Modeling method of multivariate statistical control chart for small-batch manufacturing process quality

2009 ◽  
Vol 28 (10) ◽  
pp. 2718-2720
Author(s):  
Gang LI ◽  
Hai-fei DAI
Keyword(s):  
Control Chart ◽  
Manufacturing Process ◽  
Process Quality ◽  
Modeling Method ◽  
Statistical Control ◽  
Multivariate Statistical

Application Of Cusum Chart In Control Of Paper Quality

GIS Business ◽  
2020 ◽  
Vol 14 (6) ◽  
pp. 1062-1069
Author(s):  
S.Ramesh ◽  
B.A.Vasu
Keyword(s):  
Process Parameters ◽  
Manufacturing Process ◽  
Paper Industry ◽  
Primary Data ◽  
Cusum Chart ◽  
Paper Machine ◽  
Statistical Control ◽  
Operating Characteristics ◽  
Small Shift

This paper is an attempt to assess if the manufacturing process of paper machine is in statistical control thereby improving the quality of paper being produced in a paper industry at the time of process itself. Quality is the foremost criteria for achieving the business target. Therefore, emphasis was made on controlling the quality of paper at the time of manufacturing process itself, rather than checking the finished lots at a later time.  This control on quality will help the industry deduct the small shift in the process parameters and modify the operating characteristics at the time of production itself rather than receiving complaints from customers at a later stage.  This paper describes controlling quality at the time of manufacture itself and helps the industry to concentrate on quality at low cost. The researcher has collected primary data at a leading paper industry during October, 2019.  Though X-bar and Range charges were primarily used, CUSUM charts were used to sense the minor shifts in manufacturing process, to explore the possibility of adjusting process parameters during manufacture of paper.

scholarly journals Oversizing Thread Diagnosis in Tapping Operation

Metals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 537
Author(s):  
Alain Gil Del Val ◽  
Fernando Veiga ◽  
Mariluz Penalva ◽  
Miguel Arizmendi
Keyword(s):  
Control Chart ◽  
Manufacturing Systems ◽  
Spindle Motor ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Knowing That ◽  
On Line ◽  
High Level ◽  
Screw Threads ◽  
Multivariate Control

Automotive, railway and aerospace sectors require a high level of quality on the thread profiles in their manufacturing systems knowing that the tapping process is a complex manufacturing process and the last operation in a manufacturing cell. Therefore, a multivariate statistical process control chart, for each tap, is presented based on the principal components of the torque signal directly measured from spindle motor drive to diagnosis the thread profile quality. This on-line multivariate control chart has implemented an alarm to avoid defected screw threads (oversized). Therefore, it could work automatically without any operator intervention assessing the thread quality and the safety is guaranteed during the tapping process.

scholarly journals Monitoring multinomial processes based on a weighted chi-square control chart

2021 ◽  
Vol 28 (3) ◽  
Author(s):  
Achouri Ali ◽  
Emira Khedhiri ◽  
Ramzi Talmoudi ◽  
Hassen Taleb
Keyword(s):  
Quality Improvement ◽  
Control Chart ◽  
Process Improvement ◽  
Average Run Length ◽  
The Other ◽  
Statistical Control ◽  
Chi Square ◽  
Crucial Step ◽  
Run Length ◽  
Control Situation

Abstract: Interpreting an out-of-control signal is a crucial step in monitoring categorical processes. For the Chi-Square Control Chart (CSCC), an out-of control situation does not specify if it was a process deterioration or a process improvement. For this reason, a weighted chi-square statistical control chart WSCC is proposed with different weighting categories in order to enable an accelerated disclosure of a control situation after a shift due to a deterioration of quality and on the other hand, decelerate an out of control situation after a shift due to a quality improvement. Furthermore, in comparison with Marcucci’s method, the new procedure provides an accurate and easier way to interpret several signals. In other words, the WSCC allows a faster detection of an out-of control situation in the case of a quality deterioration, however, an out-of control situation is not quickly detected in the case of a quality improvement. Indeed, comparative studies have been performed to find the best control chart for each combination. Concluding remarks with comments and recommendations are given based on Average Run Length (ARL) and standard deviation run length (SDRL).

The contribution of statistical processes in the control of technological processes

2021 ◽  
Vol 66 (1) ◽  
pp. 5-16
Author(s):  
Olga-Ioana Amariei ◽  
Codruța-Oana Hamat ◽  
Alexandru-Victor Amariei
Keyword(s):  
Quality Control ◽  
Control Chart ◽  
Manufacturing Process ◽  
Quality Characteristic ◽  
Screw Head ◽  
Technological Processes ◽  
Quality Control Chart ◽  
The Way

In this paper, a manufacturing process is analyzed, having as quality characteristic the “height of the screw head”, using analyzes and representative diagrams. Based on this case study, the way to solve these types of problems using the Quality Control Chart module of the WinQSB program, as well as the XLSTAT program is presented.

An unsupervised one-class-classifier support vector machine to simultaneously monitor location and scale of multivariate quality characteristics

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Arijit Maji ◽  
Indrajit Mukherjee
Keyword(s):  
Support Vector Machine ◽  
Control Chart ◽  
Manufacturing Process ◽  
Reference Sample ◽  
Real Life ◽  
Fine Tuning ◽  
Support Vector ◽  
Response Distribution ◽  
Content Type ◽  
One Class Classifier

PurposeThe purpose of this study is to propose an effective unsupervised one-class-classifier (OCC) support vector machine (SVM)-based single multivariate control chart (OCC-SVM) to simultaneously monitor “location” and “scale” shifts of a manufacturing process.Design/methodology/approachThe step-by-step approach to developing, implementing and fine-tuning the intrinsic parameters of the OCC-SVM chart is demonstrated based on simulation and two real-life case examples.FindingsA comparative study, considering varied known and unknown response distributions, indicates that the OCC-SVM is highly effective in detecting process shifts of samples with individual observations. OCC-SVM chart also shows promising results for samples with a rational subgroup of observations. In addition, the results also indicate that the performance of OCC-SVM is unaffected by the small reference sample size.Research limitations/implicationsThe sample responses are considered identically distributed with no significant multivariate autocorrelation between sample observations.Practical implicationsThe proposed easy-to-implement chart shows satisfactory performance to detect an out-of-control signal with known or unknown response distributions.Originality/valueVarious multivariate (e.g. parametric or nonparametric) control chart(s) are recommended to monitor the mean (e.g. location) and variance (e.g. scale) of multiple correlated responses in a manufacturing process. However, real-life implementation of a parametric control chart may be complex due to its restrictive response distribution assumptions. There is no evidence of work in the open literature that demonstrates the suitability of an unsupervised OCC-SVM chart to simultaneously monitor “location” and “scale” shifts of multivariate responses. Thus, a new efficient OCC-SVM single chart approach is proposed to address this gap to monitor a multivariate manufacturing process with unknown response distributions.

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