Monitoring and control of the multihead weighing process through a modified control chart

García-Díaz, J. Carlos; Pulido-Rojano, Alexander

doi:10.15446/dyna.v84n200.58518

Cited by 11 publications

(5 citation statements)

References 11 publications

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“…The w i weights in the hoppers are arranged in a normal distribution w i ∼ N(µ, σ) [14][15][16], where µ is the average weight of the product to be provided in each weighing hopper H Wi , and σ is the standard deviation. Previous studies have found that σ has a direct correlation with the mean quantity µ and linearly depends on it, due to vibrating feeding systems, according to σ = γµ; where γ is a proportionality coefficient and varies according to the characteristics of the packaging product [14][15][16][17]19].…”

Section: Weighing Process On Double-layered Upright and Diagonal Weig...mentioning

confidence: 99%

“…The second strategy (S 2 ) proposes diving the n weighing hopper into three groups, (n 1 , n 3 , n 5 ) and feeding different average quantities to each subgroup (µ 1 , µ 3 , µ 5 ), as per Equation (16). Finally, in the third strategy (S 3 ), the n weighing hoppers are filled with the same amount of product, µ = T k for each hopper, as expressed in Equation (17).…”

Section: Hopper-filling Strategiesmentioning

confidence: 99%

“…Statistical control of the packaging process has also been addressed. For example, several modified control charts have been developed and studied to monitor and control the package production process [17]. Finally, new bio-objective approaches have been developed for the optimization of actual package weights [18,19], incorporating package target weights and the priority associated with the product discharge times reported by hoppers into the model.…”

Section: Introductionmentioning

confidence: 99%

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Packaging Process Optimization in Multihead Weighers with Double-Layered Upright and Diagonal Systems

2021

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In multihead weighers, packaging processes seek to find the best combination of passage hoppers whose product content provides a total package weight as close as possible to its (nominal) label weight. The weighing hoppers arranged in these machines dispense the product quantity that each package contains through computer algorithms designed and executed for this purpose. For its part, in the packaging process for double-layered multihead weighers, all hoppers are arranged in two levels. The first layer comprises a group of weighing hoppers, and the second comprises a set of booster hoppers placed uprightly or diagonally to each weighing hopper based on design of the machine. In both processes, the initial machine configuration is the same; however, the hopper selection algorithm works differently. This paper proposes a new packaging process optimization algorithm for double-layer upright and diagonal machines, wherein the hopper subset combined has previously been defined, and the packaging weight is expressed as actual values. As part of its validation, product filling strategies were implemented for weighing hoppers to assess the algorithm in different scenarios. Results from the process performance metrics prove that the new algorithm improves processes by reducing variability. In addition, results reveal that some machine configurations were also able to improve their operation.

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Section: Weighing Process On Double-layered Upright and Diagonal Weig...mentioning

confidence: 99%

Section: Hopper-filling Strategiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Packaging Process Optimization in Multihead Weighers with Double-Layered Upright and Diagonal Systems

2021

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“…This study compares these approaches by reviewing the effects of parameter estimates when monitoring problems that follow a gamma distribution, such as time between events and other asymmetric processes, with a CUSUM control chart design made for optimal performance when parameters are known. To motivate our research, we use the studies of García‐Díaz and Pulido‐Rojano 21 where measurements of multihead weightings from a packaging process approaches a gamma model and requires monitoring.…”

Section: Introductionmentioning

confidence: 99%

A CUSUM control chart for gamma distribution with guaranteed performance

Madrid‐Alvarez,

García‐Díaz,

Tercero‐Gómez

2023

Quality & Reliability Eng

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Even though most control chart developments have revolved around the normal distribution, productive operations, in fact, often use non‐normal data, and some even use skewed distributions. This occurs with cycle times and many processes with a single specification limit. Under these conditions, gamma distributions often offer a better characterization where CUSUM schemes provide optimal performance to detect changes of a known in‐control (IC) parameter. In practice, gamma parameters have to be estimated, and limited phase I samples have undesirable effects on the phase II average run length (ARL) that has not been examined. To address this situation, this study uses Markovian models to assess the ARL of a CUSUM developed to monitor scale changes when IC parameters are estimated. This paper discusses the conditional ARL performance and proposes an adaptation of a stochastic estimation algorithm to guarantee a minimum performance. IC and out‐of‐control performance is compared with the conditional performance of a traditional approach where no corrections are made to guarantee a specific behavior. To demonstrate the practical application of this new approach, an implementation was made in a packaging process using multihead weighing machines. Practitioners interested in using reliable monitoring schemes will find, in this proposal, several implementation guidelines such as algorithms and look up tables to facilitate their implementations.

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“…La mejora continua en los procesos y productos se constituye como la clave para alcanzarla. Dentro de esta mejora continua, la calidad es sin duda pieza fundamental para satisfacer las expectativas de los clientes, de ahí que a través del tiempo las compañías busquen mecanismos para garantizarla y evitar productos defectuosos que se traduzcan en cuantiosas pérdidas, no solo a nivel financiero sino de imagen frente a los clientes [1][2][3][4].…”

Section: Introductionunclassified

Mejora de procesos de producción a través de la gestión de riesgos y herramientas estadísticas

Pulido-Rojano

Ruiz-Lazaro

Ortiz-Ospino

2020

Ingeniare. Rev. chil. ing.

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Los enfoques de prevención de riesgos en actividades, funciones o procesos se han convertido en piezas fundamentales a la hora de minimizar la ocurrencia de eventos que son perjudiciales para las compañías. Cada producto no conforme está estrechamente ligado con eventos no deseados relacionados con uno o algunos de los factores que intervienen en el proceso. La identificación, análisis, evaluación, tratamiento, comunicación y monitoreo de estos eventos no deseados garantizarán el incremento de la calidad en los productos y la productividad en el proceso productivo. En este artículo, proponemos un diseño metodológico para la prevención de riesgos en procesos productivos. La metodología propone una forma novedosa de combinar el uso de herramientas estadísticas de calidad y la norma ISO 31000 de gestión de riesgos. La validación fue hecha sobre un proceso de envasado de productos lácteos. Las conclusiones de esta investigación muestran que el diseño metodológico propuesto es suficientemente flexible para ser adaptado a cualquier tipo de proceso de fabricación que se desea monitorear y mejorar. Palabras clave: Control de calidad, proceso de gestión de riesgos, mejora de calidad, herramientas de estadísticas.

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Monitoring and control of the multihead weighing process through a modified control chart

Cited by 11 publications

References 11 publications

Packaging Process Optimization in Multihead Weighers with Double-Layered Upright and Diagonal Systems

Packaging Process Optimization in Multihead Weighers with Double-Layered Upright and Diagonal Systems

A CUSUM control chart for gamma distribution with guaranteed performance

Mejora de procesos de producción a través de la gestión de riesgos y herramientas estadísticas

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