Process capability analyses based on fuzzy measurements and fuzzy control charts

2011 ◽  
Vol 38 (4) ◽  
pp. 3172-3184 ◽  
Author(s):  
İhsan Kaya ◽  
Cengiz Kahraman
Keyword(s):  
Fuzzy Control ◽  
Control Charts ◽  
Process Capability

scholarly journals Construction of Fuzzy Control Chart with Multinomial Quality using Process Capability

2019 ◽  
Vol 9 (1) ◽  
pp. 1460-1465
Keyword(s):  
Fuzzy Control ◽  
Control Charts ◽  
Multinomial Distribution ◽  
Process Capability ◽  
Fuzzy Sets Theory ◽  
Statistical Process ◽  
Quantitative Form ◽  
The Times ◽  
Sets Theory ◽  
Control Limits

Control charts are the effective and quietest form of statistical process control methods. Many of the times, data are obtained in quantitative form; however there are many quality characteristics that cannot be expressed in numerical measure, such as characteristics for appearance, smoothness and colour, etc. Fuzzy sets theory is an impressive mathematical methodology to evaluate the vagueness related uncertainty that can linguistically express data in these situations. In this paper, we construct a fuzzy control limits under fixed and varying sample size with various quality levels for observing a manufacturing process based on the multinomial distribution using degrees of membership and process capability.

scholarly journals Indian Food Specialties Limited

2002 ◽  
Vol 27 (1) ◽  
pp. 55-68
Author(s):  
Satish Y Deodhar ◽  
Devanath Tirupati
Keyword(s):  
Quality Control ◽  
Control Charts ◽  
Hazard Analysis ◽  
Process Capability ◽  
Process Capability Indices ◽  
Market Conditions ◽  
Quality Control Charts ◽  
Critical Control Points ◽  
Capability Indices ◽  
Indian Food

Indian Food Specialties Limited (IFS) introduced tools of food quality management in May 2000 in response to changing market conditions and poor profitability. Spoilage in the production process was very high and the company had incurred losses for three successive years starting from 1996-97. The company addressed quality concerns by introducing management tools such as quality control charts and process capability indices, and was considering implementation of a food safety system called Hazard Analysis and Critical Control Points (HACCP). The case describes the changing market conditions and the company's response to improving quality, and provides a learning exercise on quality control charts, process capability indices, and HACCP.

scholarly journals Shewhart's control charts and process capability ratio applied to a sewage treatment station

2014 ◽  
Vol 34 (4) ◽  
pp. 770-779 ◽  
Author(s):  
Fábio Orssatto ◽  
Marcio A. Vilas Boas ◽  
Ricardo Nagamine ◽  
Miguel A. Uribe-Opazo
Keyword(s):  
Control Charts ◽  
Suspended Solids ◽  
Sewage Treatment ◽  
Oxygen Demand ◽  
Process Capability ◽  
Total Suspended Solids ◽  
Statistical Control ◽  
Environmental Legislation ◽  
Shewhart Control Charts ◽  
Shewhart Control

The current study used statistical methods of quality control to evaluate the performance of a sewage treatment station. The concerned station is located in Cascavel city, Paraná State. The evaluated parameters were hydrogenionic potential, settleable solids, total suspended solids, chemical oxygen demand and biochemical oxygen demand in five days. Statistical analysis was performed through Shewhart control charts and process capability ratio. According to Shewhart charts, only the BOD(5.20) variable was under statistical control. Through capability ratios, we observed that except for pH the sewage treatment station is not capable to produce effluents under characteristics that fulfill specifications or standard launching required by environmental legislation.

scholarly journals A Fuzzy Control Chart Approach for Attributes and Variables

2018 ◽  
Vol 8 (5) ◽  
pp. 3360-3365 ◽  
Author(s):  
N. Pekin Alakoc ◽  
A. Apaydin
Keyword(s):  
Quality Control ◽  
Fuzzy Control ◽  
Control Chart ◽  
Control Charts ◽  
Fuzzy Numbers ◽  
Fuzzy Theory ◽  
New Approach ◽  
Shewhart Control Charts ◽  
Shewhart Control ◽  
Quality Control Chart

The purpose of this study is to present a new approach for fuzzy control charts. The procedure is based on the fundamentals of Shewhart control charts and the fuzzy theory. The proposed approach is developed in such a way that the approach can be applied in a wide variety of processes. The main characteristics of the proposed approach are: The type of the fuzzy control charts are not restricted for variables or attributes, and the approach can be easily modified for different processes and types of fuzzy numbers with the evaluation or judgment of decision maker(s). With the aim of presenting the approach procedure in details, the approach is designed for fuzzy c quality control chart and an example of the chart is explained. Moreover, the performance of the fuzzy c chart is investigated and compared with the Shewhart c chart. The results of simulations show that the proposed approach has better performance and can detect the process shifts efficiently.

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.

scholarly journals Process Capability Analysis in a Polypropylene Bag Manufacturing Company

2021 ◽  
Vol 25 (8) ◽  
pp. 1477-1482
Author(s):  
O.F. Odeyinka ◽  
F.O. Ogunwolu ◽  
O.P. Popoola ◽  
T.O. Oyedokun
Keyword(s):  
Control Charts ◽  
Process Capability ◽  
Production Plant ◽  
Specification Limit ◽  
Process Capability Analysis ◽  
Capability Analysis ◽  
Vertically Aligned ◽  
Lower Specification Limit ◽  
And Control

Process capability analysis combines statistical tools and control charts with good engineering judgment to interpret and analyze the data representing a process. This work analyzes the process capability of a polypropylene bag producing company. The case study organization uses two plants for production and data was collected over a period of nine months for this study. Analysis showed that the output spread of plant 1 was greater than the specification interval spread which implies poor capability. There are non-conforming parts below the Lower Specification Limit (LSL: 500,000 metres) and above the Upper Specification Limit (USL: 600,000 metres) and that the output requires improvement. Similarly, the capability analysis of plant 2 shows that the overall output spread is greater than the specification interval spread (poor capability). The output centre in the specification and overall interval are vertically aligned, thus specifying that the output from plant 2 is also process centered and requires improvement. Recommendations were made to improve the outputs from each production plant.

Fuzzy U control chart based on fuzzy rules and evaluating its performance using fuzzy OC curve

2018 ◽  
Vol 30 (3) ◽  
pp. 232-247 ◽  
Author(s):  
Somayeh Fadaei ◽  
Alireza Pooya
Keyword(s):  
Fuzzy Control ◽  
Sample Size ◽  
Control Chart ◽  
Control Charts ◽  
Operating Characteristic ◽  
Fuzzy Rules ◽  
Type Ii ◽  
Type Ii Error ◽  
Content Type ◽  
Variable Sample Size

Purpose The purpose of this paper is to apply fuzzy spectrum in order to collect the vague and imprecise data and to employ the fuzzy U control chart in variable sample size using fuzzy rules. This approach is improved and developed by providing some new rules. Design/methodology/approach The fuzzy operating characteristic (FOC) curve is applied to investigate the performance of the fuzzy U control chart. The application of FOC presents fuzzy bounds of operating characteristic (OC) curve whose width depends on the ambiguity parameter in control charts. Findings To illustrate the efficiency of the proposed approach, a practical example is provided. Comparing performances of control charts indicates that OC curve of the crisp chart has been located between the FOC bounds, near the upper bound; as a result, for the crisp control chart, the probability of the type II error is of significant level. Also, a comparison of the crisp OC curve with OCavg curve and FOCα curve approved that the probability of the type II error for the crisp chart is more than the same amount for the fuzzy chart. Finally, the efficiency of the fuzzy chart is more than the crisp chart, and also it timely gives essential alerts by means of linguistic terms. Consequently, it is more capable of detecting process shifts. Originality/value This research develops the fuzzy U control chart with variable sample size whose output is fuzzy. After creating control charts, performance evaluation in the industry is important. The main contribution of this paper is to employs the FOC curve for evaluating the performance of the fuzzy control chart, while in prior studies in this area, the performance of fuzzy control chart has not been evaluated.

Optimal performance of plastic pipes’ extrusion process using Min-Max model in fuzzy goal programming

2015 ◽  
Vol 231 (4) ◽  
pp. 888-898 ◽  
Author(s):  
Abbas Al-Refaie
Keyword(s):  
Goal Programming ◽  
Control Charts ◽  
Process Capability ◽  
Extrusion Process ◽  
Fuzzy Goal Programming ◽  
Statistical Control ◽  
Capability Index ◽  
Plastic Pipes ◽  
Fuzzy Goal ◽  
Process Factors

The main objective of this research is to optimize performance of plastic pipes’ extrusion process with two main quality responses, including pipe’s diameter and thickness, using Min-Max model in fuzzy goal programming. First, the variables control charts are constructed at initial factor settings of extrusion process, where the results reveal that the extrusion process is in statistical control. However, the actual capability index values for diameter and thickness are estimated 0.7094 and 0.7968, respectively. The process capability for a complete product, MCpk, is calculated as 0.752. These values indicate that the extrusion process is incapable. To improve process performance, the L18 array is utilized for experimental design with three 3-level process factors. Then, the Min-Max model is used to determine the combination of optimal factor settings. The estimated capability index values for diameter and thickness at the combination of optimal factor settings are estimated and found to be 1.504 and 1.879, respectively. The integrated process capability index, MCpk, is calculated as 1.681. Confirmation experiments should that the Min-Max model results in enhancing process capability for both responses. In conclusions, the Min-Max Model may provide valuable assistance to practitioners in optimizing performance while considering both product and process preferences.

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