Robust process capability indices for multiple linear profiles

Mehri, Saeed; Ahmadi, Mohammad Mahdi; Shahriari, Hamid; Aghaie, Abdollah

doi:10.1002/qre.2934

Cited by 13 publications

(7 citation statements)

References 23 publications

(59 reference statements)

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“…(Pakzad and Basiri, 2022) introduced a new functional incapability index for dealing with asymmetric tolerances for simple linear profile. In the study of (Mehri et al, 2021) (Mehri et al, 2021), two robust PCIs for multiple linear profiles are proposed. In their study, the process capability is estimated using the M-estimator and the Fast-τ-estimator.…”

Section: Process Capability Analysis For Simple Linear Profiles In Mu...mentioning

confidence: 99%

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Process Capability Analysis for Simple Linear Profiles in Multistage Processes

Adibfar,

Noorossana

2024

Pak.j.stat.oper.res.

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When a process is statistically under control, one may be interested in assessing the process performance based on the specification limits provided by the customer. This evaluation is referred to as process capability analysis. Manufacturing operations are often involved with multistage processes, in which the output of a stage is the input of its subsequent stage. This property is known as the cascade property. Existing methods in capability analysis studies are not applicable when a process or product is represented by profiles. This study presents a method to conduct process capability analysis in a multistage process when quality of a product or process is characterized by a simple linear profile. The performance of the proposed method for a two-stage process is evaluated by numerical simulation using an example from the literature. The results indicate that the proposed method eliminates the effect of the cascade property for different shift sizes and autocorrelations.

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Section: Process Capability Analysis For Simple Linear Profiles In Mu...mentioning

confidence: 99%

“…To define accurate SLs for profile parameters, (Pakzad, 2023) considered both conforming and statistically incontrol profiles in Equation (22). As a result, the SLs for the intercept and the slope parameters are given by Equations ( 25) and (26).…”

Section: -Pcis For Parametersmentioning

confidence: 99%

Process Capability Analysis for Simple Linear Profiles in Multistage Processes

Adibfar,

Noorossana

2024

Pak.j.stat.oper.res.

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“…One of the long-standing and well-known ML methods used in many environmental areas is Linear Regression, applicable to single or multiple variable problems [19][20][21]. Another nonparametric ML method, k-Nearest Neighbor (kNN), has been used chronically in ML literature to solve regression problems [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Machine Learning Methods Application in Temperature Prediction

Azari¹,

Hassan²,

Pierce³

et al. 2022

CRPASE

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Machine Learning (ML) techniques for time series prediction are becoming increasingly accurate and helpful, particularly in considering climate change. As more methods are developed, it follows that differentiating between them is becoming increasingly more important as well. This work took a local temperature time series as a dependent variable and a collection of relevant climatology time series as independent variables and applied leading Machine Learning methods to them. The six methods tested included four simple models: Linear Regression (L.R.), k-Nearest Neighbor (kNN), Support Vector Machine (SVM), and Artificial Neural Network (ANN), In addition of two ensemble model methods: Random Forest (R.F.) and Adaptive Boosting (AdB). Results compared all the method's training and predictive performances to evaluate the method's overall effectiveness in forecasting the average daily temperature value. Actual data was used to train each of the mentioned ML methods, and then they were used to predict the future temperature in the study area. The analysis revealed that out of the six methods tested, the Artificial Neural Network outperformed the others in both training and prediction of temperature values in the Memphis, TN climate.

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“…Two novel robust control charts for monitoring multiple linear profiles were proposed in [9]. Based on the robust estimators used by [9], a robust process capability indices was suggested for multiple linear profiles [10]. New optimizing methods for minimizing the objective function were presented in [11], [12], [13] which could be used in minimizing the loss function.…”

Section: Introductionmentioning

confidence: 99%

Robust Monitoring of Simple Linear Profiles Using M-estimators

Ahmadi¹,

Shahriari²

2022

CRPASE

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In many applications of statistical process control, the quality of a product or a process is described by the relationship between the response variable and one or more independent variables which is called a profile. A profile could be either linear or nonlinear. The control limits of a chart, used to monitor a profile, are functions of model parameters. The classical estimators used to estimate the parameters, are defined under certain hypotheses such as the normality of the error terms. Deviation from any of these assumptions may cause contamination. Whenever contamination exists, the classical estimators are not robust, and the resulting control charts are not accurate when monitoring the profiles. In this research, a robust estimator of the model error term variance is introduced and evaluated using MSE. Then the robust estimators of the slope and the intercept along with the robust estimator of the error term variance are used to define the control limits for the process profile under consideration. Simulation results indicate that the out of control ARL of the proposed control charts is smaller than the ARL of the classical control charts in the presence of contamination.

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Robust process capability indices for multiple linear profiles

Cited by 13 publications

References 23 publications

Process Capability Analysis for Simple Linear Profiles in Multistage Processes

Process Capability Analysis for Simple Linear Profiles in Multistage Processes

Evaluation of Machine Learning Methods Application in Temperature Prediction

Robust Monitoring of Simple Linear Profiles Using M-estimators

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