Alternative methods for the simultaneous monitoring of simple linear profile parameters

Mahmood, Tahir; Riaz, Muhammad; Omar, M. Hafidz; Xie, Min

doi:10.1007/s00170-018-2149-9

Cited by 47 publications

(28 citation statements)

References 35 publications

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“…Most of the abovementioned studies were examined under the fixed effect model while under a random effect model, a Phase II method was suggested by Noorossana, et al [22]. The max and sum of square based linear profile monitoring methods were discussed by Mahmood, et al [23], and a progressive approach for simple linear profile was suggested by Saeed, et al [24]. Most of the linear profile studies were designed under simple random sampling but under different sampling environments such as ranked set and modified successive samplings were examined in [25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

An Improved Control Chart for Monitoring Linear Profiles and its Application in Thermal Conductivity

2020

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In most of the manufacturing processes, we encounter different quality characteristics of a product and process. These characteristics can be categorized into two kinds; study variables (variable of interest) and the supporting/explanatory variables. Sometime, a linear relationship might exist between the study and supporting variable, which is called simple linear profiles. This study focuses on the simple linear profiles under assorted control charting approach to detect the large, moderate and small disturbances in the process parameters. The evaluation of the proposed assorted method is assessed by using numerous performance measures, for instance, average run length, relative average run length, extra and sequential extra quadratic losses. A comparative analysis of the proposal is also carried out with some existing linear profile methods including Shewhart_3, Hotelling's 2 , EWMA_3, EWMA/R and CUSUM_3 charts. Finally, a real-life application of the proposed assorted chart is presented to monitor thermal management of diamondcopper composite.

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Section: Introductionmentioning

confidence: 99%

An Improved Control Chart for Monitoring Linear Profiles and its Application in Thermal Conductivity

2020

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“…If a functional relationship can be identified between the quality characteristic

Z

and one or more explanatory variables, then a profile can be defined. Profile monitoring has received a lot of attention in SPM literature: Since the early work of Kang and Albin about simple linear profiles, which are still widely investigated due to their easiness of implementation, see DeMagalhaes and Von Doellinger, Dawod et al, and Mahmood et al, the investigation has been extended to many other functional relationships and nonparametric techniques: Comprehensive reviews can be found in Woodall et al and Maleki and in the book of Noorossana et al Given the growing interest on geometric profile monitoring, see Colosimo et al, which today is becoming more and more precise thanks to the continuous developments in the measurement technologies, in this paper, we focus our attention on SPM of extreme values for geometric profiles. Product quality can be represented by geometric 1D or 2D profiles: In the latter case, we speak about surface monitoring.…”

Section: Introductionmentioning

confidence: 99%

On‐line monitoring of extreme values of geometric profiles in finite horizon processes

Celano

Castagliola

2020

Quality & Reliability Eng

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We investigate the challenges to be faced by quality practitioners to implement a control chart for individuals aimed at monitoring the stability of maximum/minimum realizations of profile quality features. We consider the situation where only a limited amount of inspections can be scheduled, which is typical of the most recent manufacturing applications for the production of small lots of customized products or processes characterized by frequent changeovers. A quantitative study on simulated profiles described by functional data modelled as random Gaussian fields is provided. Finally, we discuss how the proposed control chart can be implemented in a canning process. KEYWORDScontrol chart, generalized extreme value distribution, random Gaussian process, statistical performance Qual Reliab Engng Int. 2020;36:1313-1332.wileyonlinelibrary.com/journal/qre

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“…Özdamar used regression quality control chart to deal with the problem in a fiber‐board industry located in Isparta. The monitoring of location parameter when the study variable is correlated with auxiliary information was discussed by Adegoke et al , Sanusi et al , Adegoke et al , and Adegoke et al Moreover, recent studies on simple linear profiles are discussed in Riaz et al, Abbas et al , Mahmood et al, Saeed et al, Mahmood et al, Riaz et al , and references therein.…”

Section: Introductionmentioning

confidence: 99%

GLM‐based control charts for the inverse Gaussian distributed response variable

Kinat

Amin

Mahmood

2019

Quality & Reliability Eng

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In the modern era of digitalization, manufacturing industries needed monitoring methods to timely detect an abrupt change in the process. Control charts are widely used online monitoring method and used in several sectors for the surveillance of the process. Usually, control charts are developed for a single study variable, but there exists auxiliary information along with the study variable. Because of the linear relation between the study variable and auxiliary variable, several control chart studies are designed based on the simple linear regression model, but they are restricted to the normally distributed response variable. When the response variable follows an exponential family distribution, then the generalized linear modeling (GLM) approach provides better estimates. Hence, this study is designed to propose GLM‐based control charts when the response variable follows the inverse Gaussian (IG) distribution. In GLM‐based control charts, deviance and Pearson residuals of the IG regression are considered as plotting statistics. For the evaluation purpose, a simulation study is designed, and the performance of the proposed methods is compared with existing counterparts in terms of the run length properties. Moreover, run‐rules are also implemented to gain the efficiency of the Shewhart type GLM‐based control charts under small‐to‐moderate shifts. Finally, an example related to the yarn manufacturing industry is also used to highlight the importance of the stated proposal.

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Alternative methods for the simultaneous monitoring of simple linear profile parameters

Cited by 47 publications

References 35 publications

An Improved Control Chart for Monitoring Linear Profiles and its Application in Thermal Conductivity

An Improved Control Chart for Monitoring Linear Profiles and its Application in Thermal Conductivity

On‐line monitoring of extreme values of geometric profiles in finite horizon processes

GLM‐based control charts for the inverse Gaussian distributed response variable

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