scholarly journals Fuzzy x- and s Control Charts: A Data-Adaptability and Human-Acceptance Approach

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Ming-Hung Shu ◽  
Dinh-Chien Dang ◽  
Thanh-Lam Nguyen ◽  
Bi-Min Hsu ◽  
Ngoc-Son Phan
Keyword(s):  
Surface Roughness ◽  
Control Charts ◽  
Fuzzy Numbers ◽  
Control Surface ◽  
Process Conditions ◽  
Fuzzy Data ◽  
Size Condition ◽  
Resolution Identity ◽  
And Control ◽  
Control Limits

For sequentially monitoring and controlling average and variability of an online manufacturing process, x¯ and s control charts are widely utilized tools, whose constructions require the data to be real (precise) numbers. However, many quality characteristics in practice, such as surface roughness of optical lenses, have been long recorded as fuzzy data, in which the traditional x¯ and s charts have manifested some inaccessibility. Therefore, for well accommodating this fuzzy-data domain, this paper integrates fuzzy set theories to establish the fuzzy charts under a general variable-sample-size condition. First, the resolution-identity principle is exerted to erect the sample-statistics’ and control-limits’ fuzzy numbers (SSFNs and CLFNs), where the sample fuzzy data are unified and aggregated through statistical and nonlinear-programming manipulations. Then, the fuzzy-number ranking approach based on left and right integral index is brought to differentiate magnitude of fuzzy numbers and compare SSFNs and CLFNs pairwise. Thirdly, the fuzzy-logic alike reasoning is enacted to categorize process conditions with intermittent classifications between in control and out of control. Finally, a realistic example to control surface roughness on the turning process in producing optical lenses is illustrated to demonstrate their data-adaptability and human-acceptance of those integrated methodologies under fuzzy-data environments.

scholarly journals Combining Capability Indices and Control Charts in the Process and Analytical Method Control Strategy

2020 ◽  
Author(s):  
Alexis Oliva ◽  
Matías Llabrés
Keyword(s):  
Control Strategy ◽  
Analytical Method ◽  
Control Charts ◽  
Process Capability ◽  
Real Data ◽  
Process Capability Indices ◽  
Specification Limits ◽  
Capability Indices ◽  
And Control ◽  
Control Limits

Different control charts in combination with the process capability indices, Cp, Cpm and Cpk, as part of the control strategy, were evaluated, since both are key elements in determining whether the method or process is reliable for its purpose. All these aspects were analyzed using real data from unitary processes and analytical methods. The traditional x-chart and moving range chart confirmed both analytical method and process are in control and stable and therefore, the process capability indices can be computed. We applied different criteria to establish the specification limits (i.e., analyst/customer requirements) for fixed method or process performance (i.e., process or method requirements). The unitary process does not satisfy the minimum capability requirements for Cp and Cpk indices when the specification limit and control limits are equal in breath. Therefore, the process needs to be revised; especially, a greater control in the process variation is necessary. For the analytical method, the Cpm and Cpk indices were computed. The obtained results were similar in both cases. For example, if the specification limits are set at ±3% of the target value, the method is considered “satisfactory” (1.22<Cpm<1.50) and no further stringent precision control is required.

Effects of Hull and Control Surface Roughness on Ship Maneuvering

2020 ◽  
pp. 1-10
Author(s):  
John C. Daidola
Keyword(s):  
Surface Roughness ◽  
Equations Of Motion ◽  
Control Surface ◽  
Ship Maneuvering ◽  
Single Screw ◽  
Operation And Maintenance ◽  
Hydrodynamic Derivatives ◽  
Turning Maneuver ◽  
And Control ◽  
Surface Vessel

The effects of hull roughness on ship maneuvering characteristics are investigated. The hydrodynamic derivatives in the equations of motion for surface vessel maneuvering are modified to incorporate roughness of the hull and rudder. Vessel lifetime roughness profiles are postulated based on construction, coatings, operation, and maintenance for a vessel life of 25 years. These are then applied to the turning maneuver for single screw cargo ships with block coefficients from .60 to .80. The implications for naval missions are discussed.

Control Charts and Control Limits

2011 ◽  
Vol 112 (3) ◽  
pp. 736-737 ◽  
Author(s):  
Karthik Raghunathan ◽  
Hani Al-Najjar ◽  
Adam Snavely
Keyword(s):  
Control Charts ◽  
And Control ◽  
Control Limits

Determination of Bore Grinding Machine Parameters to Reduce Cycle Time

2014 ◽  
Vol 974 ◽  
pp. 413-417
Author(s):  
Salakjitt Buddhachakara ◽  
Wipawee Tharmmaphornphilas
Keyword(s):  
Surface Roughness ◽  
Cycle Time ◽  
Control Surface ◽  
Fine Grinding ◽  
Starting Position ◽  
Grinding Machine ◽  
And Control ◽  
Central Composite ◽  
New Machine

This paper applies a central composite design (CCD) to determine proper machine parameters to reduce the cycle time of a bore grinding process. There are 6 machine parameters, which are rough grinding 2 starting position, fine grinding starting position, speed of rough grinding 1, speed of rough grinding 2, speed of rough grinding 3 and speed of fine grinding and 2 types of responses, which are cycle time and surface roughness considered in this study. A half CCD is used to find the optimal machine setup parameters. The experiment shows that new machine conditions can reduce cycle time from 2.98 second per piece to 2.76 second per piece and control surface roughness within specification of 1.0 um. After implementing the new machine conditions in the real setting, we found that the average actual cycle time is 2.76 second per piece with roughness of 0.841 um.

Generalized Hotelling T2 control chart based on bivariate ranked set techniques with runs rules

2021 ◽  
pp. 014233122199267
Author(s):  
Rashid Mehmood ◽  
Muhammad Riaz ◽  
Iftikhar Ali ◽  
Muhammad Hisyam Lee
Keyword(s):  
Control Chart ◽  
Control Charts ◽  
The Other ◽  
Special Cases ◽  
Runs Rules ◽  
And Control ◽  
T2 Control Chart ◽  
Power Curves ◽  
Control Limits

In this study, we have introduced a generalized Hotelling T2 control chart based on bivariate ranked set techniques with runs rules to identify small and moderate variations in a process mean vector. To achieve this aim, plotting statistic and control limits are formulated in generalized approaches. For evaluation purposes, power and power curves are used as performance indicators. Afterwards, power curves are drawn through Monte Carlo simulation procedures by taking into account different choices of factors. A detailed discussion about the role of factors on the performance of the proposed generalized control chart is included. Furthermore, the proposed generalized control chart with double bivariate ranked set techniques is noted to be superb compared to the other cases of single bivariate ranked set techniques. Among single and double versions of bivariate ranked set techniques, the proposed generalized control chart on the basis of median bivariate ranked set techniques is recorded as more efficient relative to the other choices under consideration. Also, comparative analysis shows that the proposed generalized control chart with supplementary runs rules performs outstandingly for detection of small and moderate variations relative to existing control charts. Special cases of the proposed generalized control chart are elaborated to highlight its features for accommodating the existing control charts. To amplify the uses and advantages of the proposed generalized control chart, a real-world example from agriculture is presented.

Demerit-Fuzzy Rating Mechanism and Monitoring Chart

2015 ◽  
Vol 23 (03) ◽  
pp. 399-420 ◽  
Author(s):  
Ming-Hung Shu ◽  
Jan-Yee Kung ◽  
Bi-Min Hsu
Keyword(s):  
Control Charts ◽  
Process Condition ◽  
Relative Magnitude ◽  
Substantial Impact ◽  
Equal Degree ◽  
Fuzzy Ranking ◽  
Severe Degree ◽  
Resolution Identity ◽  
Fuzzy Linguistic ◽  
Control Limits

The relative magnitude of weights for defects has a substantial impact on the performance of attribute control charts. Apparently, the current demerit-chart approach is superior than the c-chart scheme, because it imposes different precise-weights on distinct types of nonconformities, enabling more severe defects to disclose the problems existing in the manufacturing or service processes. However, this crisp-weighting defect assignment, assuming defects are of equal degree of severity when classified into the same defect class, may be so subjective that it leads to the chart somewhat restricted in widespread applications. Since in many cases the severity of each defect is evaluated from practitioners' visual inspection on the key quality characteristics of products or services, when each defect is classified into one of several mutually-exclusive linguistic classes, a fuzzy-weighting defect assignment that represents a degree of seriousness of defects should be allotted in accordance. Therefore, in this paper a demerit-fuzzy rating mechanism and monitoring chart is proposed. We first incorporate a fuzzy-linguistic weight in response to the severe degree of defects. Then, we apply the resolution identity property in construction of fuzzy control limits, and further develop a new fuzzy ranking method in differentiation of the underlying process condition. Finally, the proposed fuzzy-demerit chart is elucidated by an application of TFT-LCD manufacturing processes for monitoring their LCD Mura-nonconformities conditions.

Force Based Model for Automated Surface Roughness Prediction in Slot Milling

2001 ◽  
Author(s):  
A. Sarhan ◽  
A. A. Nasr ◽  
R. M. El-Zahry
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Cutting Force ◽  
Control Surface ◽  
Percentage Error ◽  
Depth Of Cut ◽  
Monitoring And Control ◽  
Slot Milling ◽  
On Line ◽  
And Control

Abstract Study was carried out to analyze the dynamic cutting signals of slot-milling process, in order to design automated on-line tool and surface roughness monitoring strategies, based on indices extracted from these signals, to automatically monitor and control surface roughness in slot milling. Especially designed and manufactured sensitive strain gage dynamometer was used to measure slot-milling radial and tangential forces during milling cycle. The dynamometer was calibrated in static and dynamic ranges. The effect of flank wear width on the magnitude of the cutting force harmonics was constructed as function of axial depth of cut, feed rate per tooth, specific cutting pressure of work material and instantaneous angle of rotation. The results were plotted at various cutting conditions in time and frequency domains. The tool wear was measured in an off-line manner using the tool maker’s microscope and interrelationships of cutting force harmonics and tool wear magnitude were constructed and were used in the computer simulation. Surface roughness was measured using surface meter (Surtronic 3+) with a portable printer. The cutting force signal harmonics were used to establish the proposed force based model to predict the surface roughness of the workpiece machined in slot-milling and examining this system by another experimental tests to define the reliability of the system and to define the percentage error of the system model. Hence, an index named as surface index (S.I) is extracted from ratio between first force amplitude at first significant frequency and first surface amplitude at the same frequency, to predict the surface roughness of the workpiece machined in slot-milling. This is to be employed in automated on-line quality management (monitoring and control) strategy.

Different methods to fuzzy X¯-R control charts used in production

2018 ◽  
Vol 31 (6) ◽  
pp. 848-866 ◽  
Author(s):  
Hatice Ercan Teksen ◽  
Ahmet Sermet Anagun
Keyword(s):  
Fuzzy Control ◽  
Fuzzy Sets ◽  
Control Charts ◽  
Fuzzy Numbers ◽  
Quality Characteristic ◽  
Content Type ◽  
Fuzzy Methods ◽  
Interval Type ◽  
Control Limits

PurposeThe control charts are used in many production areas because they give an idea about the quality characteristic(s) of a product. The control limits are calculated and the data are examined whether the quality characteristic(s) is/are within these limits. At this point, it may be confusing to comment, especially if it is slightly below or above the limit values. In order to overcome this situation, it is suitable to use fuzzy numbers instead of crisp numbers. The purpose of this paper is to demonstrate how to create control limits ofX¯-R control charts for a specified data set of interval type-2 fuzzy sets.Design/methodology/approachThere are methods in the literature, such as defuzzification, distance, ranking and likelihood, which may be applicable for interval type-2 fuzzy set. This study is the first that these methods are adapted to theX¯-R control charts. This methodology enables interval type-2 fuzzy sets to be used inX¯-R control charts.FindingsIt is demonstrated that the methods – such as defuzzification, distance, ranking and likelihood for interval type-2 fuzzy sets – could be applied to theX¯-R control charts. The fuzzy control charts created using the methods provide similar results in terms of in/out control situations. On the other hand, the sample points depicted on charts show similar pattern, even though the calculations are different based on their own structures. Finally, the control charts obtained with interval type-2 fuzzy sets and the control charts obtained with crisp numbers are compared.Research limitations/implicationsBased on the related literature, research works on interval type-2 fuzzy control charts seem to be very limited. This study shows the applicability of different interval type-2 fuzzy methods onX¯-R control charts. For the future study, different interval type-2 fuzzy methods may be considered forX¯-R control charts.Originality/valueThe unique contribution of this research to the relevant literature is that interval type-2 fuzzy numbers for quantitative control charts, such asX¯-R control charts, is used for the first time in this context. Since the research is the first adaptation of interval type-2 fuzzy sets onX¯-R control charts, the authors believe that this study will lead and encourage the people who work on this topic.

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