified

Section: Introductionunclassified

Metodología Integral para el Mejoramiento de la Calidad Mediante la Reducción de la Variabilidad Funcional: Un Caso de Estudio

2015

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“…Traditionally, the framework of quality feature data-driven state monitor includes mainly three steps: 1) Quality feature data acquisition; 2) Statistical Process Control (SPC) control chart-based approach; and 3) condition monitoring and state evaluation. [5][6][7] In the quality feature data acquisition step, the machining samples are independent for each other and should obey the rule of independent and identically distributed (IID). In the second step, the SPC aims to analyze and establish an acceptable and stable level based on statistical techniques, and the principle analyzes whether the fluctuation pattern of control chart is normal or not to judge the process is in a stable state, 8 using methods such as neural network (NN), 9,10 principal component analysis (PCA) 11,12 and the machining learning (ML) method.…”

Section: Introductionmentioning

confidence: 99%

A quality-driven stability analysis framework based on state fluctuation space model for manufacturing process

Zhao

Yao

2018

Proceedings of the Institution of Mechanical Engineers, Part E:

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Industrial manufacturing processes often show multiple operating modes, where different modes present different regularities, so real-time monitor and analyzing the quality state stability is an important way to ensure product quality. This paper proposes a state-driven fluctuation space model for quality stability analysis for multimode manufacturing process. First, the whole process is divided into many sub-processes and the multimode formation mechanism is analyzed to form the stability analysis framework. Then each single-mode quality state fluctuation space model is built based on multi-kernel support vector data description method to determine the max effective fluctuation border of the process state. For the current process state, the deep neural network (DNN) is adopted to extract process state features automatically and recognize the mode type. Thus appropriate quality stable fluctuation space model is selected to monitor and analyze the process stability state. Finally, a case study is performed to evaluate the feasibility of proposed stability analysis method, and the result reveals that the method shows good effect for analyzing the process stability in manufacturing process.

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“…Following-up on these methods, numerous procedures for multivariate statistical quality control have been developed to simultaneously monitor several quality characteristics in a process. Although somewhat more challenging to implement, the multivariate SPC methods can promote a better understanding and control of the processes being monitored and, thus, seem to be more sensitive to special causes, which are not always easily detected while using univariate control technics (Epprecht et al, 2005;He & Grigoryan, 2005;Costa et al, 2009;Costa & Machado, 2011;Kim et al, 2014;Ferrer, 2014;García-Bustos et al, 2015;Siddiqui et al, 2015). This shows an evolution of the various development and use of SPC technics and their importance in applications.…”

Section: Introductionmentioning

confidence: 99%

“…Significant advances can also be seen in the literature regarding the development of new methods and strategies for SPC, such as models for auto-correlated data and multivariate processes (Costa & Castagliola, 2011;Costa & Machado, 2011;Dokouhaki & Noorossana, 2013;Franco et al, 2014;Leoni et al, 2015), control charts allowing for varying the sampling interval and the sample size (Reynolds et al, 1988;Reynolds, 1996 Yang et al, 2012;Mahadik, 2013), improvement in the performance of control charts by double sampling (Costa & Castagliola, 2011;Teoh et al, 2015;Inghilleri et al, 2015), the design of control charts that minimize operational costs (Michel & Fogliatto, 2002;Celano et al, 2011;Lupo, 2014;Franco et al, 2014), the integration of statistical process control and automatic/engineering process control, avoiding over adjustement of the process (Holmes & Mergen, 2011;Siddiqui et al, 2015); application of the Bernoulli control charts in the field of medicine (Szarka & Woodall, 2011), application of SPC to image data (Megahed et al, 2011;Wells et al, 2013), and strategies for monitoring the variability of small batches (Celano et al, 2012;Castagliola et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

A bibliometric analysis of 50 years of worldwide research on statistical process control

et al. 2016

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Resumo: Um crescente número de trabalhos sobre controle estatístico do processo (CEP) emergiu nos últimos cinquentaAbstract: An increasing number of papers on statistical process control (SPC) has emerged in the last fifty years, especially in the last fifteen years. This may be attributed to the increased global competitiveness generated by innovation and the continuous improvement of products and processes. In this sense, SPC has a fundamentally important role in quality and production systems. The research in this paper considers the context of technological improvement and innovation of products and processes to increase corporate competitiveness. There are several other statistical technics and tools for assisting continuous improvement and innovation of products and processes but, despite the limitations in their use in the improvement projects, there is growing concern about the use of SPC. A gap between the SPC technics taught in engineering courses and their practical applications to industrial problems is observed in empirical research; thus, it is important to understand what has been done and identify the trends in SPC research. The bibliometric study in this paper is proposed in this direction and uses the Web of Science (WoS) database. Data analysis indicates that there was a growth rate of more than 90% in the number of publications on SPC after 1990. Our results reveal the countries where these publications have come from, the authors with the highest number of papers and their networks. Main sources of publications are also identified; it is observed that the publications of SPC papers are concentrated in some of the international research journals, not necessarily those with the major high-impact factors. Furthermore, the papers are focused on industrial engineering, operations research and management science fields. The most common term found in the papers was cumulative sum control charts, but new topics have emerged and have been researched in the past ten years, such as multivariate methods for process monitoring and nonparametric methods.

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Integration of multivariate statistical process control and engineering process control: a novel framework

Cited by 21 publications

References 22 publications

Metodología Integral para el Mejoramiento de la Calidad Mediante la Reducción de la Variabilidad Funcional: Un Caso de Estudio

Metodología Integral para el Mejoramiento de la Calidad Mediante la Reducción de la Variabilidad Funcional: Un Caso de Estudio

A quality-driven stability analysis framework based on state fluctuation space model for manufacturing process

A bibliometric analysis of 50 years of worldwide research on statistical process control

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