High-Definition Metrology Based Spatial Variation Pattern Analysis for Machining Process Monitoring and Diagnosis

2009 ◽  
Author(s):  
Hui Wang ◽  
Saumuy Suriano ◽  
Liang Zhou ◽  
S. Jack Hu
Keyword(s):  
Spatial Variation ◽  
Process Monitoring ◽  
Spatial Data ◽  
Control Charts ◽  
Pattern Analysis ◽  
Surface Region ◽  
High Definition ◽  
Statistical Process ◽  
Surface Variation ◽  
And Control

Non-contact high-definition measurement technology, such as laser holographic interferometry, makes it feasible to quickly inspect dimensional variation at micron level, providing up to 2 million data points over a surface area of up to 300×300 mm2. Such high-definition metrology (HDM) data contain rich spatial variation information but it is challenging to utilize this information for process monitoring and control. The spatial distribution of the data is in high-dimensional form and may show nonlinear patterns. Conventional statistical process monitoring and diagnostic schemes based on simple test statistics and linear statistical process models are incapable of capturing the complex surface characteristics as reflected by large amounts of spatial data. This paper develops a framework for efficient monitoring of spatial variation in HDM data using principal curves and quality control charts. Since large scale surface variation patterns (caused by fixturing and part bending) may camouflage those in the smaller scale (generally associated with tooling conditions), it is essential to separate the patterns in these scales and monitor them individually. At each scale, process monitoring is implemented in a sequential manner by monitoring the overall spatial features followed by localized variation identification if an out-of-control condition is detected. To examine the overall spatial characteristics, a principal-component-analysis (PCA) filtered principal curve regression is proposed in conjunction with multivariate control charts whereby nonlinear patterns of spatial data are extracted and monitored. When the overall monitoring indicates a problem, the identification of a surface variation change can be achieved through localized monitoring over each surface region based on variogram pattern analysis and control charts. The location of surface region change provides clues for variation source diagnosis. The proposed method is illustrated using simulated HDM data.

scholarly journals Online monitoring scheme using principal component analysis through Kullback-Leibler divergence analysis technique for fault detection

2019 ◽  
Vol 42 (6) ◽  
pp. 1225-1238 ◽  
Author(s):  
Wahiba Bounoua ◽  
Amina B Benkara ◽  
Abdelmalek Kouadri ◽  
Azzeddine Bakdi
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Process Monitoring ◽  
Control Charts ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Process ◽  
Multivariate Gaussian Distribution ◽  
Tennessee Eastman Process ◽  
Leibler Divergence

Principal component analysis (PCA) is a common tool in the literature and widely used for process monitoring and fault detection. Traditional PCA is associated with the two well-known control charts, the Hotelling’s T2 and the squared prediction error (SPE), as monitoring statistics. This paper develops the use of new measures based on a distribution dissimilarity technique named Kullback-Leibler divergence (KLD) through PCA by measuring the difference between online estimated and offline reference density functions. For processes with PCA scores following a multivariate Gaussian distribution, KLD is computed on both principal and residual subspaces defined by PCA in a moving window to extract the local disparity information. The potentials of the proposed algorithm are afterwards demonstrated through an application on two well-known processes in chemical industries; the Tennessee Eastman process as a reference benchmark and three tank system as an experimental validation. The monitoring performance was compared to recent results from other multivariate statistical process monitoring (MSPM) techniques. The proposed method showed superior robustness and effectiveness recording the lowest average missed detection rate and false alarm rates in process fault detection.

scholarly journals Application of SPC in Short Run and Small Mixed Batch Production: Case of Bakery Equipment Producer

2018 ◽  
Vol 22 (3) ◽  
pp. 55 ◽  
Author(s):  
Darina Juhászová
Keyword(s):  
Control Charts ◽  
Single Case ◽  
Statistical Process ◽  
Pareto Analysis ◽  
Environmental Consequences ◽  
Starting Point ◽  
Short Run ◽  
And Performance ◽  
And Control ◽  
The Impact

<p><strong>Purpose: </strong>The purpose of this paper is to present preliminary research in statistical process control (SPC) of short run and small mixed batches (SR-SMB) at the organization producing bakery equipment.</p><p><strong>Methodology/Approach:</strong> The starting point of the research is a literary survey of possibilities of using SPC for SR-SMB and analysis of the current state of production in a particular organization. Through Pareto analysis, verifying the normality of the data obtained during eleven months, calculation of process capability and performance it was possible to prepare control charts. Finally, the single-case study shows that the proposed control charts are applicable in a small batch and mixed production in the organization producing bakery equipment.</p><p><strong>Findings: </strong>Through SPC implementation in bakery equipment SR-SMB production it is possible to understand the behaviour of the process and to organize better and control the production of expensive precision components.</p><p><strong>Research Limitation/implication:</strong> Limitation of the research is that data have not been reviewed by individual machines and the impact of individual machines and their settings is not displayed separately.</p><strong>Originality/Value of paper:</strong> Using SPC in the bakery equipment industry is far from common practice. The article presents the first part of the research, which is the starting point for more detailed analysis needed to optimize the use of materials, energy and environmental consequences.

CONTROLE ESTATÍSTICO DE PROCESSO (CEP) APLICADO NA COLHEITA MECANIZADA DO SORGO

2018 ◽  
Vol 17 (3) ◽  
pp. 490
Author(s):  
JOÃO PAULO BARRETO CUNHA ◽  
MILA SOUZA CASTRO ◽  
ANDERSON GOMIDE COSTA ◽  
MURILO MACHADO DE BARROS ◽  
TULIO ALMEIDA MACHADO ◽  
...  
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Maximum Level ◽  
Statistical Process ◽  
Forage Sorghum ◽  
Randomized Design ◽  
Completely Randomized Design ◽  
And Control ◽  
The Ideal

RESUMO - A colheita, sendo uma das principais etapas no processo produtivo, precisa manter as perdas dentro de um controle aceitável para que seja possível atingir o máximo nível de qualidade e produtividade. No presente estudo, objetivou-se avaliar as perdas quantitativas durante a colheita mecanizada do sorgo forrageiro por meio do controle estatístico de processo (CEP). O experimento foi arranjado em delineamento inteiramente casualizado (DIC), em que foi realizada a análise de variância para a verificação do efeito significativo da declividade e da velocidade operacional nas perdas, e, quando significativos, foi submetida ao teste de comparação de médias de Tukey a 5% de significância. Cartas sequenciais e cartas de controle para valores individuais e de amplitude móveis foram utilizadas como ferramentas de controle estatístico de processo para verificar o efeito da velocidade operacional nas perdas. Com base nos resultados obtidos é possível indicar que a faixa de velocidade operacional de 4 a 5 km h-1 apresentou a menor variação dos dados, não apresentando nenhum ponto fora do limite de controle, o que lhe conferiu a condição de faixa ideal para colheita. Com base na análise estatística houve maiores perdas no transporte à medida que se aumenta a faixa de declividade do terreno.Palavras-chave: colheita mecanizada, forragicultura, carta de controle, velocidade operacional. STATISTICAL PROCESS CONTROL (SPC) APPLIED IN THE MECHANIZED HARVEST OF SORGHUM  ABSTRACT - Harvesting is one of the main steps in the production process and it is necessary to keep the losses under control in order to reach the maximum level of quality and productivity. The present study aimed to evaluate the quantitative losses during the mechanized harvesting of forage sorghum using the statistical process control (SPC). The experiment was arranged in a completely randomized design (DIC), and analysis of variance was performed to verify the significant effect of declivity and operational velocity on losses, and the significant was submitted to the Tukey test at 5% significance. Sequential charts and control charts for individual and mobile amplitude values, composed of upper and lower control and average limits, were used as statistical process control tools to verify the effect of operational speed on losses. Based on the results obtained it is possible to indicate that the operational velocity range from 4 to 5 km h-1 presented the lowest variation of data, presenting no point outside of the control limit, being the ideal range for harvest. The statistical analysis showed higher losses in transportation as the slope of the terrain increased.Keywords: Mechanized harvesting, forage farming, control charts, operational speeds.

On wavelet-based statistical process monitoring

2020 ◽  
pp. 014233122093570
Author(s):  
Achraf Cohen ◽  
Mohamed Amine Atoui
Keyword(s):  
Process Monitoring ◽  
Control Charts ◽  
Data Availability ◽  
Image Process ◽  
Monitoring Methods ◽  
Statistical Process ◽  
Learning Framework ◽  
Research Areas ◽  
Statistical Process Monitoring ◽  
Effective Contribution

This paper presents an overview of wavelet-based techniques for statistical process monitoring. The use of wavelet has already had an effective contribution to many applications. The increase of data availability has led to the use of wavelet analysis as a tool to reduce, denoise, and process the data before using statistical models for monitoring. The most recent review paper on wavelet-based methods for process monitoring had the goal to review the findings up to 2004. In this paper, we provide a recent reference for researchers and engineers with a different focus. We focus on: (i) wavelet statistical properties, (ii) control charts based on wavelet coefficients, and (iii) wavelet-based process monitoring methods within a machine learning framework. It is clear from the literature that wavelets are widely used with multivariate methods compared to univariate methods. We also found some potential research areas regarding the use of wavelet in image process monitoring and designing control charts based on wavelet statistics, and listed them in the paper.

Characterization of Cutting Force Induced Surface Shape Variation in Face Milling Using High-Definition Metrology1

2013 ◽  
Vol 135 (4) ◽  
Author(s):  
Hai Trong Nguyen ◽  
Hui Wang ◽  
S. Jack Hu
Keyword(s):  
Cutting Force ◽  
Process Monitoring ◽  
Face Milling ◽  
Surface Shape ◽  
Machining Process ◽  
High Definition ◽  
Surface Patterns ◽  
Cutting Processes ◽  
And Control ◽  
The Impact

High-definition metrology (HDM) systems with fine lateral resolution are capable of capturing the surface shape on a machined part that is beyond the capability of measurement systems employed in manufacturing plants today. Such surface shapes can precisely reflect the impact of cutting processes on surface quality. Understanding the cutting processes and the resultant surface shape is vital to high-precision machining process monitoring and control. This paper presents modeling and experiments of a face milling process to extract surface patterns from measured HDM data and correlate these patterns with cutting force variation. A relationship is established between the instantaneous cutting forces and the observed dominant surface patterns along the feed and circumferential directions for face milling. Potential applications of this relationship in process monitoring, diagnosis, and control are also discussed for face milling. Finally a systematic methodology for characterizing cutting force induced surface variations for a generic machining process is presented by integrating cutting force modeling and HDM measurements.

Statistical Process Control on Time Delay Feedback Adjustment Process

2011 ◽  
Vol 211-212 ◽  
pp. 305-309
Author(s):  
Hai Yu Wang
Keyword(s):  
Time Delay ◽  
Control Chart ◽  
Control Charts ◽  
Average Run Length ◽  
Adjustment Process ◽  
Statistical Process ◽  
Controlled Processes ◽  
Monitoring Process ◽  
Set Up ◽  
And Control

Control chart can be designed to quickly detect small shifts in the mean of a sequence of independent normal observations. But this chart cannot perform well for autocorrelated process. The main goal of this article is to suggest a control chart method using to monitoring process with different time delay feedback controlled processes. A quality control model based on delay feedback controlled processes is set up. And the calculating method of average run length of control charts based on process output and control action of multiple steps delay MMSE feedback controlled processes is provided to evaluate control charts performance. A simple example is used to illustrate the procedure of this approach.

scholarly journals Surveillance of Cyclospora cayetanensis Epidemics in USA from Long-Term National Outbreaks Reporting System-Based Monitoring: An Observational Study Using Statistical Process Control Methodologies

2019 ◽  
Vol 2 (1) ◽  
pp. 4-9
Author(s):  
Mostafa Essam Ahmed Mostafa Eissa ◽  
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Reporting System ◽  
Statistical Process ◽  
Pareto Analysis ◽  
Cyclospora Cayetanensis ◽  
The Usa ◽  
One Year ◽  
And Control

Cyclosporiasis epidemics are caused primarily by food contaminated essentially with Cyclospora cayetanensis protozoa from Phylum Apicomplexa. National Outbreaks Reporting System (NORS) provides comprehensive monitoring and records for outbreaks in the USA. The pattern of the microbial epidemics could be investigated using statistical process control (SPC) techniques including Pareto analysis and control charts. The incidence of this outbreak is higher in some states more than others, especially Florida and transmitted mainly through herbal food constituents as a vehicle. Process-behavior charts show disease patterns and trends with the rate of occurrence per day 14.4%. Most of illness cases tend to occur in the summer environment except for March in one-year due spiking in the number of affected individuals during a solitary outbreak episode.

scholarly journals Improving Process Performance of Cotton Spinning by Using Statistical Process Control Techniques

2020 ◽  
pp. 15-22
Author(s):  
Dereje Girma ◽  
Omprakash Sahu
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Shop Floor ◽  
Statistical Control ◽  
Statistical Process ◽  
Process Capability Analysis ◽  
Capability Analysis ◽  
R Process ◽  
And Control

Identifying the presence and understanding the causes of process variability are key requirements for well controlled and quality manufacturing. This pilot study demonstrates the introduction of Statistical Process Control (SPC) methods to the spinning department of a textile manufacturing company. The methods employed included X Bar and R process control charts as well as process capability analysis. Investigation for 29 machine processes identified that none were in statistical control. Recommendations have been made for a repeat of the study using validated data together with practical application of SPC and control charts on the shop floor and extension to all processes within the factory.

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