Performance comparison between on-line sensors and control charts Performance comparison between on-line sensors and control charts in manufacturing process monitoring

Nowadays manufacturing industry faces increasing demands to customize products according to personal needs. This trend leads to a proliferation of complex product designs. To cope with this complexity, manufacturing systems are equipped with advanced sensing capabilities. However, traditional statistical process control methods are not concerned with the stream of in-process imaging data. Also, very little has been done to investigate nonlinearity, irregularity, and inhomogeneity in image stream collected from manufacturing processes. This paper presents the multifractal spectrum and lacunarity measures to characterize irregular and inhomogeneous patterns of image profiles, as well as detect the hidden dynamics of the underlying manufacturing process. Experimental studies show that the proposed method not only effectively characterizes the surface finishes for quality control of ultra-precision machining but also provides an effective model to link process parameters with fractal characteristics of in-process images acquired from additive manufacturing. This, in turn, will allow a swift response to processes changes and consequently reduce the number of defective products. The proposed fractal method has strong potentials to be applied for process monitoring and control in a variety of domains such as ultra-precision machining, additive manufacturing, and biomanufacturing.

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Optical-Based Manufacturing Process: Monitoring and Control

Opto-Mechatronic Systems Handbook ◽

10.1201/9781420040692-21 ◽

2002 ◽

pp. 467-506

Keyword(s):

Process Monitoring ◽

Manufacturing Process ◽

Monitoring And Control ◽

Process Monitoring And Control ◽

And Control

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

ASME 2009 International Manufacturing Science and Engineering Conference, Volume 2 ◽

10.1115/msec2009-84154 ◽

2009 ◽

Cited By ~ 3

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.

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Optical characterization of concentrated dispersions: applications to laboratory analyses and on-line process monitoring and control

Polymer International ◽

10.1002/pi.1231 ◽

2004 ◽

Vol 53 (9) ◽

pp. 1205-1209 ◽

Cited By ~ 67

Author(s):

H Buron ◽

O Mengual ◽

G Meunier ◽

I Cayré ◽

P Snabre

Keyword(s):

Process Monitoring ◽

Optical Characterization ◽

Monitoring And Control ◽

Process Monitoring And Control ◽

On Line ◽

Line Process ◽

And Control ◽

Concentrated Dispersions

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Measurement System Analysis Combined with Shewhart’s Approach

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.637.7 ◽

2015 ◽

Vol 637 ◽

pp. 7-11 ◽

Cited By ~ 13

Author(s):

Magdalena Diering ◽

Adam Hamrol ◽

Agnieszka Kujawińska

Keyword(s):

Process Control ◽

Control Chart ◽

Measurement System ◽

Manufacturing Process ◽

Control Charts ◽

System Analysis ◽

Standard Procedure ◽

Reference Value ◽

Measurement Systems ◽

On Line

The paper presents new procedure of methodology for statistical assessment of measurement systems variation (methodology known in the literature as Measurement Systems Analysis, MSA). This procedure allows for calculation and monitoring in real time (that is on-line) of measurement system (MS) characteristics which determine its usability for manufacturing process control. The presented solution pointed out the gap in process control, which consists in lack of methods for monitoring measurement processes in the on-line way. Their key point consists of taking samples that are also needed for the process control chart for the needs of the MSA method. This means that the samples are taken directly from the production line and during the production process. The method is combined with the standard procedure of statistical process control (SPC) with the use of process control charts. It is based on two control charts. The first one is called AD-chart (Average Difference chart) and it allows to estimate the variation between the operators and stability of the monitored measurement system. The second control chart illustrates the %R&R index (Repeatability and Reproducibility) and allows to monitor the MS capability.The paper also presents authors’ proposal of guidelines about the reference value for the %R&R index calculation and assessment. Recommendations and guidelines for choosing the reference value are based on two criteria: information about sample and manufacturing process variation and the purpose of using MS (product or process control).

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