α - CUT FUZZY CONTROL CHARTS FOR BOTTLE BURSTING STRENGTH DATA

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1 International Journal of Electronics, Communication & Instrumentation Engineering Research and Development (IJECIERD ISSN X Vol. 2 Issue 4 Dec TJPRC Pvt. Ltd., α - CUT FUZZY CONTROL CHARTS FOR BOTTLE BURSTING STRENGTH DATA 1 A. SARAVANAN & 2 P. NAGARAJAN 1 Assistant Professor, Department of Instrumentation Technology, MSRIT, Bangalore, India 2 Associate Professor, Department of Chemical Engineering, Annamalai University, India ABSTRACT Quality has become one of the most important consumer decision factors in the selection among competing products and services. Statistical Process Control (SPC is a technique applied towards improving the quality of characteristics by monitoring the process under study continuously, in order to detect assignable causes and take required actions as quickly as possible. A traditional variable control chart consists of three lines namely Center Line (average value Upper Control Limit and Lower Control Limit (other two horizontal lines. These limits are represented by the numerical values. The process is either in-control or out-of-control depending on numerical observations. For many problems, control limits could not be so precise. Uncertainty comes from the measurement system including operators and gauges and environmental conditions. In this situation, fuzzy set theory is a useful tool to handle this uncertainty. Fuzzy control limits provide a more accurate and flexible evaluation. In this paper, the fuzzy α cut control charts are constructed and applied in bottle bursting strength data. KEYWORDS: Statistical Process Control, Fuzzy s, -cut and - Level Fuzzy Midrange INTRODUCTION Statistical Process Control (SPC is used to monitor the process stability which ensures the predictability of the process. Control charts are viewed as the most commonly applied SPC tools. A control chart consists of three horizontal lines called; Upper Control Limit (UCL, Center Line (CL and Lower Control Limit (LCL. The center line in a control chart denotes the average value of the quality characteristic under study. If a point lies within UCL and LCL, then the process is deemed to be under control. Otherwise, a point plotted outside the control limits can be regarded as evidence representing that the process is out of control and, hence preventive or corrective actions are necessary in order to find and eliminate the assignable cause or causes, which subsequently result in improving quality characteristics [7]. The control chart may be classified into two types namely variable and attribute control charts. The fuzzy set theory was first introduced by Zadeh and studied by many authors [2], [3], [4], [5]. It is mostly used when the data is attribute in nature and these types of data may be expressed in linguistic terms such as very good, good, medium, bad and very bad. The measures of central tendency in descriptive Statistics are used in variable control charts. These measures can be used to convert fuzzy sets into scalars which are fuzzy mode, -level fuzzy midrange, and fuzzy median and fuzzy average. There is no theoretical basis to select the appropriate fuzzy measures among these four. The objective of this study is first to construct the fuzzy and control charts with α cuts by using α -level fuzzy midrange. The following procedures are used to construct the fuzzy and control charts.

2 18 A. Saravanan & P. Nagarajan 1. First transform the traditional and control charts to fuzzy control charts. To obtain fuzzy and control charts, the trapezoidal fuzzy number (a, b, c, d are used. 2. The cut fuzzy control charts and cut fuzzy control charts are developed by using cut approach. 3. The -level fuzzy and midrange for fuzzy control charts are calculated by using - level fuzzy midrange transformation techniques 4. Finally, the application of control charts is highlighted by using bottle bursting strength data. FUZZY TRANSFORMATION TECHNIQUES Mainly four fuzzy transformation techniques, which are similar to the measures of central tendency, used in descriptive statistics: - level fuzzy midrange, fuzzy median, fuzzy average, and fuzzy mode are used. In this paper, among the above four transformation techniques, the - level fuzzy midrange transformation technique is used for the construction of fuzzy and control charts based on fuzzy trapezoidal number. - LEVEL FUZZY MIDRANGE This is defined as the midpoint of the ends of the - level cuts, denoted by, is a non fuzzy set that comprises all elements whose membership is greater than or equal to. If and are the end points of, then ( In fact, the fuzzy mode is a special case of - level fuzzy midrange when =1. - level fuzzy midrange of sample j, is used to transform the fuzzy control limits into scalar and is determined as follows. FUZZY CONTROL CHART BASED ON RANGES In monitoring the production process, the control of process averages or quality level is usually done by charts. The process variability or dispersion can controlled by either a control chart for the range, called R chart, or a control chart for the standard deviation, called S chart. In this section, fuzzy control charts are introduced based on fuzzy trapezoidal number. The fuzzy control charts are presented in the next section. Montgomery [7] has proposed the control limits for control chart based on sample range is given below Where is a control chart co-efficient and is the average of Ri that are the ranges of samples. In the case of fuzzy control chart, each sample or subgroup is represented by a trapezoidal fuzzy number (a, b, c, d as shown in Fig. 1.

3 α - Cut Fuzzy s for Bottle Bursting Strength Data 19 In this study, trapezoidal fuzzy numbers are represented as (, for each observation. Note that a trapezoidal fuzzy number becomes triangular when b=c. For the case of representation and calculation, a triangular fuzzy number is also represented as a trapezoidal fuzzy number by (a, b, b, d or (a, c, c, d.the center line C is the arithmetic mean of the fuzzy sample means, which are represented by (.Here are called the overall means and is calculated as follows. ; r =a,b,c,d; i=1,2,3,.n ; j =1,2,3, m. ; r=a,b,c,d; j=1,2,3 m. =( = {,,, } Where n is the fuzzy sample size, m is the number of fuzzy samples and is the center line for fuzzy control chart. Control Limits for Fuzzy By using the traditional control chart procedure, the control limits of fuzzy control charts with ranges based on fuzzy trapezoidal number are calculated as follows = + = ( + A 2 ( = ( = ( = ( C - = ( A 2 ( = ( Where ; r=a,b,c,d; j=1,2,3 m the proceduce for calculating is as follows j= 1, 2, 3,.m. Where ( is the maximum fuzzy number in the sample and

4 20 A. Saravanan & P. Nagarajan ( is the minimum fuzzy number in the sample. Fig.1: Representation of a Sample by Trapezoidal Fuzzy Numbers Control Limits for α- Cut Fuzzy Introducing the α - cut procedure to the above fuzzy control limits, it can be rewritten as follows (the value of α can be selected according to the nature of the given problem and the selected α value must should lies between0 and 1 = ( + A 2 ( = ( = ( = ( - A 2 ( = ( Where a α = a+ α(b a ; d α = d+ α(d c The α - cut fuzzy control limits based on ranges are shown in fig.2 Fig.2: α - Cut Fuzzy Based on Ranges using Fuzzy Trapezoidal Number

5 α - Cut Fuzzy s for Bottle Bursting Strength Data 21 α - Level Fuzzy Midrange for α- Cut Fuzzy Based on Ranges The α - level fuzzy midrange is one of the transformation techniques (among the four used to transform the fuzzy set into scalar. It is used to check the production process, whether the process is in-control or out-of-control. The control limits for α - level fuzzy midrange for α -Cut Fuzzy follows. control chart based on ranges can be obtained as The definition of α - level fuzzy midrange of sample j for fuzzy control chart is Then, the condition of process control for each sample can be defined as: Process control = {in control; for Out of control; otherwise} FUZZY CONTROL CHART The control limits for Shewhart R control chart is given by UCL R = D 4 ; CL R = ; UCL R = D 3 Where and are control chart co-efficient [6]. By using the traditional R control chart procedure, the control limits for fuzzy control chart with trapezoidal fuzzy number is obtained as follows. Control Limits for α Cut Fuzzy The control limits of α - cut fuzzy control chart based on trapezoidal fuzzy numbers are obtained as follows

6 22 A. Saravanan & P. Nagarajan α - Level Fuzzy Midrange for α - Cut Fuzzy The control limits of α - Level fuzzy midrange for α - Cut Fuzzy Control chart based on fuzzy Trapezoidal number can be calculated as follows The definition of α - level fuzzy midrange of sample j for fuzzy control chart can be calculated as follows Then, the condition of process control for each sample can be defined as: Process control ={ in control; for Out of control; otherwise} FUZZY CONTROL CHART BASED ON STANDARD DEVIATION The R chart is used to monitor the dispersion associated with a quality characteristic. Its simplicity of construction and maintenance make the R chart very commonly used and the range is a good measure of variation for small subgroup sizes. When the sample size increases (n>10, the utility of the range as a measure of dispersion falls off and the standard deviation measure is preferred (Montgomery 2002 The Shewhart chart based on standard deviation is given below Where is a control chart co-efficient (Kolarik 1995 The value of is = Where is the standard deviation of sample j and is the average of s.

7 α - Cut Fuzzy s for Bottle Bursting Strength Data 23 Fuzzy Based on Standard Deviation The theoretical structure of fuzzy control chart and fuzzy control chart has been developed by Senturk and Erginel (2009. The fuzzy is the standard deviation of sample j and it is calculated as follows and the fuzzy average is calculated by using standard deviation represented by the following Trapezoidal fuzzy number = {, }=( And the control limits of fuzzy control chart based on standard deviation are defined as follows = + = ( +, = ( = ( =( C - = ( -, = ( Control Limits for α Cut Fuzzy The control limits for α - Cut Fuzzy control chart based on standard deviation are obtained as follows = ( +, = ( = ( =( ( -, = (

8 24 A. Saravanan & P. Nagarajan Where α - Level Fuzzy Midrange for α - Cut Fuzzy Based on Standard Deviation The control limits and centre line for α - Cut Fuzzy midrange are control chart based on standard deviation using α Level fuzzy The definition of α - level fuzzy midrange of sample j for fuzzy control chart is Then, the condition of process control for each sample can be defined as: Process control = {in control; for Out of control;otherwise } FUZZY CONTROL CHART The control limits for Shewhart control chart is given by Where and are control chart co-efficient. Then the Fuzzy control chart limits can be obtained as follows α - Cut Fuzzy The control limits of α - Cut Fuzzy control chart can be obtained as follows:

9 α - Cut Fuzzy s for Bottle Bursting Strength Data 25 α - Level Fuzzy Midrange for α - Cut Fuzzy The control limits of α - Level fuzzy midrange for α - Cut Fuzzy control chart can be obtained in a similar way to α - Cut Fuzzy control chart. The definition of α - level fuzzy midrange of sample j for fuzzy control chart can be calculated as follows Then, the condition of process control for each sample can be defined as: Decision ={ in control; for Out of control; otherwise } Application: Different Observation data for Bottle bursting strength have been considered with 10 samples. Fuzzy control limits are calculated according to the procedures given in the previous section. For n=5, A 2 = Where A 2 is obtained from the coefficients table for variable control charts Sa mp le no Table: The values for r and is given below, where r = a, b, c, d

10 26 A. Saravanan & P. Nagarajan (Note: Refer To Appendices Fuzzy Based on Range By using the above and, the control limits of fuzzy control charts with ranges based on fuzzy trapezoidal number are calculated as follows =C + = ( + A 2 =(240.42,287.64,263.1, (31.9, 54.1, 86.8, 96.7 = ( = (258.82, , , = ( = ( = (240.42, , 263.1, C - = ( - A 2 = (240.42, , 263.1, (31.9,54.1,86.8,96.7 = ( = (220.02, , , α - Cut Fuzzy Based on Ranges α - Cuts in the control limits provide the ability of determining the tightness of the sampling process. α - Level can be selected according to the nature of the production process. α - level was defined as 0.6 this production process = = d+ α (d c = = ( + = (268.75,287.64,263.1, (45.22,54.1,86.8,90.76 = (

11 α - Cut Fuzzy s for Bottle Bursting Strength Data 27 = (294.84,318.85,313.19,315.9 = ( = ( = (268.75,287.64,263.1, ( - = (268.75,287.64, (45.22,54.1,86.8,90.76 = ( = (242.66, , , α - LEVEL FUZZY MIDRANGE FOR α CUT FUZZY CONTROL CHART BASED ON RANGES The control limits for α - level fuzzy midrange for - α Cut Fuzzy follows control chart based on ranges can be obtained as = [ ] = = = = [ ] = FUZZY CONTROL CHART = (67.46, , , = (31.9, 54.1, 86.8, 96.7 = (0, 0, 0, 0 Where, n =5, and are obtained from the coefficients table for variable control charts. α Cut Fuzzy The control limits of α - cut fuzzy control chart based on trapezoidal fuzzy numbers are obtained as follows

12 28 A. Saravanan & P. Nagarajan = (95.6, , 183.5, = (45.22, 54.1, 86.8, = (0, 0, 0, 0 α - LEVEL FUZZY MIDRANGE FOR α - CUT FUZZY CONTROL CHART The control limits of α - Level fuzzy midrange for α - Cut Fuzzy Control chart based on fuzzy Trapezoidal number can be calculated as follows = 2.115[ ] = = = 0 The values of and have been calculated by using the formula of α - Level fuzzy midrange for α - Cut Fuzzy control chart based on ranges and α - Level fuzzy midrange for α - Cut Fuzzy control chart respectively and the values are given in Table 2. Control Limits using α- Level Fuzzy Mid Range for α -cut Fuzzy Fuzzy Mid Range for α -Cut Fuzzy Based on Ranges and α- Level Table: 2 Sample No CONCLUSIONS In Control 82.4 In Control In Control 63.6 In Control In Control 70 In Control In Control 73.4 In Control In Control 74.1 In Control In Control 71.8 In Control In Control 70 In Control In Control 50.2 In Control In Control 57 In Control In Control 67.4 In Control This paper shows that this process was in control with respect to and for each sample as shown in table 2. So, these control limits can be used to control the production process. Since the Plotted values are close to the control limits.fuzzy observations & Fuzzy control limits can provide more flexibility for controlling a process. The

13 α - Cut Fuzzy s for Bottle Bursting Strength Data 29 α - Level fuzzy midrange transformation techniques are used to illustrate applications in a production process. The methodology can be extended to variable samples for production processes. REFERENCES 1. A.Pandurangan,R.Varadharajan. ( Cheng, C.B. (2005. Fuzzy Process Control: Construction of control charts with fuzzy number. Fuzzy Sets and Systems, 154, El Shal, S. M., Morris A. S. (2000. A fuzzy rule -based algorithm to improve the performance of statistical process control in quality Systems, Journal of Intelligent Fuzzy Systems, 9, Gulbay, M., Kahraman, C and Ruan D. (2004. α - Cut fuzzy control charts for linguistic data.international Journal of Intelligent Systems, 19, Gulbay, M and Kahraman, C. (2006. Development of fuzzy process control charts and fuzzy unnatural pattern analysis. Computational Statistics and Data Analysis, 51, Gulbay, M and Kahraman, C. (2006. An alternative approach to fuzzy control charts: direct fuzzy approach.information Sciences, 77(6, Kolarik, W.J, (1995. Creating Quality- Concepts, Systems Strategies and Tools, McGraw Hill. 8. Montgomery, D.C., (2002. Introduction to Statistical Quality Control, John Wiley and Sons, New York 9. Rowlands, H and Wang, L.R (2000. An approach of fuzzy logic evaluation and control in SPC. Quality Reliability Engineering Intelligent, 16, Sentruk, S and Erginel, N. (2009. Development of Fuzzy and charts using α- cuts. Information APPENDIX Sciences, 179(10, The fuzzy ranges for the ; r = a, b, c, d values for the 10 samples are calculated as follows 1. = = 53 = = 36 = = 102 = = = = 26 = = 42 = = 84 = = = =86 = = 43 = =89 = = = = 2 = = 50 = = 104

14 30 A. Saravanan & P. Nagarajan = = = = 42 = = 51 = = 104 = = = = 10 = = 53 = = 113 = = = = 16 = = 59 = = 79 = = = = 14 = = 65 = = 49 = = = = 15 = = 67 = = 71 = = = =55 = = 75 = = 73 = = 60

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