Current research on profile monitoring

Woodall, William H.

doi:10.1590/s0103-65132007000300002

Cited by 266 publications

(173 citation statements)

References 35 publications

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“…Kang and Albin (2000) put forward a quality control method of semiconductor manufacturing process based on the linear relationship between pressure and the rate of flow. Woodall et al (2004) and Woodall (2007) gave a broad review of current research on profile monitoring and suggested that the monitoring of the linear profile can be replaced by monitoring linear regression parameters using univariate or multivariate control charts. The estimated regression parameters include the slope, y-intercept and the variance of the errors.…”

Section: Introductionmentioning

confidence: 99%

Profile monitoring based quality control method for fused deposition modeling process

Zhang

Hong

2018

J Intell Manuf

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In order to monitor the quality of parts in printing, the methodology to monitor the geometric quality of the printed parts in fused deposition modeling process is researched. A non-contact measurement method based on machine vision technology is adopted to obtain the precise complete geometric information. An image acquisition system is established to capture the image of each layer of the part in building and image processing technology is used to obtain the geometric profile information. With the above information, statistical process control method is applied to monitor the geometric quality of the parts during the printing process. Firstly, a border signature method is applied to transform complex geometry into a simple distance-angle function to get the profile deviation data. Secondly, monitoring of the profile deviation data based on profile monitoring method is studied and applied to achieve the goal of layer-to-layer monitoring. In the research, quantile-quantile plot method is used to transform the profile deviation point cloud data monitoring problem into a linear profile relationship monitoring problem and EWMA control charts are established to monitor the parameters of the linear relationship to detect shifts occurred in the Fused Deposition Modeling process. Finally, laboratory experiments are conducted to demonstrate the effectiveness of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Profile monitoring based quality control method for fused deposition modeling process

Zhang

Hong

2018

J Intell Manuf

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“…In di erent applications, simple linear regression, multiple linear or polynomial regression, or even more complicated models such as nonlinear regressions are used to model the quality of processes. The applications of pro le monitoring in the literature have been introduced by several authors such as Kang Woodall et al [3], Wang and Tsung [4], Woodall [5], Zou et al [6], and Amiri et al [7]. A pro le monitoring problem consists of two phases, including Phase I and Phase II.…”

Section: Introductionmentioning

confidence: 99%

Maximum Multivariate Exponentially Weighted Moving Average and Maximum Multivariate Cumulative Sum Control Charts for Simultaneous Monitoring of Mean and Variability of Multivariate Multiple Linear Regression Profiles

Ghashghaei

Amiri

2017

Scientia Iranica

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Abstract. In some applications, quality of product or performance of a process is described by some functional relationships among some variables known as multivariate linear pro le in the literature. In this paper, we propose Max-MEWMA and Max-MCUSUM control charts for simultaneous monitoring of mean vector and covariance matrix in multivariate multiple linear regression pro les in Phase II. The proposed control charts also have the ability to diagnose whether the location or variation of the process is responsible for out-of-control signal. The performance of the proposed control charts is compared with that of the existing method through Monte-Carlo simulations. Finally, the applicability of the proposed control charts is illustrated using a real case of calibration application in the automotive industry.

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“…Other examples of the processes with profile output can be found in (Mestek et al, 1994;Stover and Brill, 1998). Besides, Woodall (2007) considered some processes with profile output in lumber manufacturing, monitoring of shapes and public health surveillance. The term profile monitoring refers to control of such processes, which is performed during two phases.…”

Section: Introductionmentioning

confidence: 99%

Designing Statistical Test for Mean of Random Profiles

Bahri¹,

Hadi-Vencheh²

2016

Industrial Engineering and Management Systems

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A random profile is the result of a process, the output of which is a function instead of a scalar or vector quantity. In the nature of these objects, two main dimensions of "functionality" and "randomness" can be recognized. Valuable researches have been conducted to present control charts for monitoring such processes in which a regression approach has been applied by focusing on "randomness" of profiles. Performing other statistical techniques such as hypothesis testing for different parameters, comparing parameters of two populations, ANOVA, DOE, etc. has been postponed thus far, because the "functional" nature of profiles is ignored. In this paper, first, some needed theorems are proven with an applied approach, so that be understandable for an engineer which is unfamiliar with advanced mathematical analysis. Then, as an application of that, a statistical test is designed for mean of continuous random profiles. Finally, using experimental operating characteristic curves obtained in computer simulation, it is demonstrated that the presented tests are properly able to recognize deviations in the null hypothesis.

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Current research on profile monitoring

Cited by 266 publications

References 35 publications

Profile monitoring based quality control method for fused deposition modeling process

Profile monitoring based quality control method for fused deposition modeling process

Maximum Multivariate Exponentially Weighted Moving Average and Maximum Multivariate Cumulative Sum Control Charts for Simultaneous Monitoring of Mean and Variability of Multivariate Multiple Linear Regression Profiles

Designing Statistical Test for Mean of Random Profiles

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