Desinging dsp (0, 1) acceptance sampling plans based on

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 01 | Jan-2014, Available @ http://www.ijret.org 116
DESINGING DSP (0, 1) ACCEPTANCE SAMPLING PLANS BASED ON
TRUNCATED LIFE TESTS UNDER VARIOUS DISTRIBUTIONS USING
MINIMUM ANGLE METHOD
A. R. Sudamani Ramaswamy1
, R. Sutharani2
1
Associate Professor, Department of Mathematics, Avinashilingam University, Coimbatore – 641043(T.N), India
2
Assistant professor, Department of Mathematics, Coimbatore Institute of Technology, Coimbatore-641014(T.N), India.
Abstract
In this paper DSP (0, 1) sampling plans for truncated life tests are developed using minimum angle method, when the life time of the
items follows some selected distributions. The design parameters of the sampling plan are determined for pre-determined acceptance
number by satisfying two risks at the specified quality levels simultaneously. The tables of design parameters are provided for various
test termination time and mean ratio for some selected distributions. The operating characteristic values are also provided in the
table. Some comparisons are made among the selected distributions. The results are explained with examples.
Keywords: Probability of acceptance, Rayleigh distribution, generalized exponential distribution, Weibull distribution,
Gamma distribution, Producer’s risk, Consumer’s risk, Minimum angle method.
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1. INTRODUCTION
Acceptance sampling is a inspecting procedure applied in
statistical quality control. Acceptance sampling is a part of
operations management and service quality maintenance. It is
important for industrial, but also for business purposes helping
the decision - making process for the purpose of quality
management. In a time- truncated sampling plan, a random
sample is selected from a lot of products and put on the test
where the number of failures is recorded until the pre –
specified time. If the number of failures observed is not
greater than the specified acceptance number, then the lot will
be accepted. Two risks are always attached to an acceptance
sampling. The probability of rejecting the good lot is known as
the type – 1 error (producer’s risk) and it is denoted by α. The
probability of accepting the bad lot is known as the type – 2
error (consumer’s risk) and it is denoted by β. An acceptance
sampling plan should be designed so that both risks are
smaller than the required values. An acceptance sampling plan
involves quality contracting on product orders between the
producer’s risk and consumer’s risk. In many practical
situations, an important quality characteristic is the lifetime of
the product that is, the time over which it fulfills its task.
Sampling plans to determine acceptability of a product based
on the number of failures recorded until the pre-specified time
are called time-truncated sampling plans.
The life tests are discussed by many authors [1] Goode and
Kao (1961). Gupta and Groll. [2] Balklizi (2003), [3] Balklizi
and EI Masri (2004). [4] Rosaiah and Kantam (2005) and [5]
Tsai, Tzong and Shuo (2006). Mohammad Aslam [6] have
designed double acceptance sampling plan based on truncated
life tests in various distribution. Srinivasa Rao [8] have
designed double acceptance sampling plan based on truncated
life tests for the Marshall – Olkin extended exponential
distribution.
The intent of this paper is to design DSP (0,1) sampling plans
for truncated life tests using minimum angle method, when
life times of the items follows various distribution.
For designing any sampling inspection plan it is usual practice
to select the plan parameters based on the operating
characteristic (OC) curve with desired discrimination such that
the operating characteristic curve passes through the points
with acceptable quality level α ≤ 0.05 and limiting quality
level β≤0.10. Cameron (1952) has used the operating ratio to
design the single sampling plan and constructed tables for easy
selection of the plan parameters with Poisson model, which is
widely, used measure of discrimination to fix the OC curve.
From Cameron table one can observe a jump between the
operating ratios of c = 0 and c = 1 and slow reduction of
operating ratios for other values of c .It may also be seen that
, in between the OC curves of c = 0, and c = 1 plans , there is

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vast gap to be filled which leads one to access the possibility
of designing plans having OC curves lying between the OC
curves of c = 0, and c = 1 plans . To overcome such
situation, Craig (1981) has proposed double sampling plan
with acceptance number 0 and 1 and rejection number 2.
Dodge and Romig (1959) have studied the use of DSP – (0, 1)
plan to product characteristics involving costly and destructive
testing.
1.1. Operating Procedure of Double Sampling Plan of
the Type DSP (0, 1)
According to Hald (1981), the operating procedure for DSP (0,
1) is as follows:
(i) From a lot, select a sample size n1, and observe
the number of defectives d1.
(ii) If d1 = 0, accept the lot.
If d1 > 1, reject the lot.
(iii) If d1 = 1, select a second sample of size n2 and
observe d2.
If d2 = 0, accept the lot. Otherwise reject the lot..
1.1.1 Operating Procedure of Double Sampling Plan
of the Type DSP (0, 1) for Life Tests
(i) From a lot, select a sample size n1 , and observe
the number of defectives d1 ,during the time t0.
(ii) If d1 = 0, accept the lot.
If d1 > 1, reject the lot.
(iii) If d1 = 1, select a second sample of size n2 and
observe d2 during the time t0..
If d2 = 0, accept the lot. Otherwise reject the lot..
1.2 PROPERTIES
1. Fixing n1 = n and n2 = kn (k>0), the OC function of
DSP – (0, 1) plan is identical to the OC function of
ChSP – 1 plan.
2. When k = 0 and k = ∞, the OC function of DSP – (0,
1) plan reduces to the OC functions of single sampling
plans with c = 0 and c = 1 respectively.
3. The OC curves of DSP – (0, 1) plans are lying
between those of c = 0 and c = 1 single sampling
plans.
The following is the operating procedure for DSP(0,1) for life
test in the form of a flow chart.
FLOWCHART
Draw the first sample of size n1= n
Count the number
of defectives d1 during time t0
d1 = 0 d1 = 1 d1 > 1
Accept the lot Draw the second Reject the lot
sample of size n2=kn
Count the number of defectives d2 during the time t0

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2
2
1
1),(






−
−= λλ
t
etF
d2 = 0 d2 > 1
Accept the lot Reject the lot
The DSP (0, 1) is composed of parameters n1 and n2 if t0 is
specified. Let λ be the unknown average life and λ0 be the
specified average life. A lot is considered to be good if the true
unknown average life is more than the specified average life.
In this paper we have applied Rayleigh, Generalised
exponential, Weibull and Gamma distribution to design DSP
(0, 1) plan for life test.We assume that the lot size is large
enough to use the binomial distribution to find the probability
of acceptance of the lot. Then the probability of acceptance for
DSP (0, 1) is given by
P (A) = P (no failure occur in sample 1) + P (1 failure occur in
sample 1 and 0 failure occur in sample 2). Under
the conditions for application of binomial model for the OC
function of DSP – (0, 1) or the probability of acceptance DSP
(0,1) is given by,
L (p) = + n1 p
The usual practice is to choose the second sample size equals
to some constant (k) multiple of first sample size which
facilitates sample administration of the DSP(0,1) sampling
plan.
Taking n1 = n and n2 = kn we get
L (p) = + np
(1)
The failure probability of an item by time t0 is given by
P = F (t0, λ)
2. RAYLEIGH DISTRIBUTION:
The Cumulative Distribution Function (CDF) is given as,
(2)
Where, λ is the scale parameter
3. GENERALIZED EXPONENTIAL
DISTRIBUTION
The cumulative distribution function (cdf) of the exponential
distribution is given by
F (t,λ ) = (3)
Where λ is the scale parameter and α is the shape parameter
and it is equal to 2
4. WEIBULL DISTRIBUTION:
The cumulative distribution function (cdf) of the weibull
F ( t, λ ) = 1 - (4)
Where λ is the scale parameter and m is the shape parameter
5. GAMMA DISTRIBUTION:
The cumulative distribution function (cdf) of the exponential
!/1),(
1
0
∑
−
=
−






−=
γ
λ
λ
λ
j
jt
j
t
etF (5)
Where λ is the scale parameter and γ is the shape parameter
The probability of acceptance can be regarded as a
function of the deviation of the unknown average life λ0
from its specified average life λ. By fixing the time

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termination ratio t/λ0 as 0.628, 0.942, 1.257, 2.356, 3.141,
3.927, 4.712 and the mean ratios λ/λ0 = 4,6,8,10 and 12 ,
producers risk α ≤ 0.05 and consumers risk β ≤ 0.10 one can
find the parameters.
6. NOTATION
n - Sample size
c - Acceptance number
t0 - Termination time
α - Producer's risk
β - Consumer’s risk
P - Failure probability
L (p) - Probability of acceptance
λ - Mean life
λ0 - Specified life
θ - Minimum angle
d - Number of defectives
7. MINIMUM ANGLE METHOD
The practical performance of a sampling plan is revealed by it
operating characteristic curve. Norman Bush et. al. [7] have
used different techniques involving comparison of some
portion of the OC curve to that of the ideal curve. The
approach of minimum angle method by considering the
tangent of the angle between the lines joining the points
(AQL, 1- α) & (LQL, β) is shown in Figure where p1 = AQL,
p2= LQL. By employing this method one can get a better
discriminating plan with the minimum angle. Tangent of angle
made by lines AB and AC is
Tan θ = BC / AC
Tan θ = (p2 - p1) / (Pa (p1) – Pa (p2)) ________________
(6)
The smaller the value of this tanθ, closer is the angle θ
approaching zero and the chord AB approaching AC, the ideal
condition through (AQL, 1-α). This criterion minimizes
simultaneously the consumer’s and producer’s risks. Thus
both the producer and consumer favour the plans evolved by
the criterion.
In this paper we design parameters of the DSP (0, 1) plan
based on truncated life tests for various distributions, using
minimum angle method. The minimum angle method of the
DSP (0, 1) sampling plan under various distributions for
truncated life test is given below.
8. CONSTRUCTION OF TABLES
The Tables are constructed using OC function for DSP (0, 1)
sampling plans under various distributions. The test
termination ratio t/λ0 values are fixed as 0.628, 0.912, 1.257,
1.571, 2.356, 3.141, 3.927 and 4.712, and the mean ratio λ/λ0
values are fixed as 4,6,8,10,12. For various time ratios t/λ0 and
mean ratios λ/λ0 the parameter values n1 and n2 are obtained
and satisfying L(p1)≥0.95 and L(p2)≤0.10 for various
distribution and are provided in Table 1 to Table 4. The value
θ and tanθ values are also provided in each table. The
parameters can be selected such that the angle is minimum.
9. EXAMPLES
Assume that an experimenter wants to establish that the
lifetime of the electrical devices produced in the factory
ensures that the true unknown mean life is at least 1000 hours
when the ratio of the unknown average life is 4. Following are
the results obtained when the lifetime of the test items follows
the Rayleigh, generalized exponential distribution, Weibull
distribution, Gamma distribution respectively.
9.1 Rayleigh Distribution
Let the distribution followed be Rayleigh. Here we get the
sample size as n = 15 and k = 2. The lot is accepted at given
mean ratio λ/λ0 = 6 during 628 hours which satisfies the
condition of producer’s risk and consumer’s risk α ≤ 0.05, β
≤ 0.10, from the Table 1, one can observe that the minimum
angle is θ = 10.54134° and also α =0.0097 and β =0.057
which is very much less than the specified risk. Thus the
required DSP (0, 1) plan has parameters (15,30) which
satisfies both the producers risk and consumer’s risk.
9.2 Generalized Exponential Distribution
Let the distribution followed be generalized exponential.
Here we get the sample size as n = 10 and k = 3. The lot is
accepted at given mean ratio λ/λ0 = 6 during 628 hours which
satisfies the condition of producer’s risk and consumer’s risk α
≤ 0.05, β ≤ 0.10, from the Table 2, one can observe that the
minimum angle is θ = 13.12339° and also α = .0225 and β =

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.086 which is very much less than the specified risk. Thus the
required DSP (0,1) plan has parameters (10,30) which
9.3 Weibull Distribution
Let the distribution followed be Weibull. Here we get the
condition of producer’s risk and consumer’s risk α ≤ 0.05, β ≤
0.10, from the Table 3, one can observe that the minimum
angle is θ = 18.87288° and also α = .009 and β = .069
required DSP (0,1) plan has parameters (7,14) which satisfies
both the producers risk and consumer’s risk.
9.4 Gamma Distribution
Let the distribution followed be Gamma. Here we get the
condition of producer’s risk and consumer’s risk α ≤ 0.05, β ≤
0.10, from the Table 4, one can observe that the minimum
angle is θ = 8.015064° and also α = .0119 and β = .092
required DSP (0,1) plan has parameters (18,36) which
Table – 1: Sample size for DSP (0, 1) using Minimum Angle Method for specified values of k when the life time of the items follows
Rayleigh Distribution
t/ λ0 λ/λ0 k n L(p1) L(p2) Tanθ Θ
0.628 4 2 15 0.956604 0.057872 0.185504 10.50913
0.628 4 1 20 0.972999 0.088719 0.188536 10.67696
0.628 4 3 12 0.954543 0.095023 0.193967 10.97715
0.942 4 1 10 0.965085 0.054237 0.363377 19.96997
0.942 4 2 6 0.961153 0.076524 0.374147 20.51319
1.257 4 1 5 0.969421 0.071827 0.554805 29.02171
1.257 4 2 3 0.964615 0.099971 0.575947 29.93966
1.571 4 1 4 0.952636 0.027549 0.686047 34.45195
1.571 4 2 3 0.923221 0.025049 0.706606 35.24525
0.628 6 1 20 0.994152 0.088719 0.191626 10.84785
0.628 6 2 15 0.990264 0.057872 0.186085 10.54134
0.628 6 3 12 0.989616 0.095023 0.193948 10.97612
0.942 6 1 9 0.993676 0.077916 0.377918 20.70246
0.942 6 2 6 0.991334 0.076524 0.37831 20.72212
1.257 6 1 5 0.993332 0.071827 0.569135 29.6457
1.257 6 2 3 0.99213 0.099971 0.587855 30.44935
1.257 6 3 3 0.987437 0.093749 0.586849 30.40652
1.571 6 1 3 0.993489 0.077142 0.73682 36.38354
1.571 6 2 3 0.981802 0.025049 0.705703 35.21072
2.356 6 1 2 0.984301 0.003886 0.880726 41.3712
2.356 6 2 2 0.958039 0.01117 0.911928 42.36257
3.141 6 1 2 0.955007 0.000155 0.905629 42.16491
3.141 6 2 1 0.956838 0.007206 0.910607 42.32122
3.927 6 1 1 0.962828 0.000896 0.838678 39.98581
0.628 8 1 20 0.998088 0.088719 0.19342 10.94699
0.628 8 2 13 0.997538 0.088371 0.193464 10.94938

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0.628 8 3 12 0.996539 0.095023 0.195105 11.04002
0.942 8 1 9 0.99793 0.077916 0.381975 20.90559
0.942 8 2 6 0.997137 0.076524 0.381726 20.89316
0.942 8 3 6 0.995343 0.070103 0.379817 20.79764
1.257 8 1 5 0.997815 0.071827 0.576571 29.96651
1.257 8 2 3 0.997403 0.099971 0.594918 30.74916
1.257 8 3 3 0.995777 0.093749 0.591887 30.62074
1.571 8 1 3 0.997867 0.077142 0.749174 36.83961
1.571 8 2 4 0.989991 0.007225 0.70188 35.06424
2.356 8 1 2 0.99475 0.000242 0.900179 41.99288
2.356 8 2 2 0.985256 0.000019 0.964629 43.96856
3.141 8 1 1 0.994497 0.014359 0.937228 43.14409
3.141 8 2 1 0.984686 0.007206 0.939777 43.22174
3.927 8 1 1 0.987117 0.000896 0.898427 41.93738
3.927 8 2 1 0.965571 0.000448 0.918067 42.55401
4.712 8 1 1 0.97464 0.000002 0.862638 40.78231
0.628 10 1 20 0.999205 0.088719 0.194398 11.00099
0.628 10 2 13 0.998973 0.088371 0.194374 10.99963
0.628 10 3 12 0.998548 0.095023 0.195896 11.08366
0.942 10 1 9 0.999139 0.077916 0.384167 21.01514
0.942 10 2 6 0.998804 0.076524 0.383727 20.99315
0.942 10 3 6 0.998037 0.070103 0.381389 20.87629
1.257 10 1 5 0.99909 0.071827 0.580523 30.13613
1.257 10 2 3 0.998915 0.099971 0.59881 30.91362
1.257 10 3 3 0.99822 0.093749 0.595152 30.75906
1.571 10 1 3 0.999112 0.077142 0.755573 37.07372
1.571 10 2 4 0.995738 0.007225 0.704711 35.17277
1.571 10 3 2 0.997845 0.077142 0.756614 37.11165
2.356 10 1 2 0.997793 0.01117 0.922643 42.69596
2.356 10 2 2 0.993656 0.003886 0.91971 42.60507
3.141 10 1 1 0.997683 0.014359 0.960682 43.85119
3.141 10 2 1 0.993379 0.007206 0.957907 43.76839
3.141 10 3 2 0.970927 0.000019 0.973001 44.21599
3.927 10 1 1 0.994493 0.000896 0.931306 42.96293
3.927 10 2 1 0.984675 0.000448 0.940173 43.23378
3.927 10 3 1 0.97626 0.000448 0.94828 43.47937
4.712 10 1 1 0.988959 0.000002 0.904929 42.14285
4.712 10 2 1 0.970236 0.000051 0.922378 42.68775
0.628 12 1 20 0.999613 0.088719 0.194972 11.03264
0.628 12 2 13 0.9995 0.088371 0.194921 11.02987

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0.628 12 3 12 0.99929 0.095023 0.1964 11.11149
0.942 12 1 9 0.999581 0.077916 0.385448 21.07907
0.942 12 2 6 0.999417 0.076524 0.384935 21.05348
0.942 12 3 6 0.999038 0.070103 0.382432 20.92843
1.257 12 1 5 0.999557 0.071827 0.582815 30.23427
1.257 12 2 3 0.999471 0.099971 0.601106 31.01033
1.257 12 3 3 0.999128 0.093749 0.597203 30.84578
1.571 12 1 3 0.999568 0.025049 0.71866 35.70328
1.571 12 2 3 0.998727 0.025049 0.71928 35.72672
2.356 12 1 2 0.998921 0.01117 0.929976 42.92208
2.356 12 2 3 0.993859 0.000242 0.924486 42.75295
3.141 12 1 1 0.998866 0.014359 0.974212 44.25161
3.141 12 2 1 0.996711 0.007206 0.969291 44.1066
3.927 12 1 1 0.997282 0.000896 0.950851 43.55681
3.927 12 2 2 0.978225 0.000001 0.968503 44.08332
4.712 12 1 1 0.994495 0.000002 0.930941 42.95173
4.712 12 2 1 0.98468 0.000051 0.940207 43.23482
4.712 12 3 1 0.976267 0.000051 0.948309 43.48022
Table – 2: Sample size for DSP (0,1) using Minimum Angle Method for specified values of k when the life time of the items follows
Generalized Exponential Distribution
t/ λ0 λ/λ0 k n L(p1) L(p2) tanθ Θ
0.628 4 1 16 0.950865 0.088555 0.227718 12.82855
0.628 6 1 16 0.987981 0.088555 0.230813 12.99701
0.628 6 2 10 0.985696 0.098711 0.23405 13.17298
0.628 6 3 10 0.977404 0.086946 0.233138 13.12339
0.942 6 1 8 0.985463 0.095894 0.39477 21.54261
0.942 6 2 6 0.975817 0.066422 0.386163 21.11474
1.257 6 1 5 0.982608 0.098585 0.538681 28.31052
1.257 6 2 4 0.967578 0.059963 0.524679 27.68504
1.257 6 3 4 0.950448 0.056834 0.532899 28.05312
1.571 6 1 4 0.974711 0.067565 0.633256 32.34427
1.571 6 2 3 0.957787 0.05355 0.635294 32.42753
2.356 6 1 2 0.96898 0.086077 0.808616 38.95956
3.141 6 1 2 0.926396 0.020265 0.82691 39.58767
3.141 6 2 1 0.930217 0.085164 0.886676 41.56263
0.628 8 1 16 0.995826 0.088555 0.23342 13.13871
0.628 8 2 10 0.994992 0.098711 0.236282 13.29412
0.628 8 3 10 0.991912 0.086946 0.234014 13.17102
0.942 8 1 8 0.994826 0.095894 0.400416 21.82197

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0.942 8 2 6 0.991211 0.066422 0.38922 21.26701
0.942 8 3 5 0.989764 0.098135 0.403696 21.98373
1.257 8 1 5 0.993664 0.098585 0.54832 28.73682
1.257 8 2 4 0.987849 0.059963 0.528933 27.87583
1.257 8 3 4 0.980778 0.056834 0.531189 27.97675
1.571 8 1 4 0.990522 0.067565 0.645461 32.84066
1.571 8 2 3 0.983698 0.05355 0.640471 32.63838
2.356 8 1 2 0.987847 0.086077 0.836483 39.91192
2.356 8 2 2 0.967053 0.032935 0.807517 38.92146
3.141 8 1 1 0.988882 0.16206 0.979593 44.40937
3.141 8 2 1 0.970039 0.085164 0.915325 42.46865
3.927 8 1 1 0.977358 0.076512 0.89972 41.97836
4.712 8 1 1 0.960744 0.035465 0.847287 40.27418
0.628 10 1 16 0.998201 0.088555 0.235003 13.22473
0.628 10 2 10 0.997833 0.098711 0.237754 13.37398
0.628 10 3 10 0.996462 0.086946 0.235037 13.22655
0.942 10 1 8 0.997736 0.095894 0.403845 21.99107
0.942 10 2 6 0.996114 0.066422 0.391747 21.39261
0.942 10 3 5 0.995441 0.098135 0.405886 22.09157
1.257 10 1 5 0.997187 0.098585 0.554171 28.99395
1.257 10 2 4 0.994528 0.059963 0.532846 28.05072
1.257 10 3 4 0.991191 0.056834 0.532964 28.056
1.571 10 1 4 0.995719 0.067565 0.653325 33.15759
1.571 10 2 3 0.992524 0.05355 0.645797 32.85424
1.571 10 3 3 0.988052 0.051714 0.647614 32.92767
2.356 10 1 2 0.994347 0.086077 0.853634 40.4852
2.356 10 2 2 0.984166 0.032935 0.815081 39.18282
3.141 10 1 1 0.994721 0.16206 1.012095 45.3444
3.141 10 2 1 0.985285 0.085164 0.936242 43.11401
3.141 10 3 1 0.97717 0.084613 0.944176 43.35531
3.927 10 1 1 0.988875 0.076512 0.937684 43.158
3.927 10 2 1 0.970022 0.039074 0.918965 42.58192
3.927 10 3 1 0.954936 0.039017 0.934044 43.04682
4.712 10 1 1 0.980067 0.035465 0.89024 41.67674
0.628 12 1 16 0.999104 0.088555 0.235984 13.27796
0.628 12 2 10 0.998919 0.098711 0.238695 13.425
0.628 12 3 10 0.998224 0.086946 0.235795 13.26772
0.942 12 1 8 0.998861 0.095894 0.40598 22.09614
0.942 12 2 6 0.998034 0.066422 0.393496 21.47946
0.942 12 3 5 0.997684 0.098135 0.407522 22.17199

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1.257 12 1 5 0.998571 0.098585 0.557832 29.15419
1.257 12 2 4 0.997198 0.059963 0.535662 28.17625
1.257 12 3 4 0.995444 0.056834 0.534877 28.1413
1.571 12 1 4 0.9978 0.067565 0.658397 33.36079
1.571 12 2 3 0.996125 0.05355 0.649778 33.01492
2.356 12 1 2 0.997035 0.086077 0.864598 40.84664
2.356 12 2 2 0.991534 0.032935 0.821629 39.40751
3.141 12 1 1 0.997187 0.16206 1.032602 45.91893
3.141 12 2 1 0.992001 0.085164 0.950947 43.55971
3.927 12 1 1 0.993932 0.076512 0.962577 43.90761
3.927 12 2 2 0.954136 0.001523 0.927016 42.83101
4.712 12 1 1 0.988878 0.035465 0.919483 42.59802
4.712 12 2 1 0.97003 0.017898 0.920721 42.63642
4.712 12 3 1 0.954947 0.017893 0.935535 43.09242
Weibull Distribution
0.628 4 2 7 0.959278 0.0694 0.338875 18.72023
0.628 4 1 10 0.971826 0.082513 0.33909 18.73129
0.628 4 3 6 0.951752 0.094561 0.351797 19.3817
0.942 4 1 4 0.973909 0.096367 0.608871 31.33604
0.942 4 2 3 0.956501 0.073342 0.604999 31.17389
1.257 4 1 3 0.953363 0.029551 0.757731 37.15233
0.628 6 1 10 0.993884 0.082513 0.345644 19.06738
0.628 6 2 7 0.990891 0.0694 0.341848 18.87288
0.628 6 3 6 0.988892 0.094561 0.352229 19.40374
0.942 6 1 4 0.994355 0.096367 0.627973 32.12773
0.942 6 2 3 0.990188 0.073342 0.615057 31.59389
1.257 6 1 3 0.98956 0.029551 0.782382 38.039
1.257 6 2 2 0.984922 0.04301 0.797415 38.56937
1.257 6 3 2 0.976414 0.042427 0.80418 38.80555
1.571 6 1 2 0.987411 0.020331 0.87789 41.27956
1.571 6 2 1 0.987682 0.085308 0.94084 43.25406
2.356 6 1 1 0.979583 0.007754 0.87796 41.28185
3.141 6 1 1 0.94254 0.000104 0.806676 38.89227
0.628 8 1 10 0.997999 0.082513 0.349281 19.25332
0.628 8 2 7 0.996988 0.0694 0.344724 19.02029
0.628 8 3 6 0.996287 0.094561 0.35461 19.52503
0.942 8 1 4 0.998156 0.096367 0.637057 32.49947

__________________________________________________________________________________________
0.942 8 2 3 0.996745 0.073342 0.622145 31.88762
0.942 8 3 3 0.994723 0.069904 0.621193 31.84825
1.257 8 1 3 0.996547 0.029551 0.795917 38.51689
1.257 8 2 2 0.994931 0.04301 0.808522 38.95631
1.257 8 3 2 0.991854 0.042427 0.810646 39.02981
1.571 8 1 2 0.995815 0.020331 0.899471 41.97047
1.571 8 2 1 0.995867 0.085308 0.963606 43.93818
2.356 8 1 1 0.993098 0.007754 0.92662 42.81881
2.356 8 2 1 0.980968 0.003885 0.934455 43.05938
3.141 8 1 1 0.979591 0.000104 0.875037 41.18714
3.927 8 1 1 0.95415 0.000001 0.823638 39.47617
0.628 10 1 10 0.999168 0.082513 0.351243 19.35349
0.628 10 2 7 0.998741 0.0694 0.346449 19.10858
0.628 10 3 6 0.99844 0.094561 0.356208 19.60629
0.942 10 1 4 0.999233 0.096367 0.641763 32.69085
0.942 10 2 3 0.998637 0.073342 0.626207 32.05508
0.942 10 3 3 0.997769 0.069904 0.624472 31.98364
1.257 10 1 3 0.998556 0.029551 0.803255 38.77335
1.257 10 2 2 0.997864 0.04301 0.81516 39.18554
1.257 10 3 2 0.996523 0.042427 0.815807 39.20782
1.571 10 1 2 0.998246 0.020331 0.91099 42.3332
1.571 10 2 1 0.99826 0.085308 0.975813 44.29865
1.571 10 3 1 0.99717 0.020331 0.911993 42.36461
2.356 10 1 1 0.997085 0.007754 0.952281 43.59981
2.356 10 2 1 0.991717 0.003885 0.953725 43.64316
3.141 10 1 1 0.991174 0.000104 0.914163 42.43241
3.141 10 2 1 0.975931 0.000019 0.928394 42.87343
3.141 10 3 1 0.963418 0.000019 0.940453 43.2423
3.927 10 1 1 0.979577 0.000001 0.874959 41.1846
4.712 10 1 2 0.896855 0.000072 0.893001 41.76487
0.628 12 1 10 0.999595 0.082513 0.352389 19.41191
0.628 12 2 7 0.999386 0.0694 0.3475 19.16233
0.628 12 3 6 0.999237 0.094561 0.357221 19.65782
0.942 12 1 4 0.999627 0.096367 0.644462 32.80026
0.942 12 2 3 0.999335 0.073342 0.628641 32.15516
0.942 12 3 3 0.998905 0.069904 0.626605 32.07147
1.257 12 1 3 0.999296 0.029551 0.807555 38.92278
1.257 12 2 2 0.998954 0.04301 0.819214 39.32481
1.257 12 3 2 0.998285 0.042427 0.819287 39.32732
1.571 12 1 2 0.999144 0.085308 0.982952 44.50741

__________________________________________________________________________________________
1.571 12 2 1 0.999148 0.085308 0.982946 44.50726
2.356 12 1 1 0.99857 0.007754 0.967185 44.04432
2.356 12 2 1 0.995871 0.003885 0.966044 44.01053
3.141 12 1 1 0.995615 0.000104 0.93794 43.16579
3.141 12 2 1 0.987697 0.000019 0.94541 43.39264
3.927 12 1 1 0.989686 0.000001 0.907806 42.23335
3.927 12 2 1 0.972094 0.000001 0.924234 42.74516
4.712 12 1 4 0.848162 0.000067 1.010552 45.3007
Gamma Distribution
0.628 4 2 18 0.950122 0.092504 0.140036 7.971624
0.628 4 1 28 0.958916 0.091065 0.138385 7.87882
0.942 4 1 14 0.951792 0.089443 0.254165 14.2606
0.628 6 1 28 0.990327 0.091065 0.14022 7.981991
0.628 6 2 18 0.988006 0.092504 0.140809 8.015064
0.628 6 3 17 0.982806 0.092828 0.141683 8.064164
0.942 6 1 14 0.988232 0.089443 0.257913 14.46213
0.942 6 2 9 0.985213 0.092765 0.259745 14.56054
0.942 6 3 9 0.976669 0.089443 0.261274 14.64258
1.257 6 2 6 0.979907 0.07688 0.37514 20.56306
1.257 6 1 9 0.985163 0.078344 0.373571 20.48425
1.257 6 3 6 0.968636 0.070409 0.377144 20.66367
1.571 6 1 6 0.984138 0.088316 0.487605 25.99408
1.571 6 2 4 0.978179 0.088142 0.490774 26.14061
1.571 6 3 4 0.96611 0.081815 0.493961 26.28757
2.356 6 1 3 0.980345 0.098071 0.70529 35.19494
3.141 6 1 2 0.973381 0.084701 0.814165 39.15128
0.628 8 1 28 0.996705 0.091065 0.141646 8.062078
0.628 8 2 18 0.995882 0.092504 0.142 8.081996
0.628 8 3 17 0.993998 0.092828 0.142349 8.10154
0.942 8 1 14 0.995917 0.092765 0.261866 14.67433
0.942 8 2 9 0.99482 0.092765 0.262185 14.6914
0.942 8 3 9 0.991638 0.082397 0.260112 14.58024
1.257 8 1 9 0.994752 0.078344 0.378374 20.72535
1.257 8 2 6 0.992795 0.07688 0.378578 20.73555
1.257 8 3 6 0.988449 0.070409 0.377702 20.69163
1.571 8 1 6 0.99431 0.081815 0.491747 26.18553
1.571 8 2 4 0.99205 0.088316 0.496514 26.40503

__________________________________________________________________________________________
1.571 8 3 4 0.987296 0.081815 0.495556 26.361
2.356 8 1 3 0.992703 0.098071 0.722234 35.83809
2.356 8 2 3 0.979692 0.032876 0.682428 34.31072
3.141 8 1 2 0.989772 0.032363 0.795229 38.49275
3.141 8 2 2 0.972033 0.032363 0.810242 39.01583
3.927 8 1 2 0.978395 0.026441 0.856633 40.58445
4.712 8 1 2 0.961331 0.007635 0.870691 41.04579
0.628 10 1 28 0.998596 0.091065 0.142489 8.109444
0.628 10 2 18 0.998239 0.092504 0.142772 8.125309
0.628 10 3 17 0.997413 0.092828 0.142953 8.135509
0.942 10 1 14 0.998241 0.089443 0.262708 14.71947
0.942 10 2 9 0.997758 0.092765 0.263813 14.77865
0.942 10 3 9 0.99634 0.082397 0.261229 14.64016
1.257 10 1 9 0.997713 0.078344 0.38135 20.87433
1.257 10 2 6 0.996839 0.07688 0.381105 20.86209
1.257 10 3 6 0.994864 0.070409 0.379251 20.76929
1.571 10 1 6 0.997498 0.088316 0.499933 26.56197
1.571 10 2 4 0.996477 0.088142 0.500399 26.58332
1.571 10 3 4 0.99429 0.088316 0.501703 26.64305
2.356 10 1 3 0.99672 0.098071 0.73232 36.21608
2.356 10 2 3 0.99064 0.032876 0.68712 34.49375
3.141 10 1 2 0.9953 0.084701 0.857484 40.61255
3.141 10 2 2 0.986754 0.032363 0.818139 39.28793
3.141 10 3 2 0.979195 0.032062 0.824408 39.50245
3.927 10 1 2 0.989765 0.026441 0.875427 41.19977
3.927 10 2 2 0.972015 0.009439 0.876107 41.22183
4.712 10 1 2 0.981111 0.007635 0.890692 41.6912
4.712 10 2 2 0.950089 0.002636 0.915156 42.46339
0.628 12 1 28 0.999307 0.091065 0.143004 8.138349
0.628 12 2 18 0.999128 0.092504 0.143259 8.152669
0.628 12 3 17 0.998713 0.092828 0.143376 8.159233
0.942 12 1 14 0.999124 0.089443 0.26382 14.77903
0.942 12 2 9 0.998881 0.092765 0.264858 14.83462
0.942 12 3 9 0.998163 0.082397 0.262067 14.68509
1.257 12 1 9 0.998853 0.078344 0.383214 20.96752
1.257 12 2 6 0.998409 0.07688 0.382789 20.94632
1.257 12 3 6 0.997395 0.070409 0.380536 20.83361
1.571 12 1 6 0.998737 0.088142 0.502741 26.69055
1.571 12 2 4 0.998215 0.088142 0.50303 26.70375
2.356 12 1 3 0.998319 0.098071 0.738606 36.44979

__________________________________________________________________________________________
2.356 12 2 3 0.995135 0.032876 0.691008 34.64479
3.141 12 1 2 0.997555 0.084701 0.867738 40.94942
3.141 12 2 2 0.992987 0.032062 0.82433 39.49977
3.141 12 3 2 0.988802 0.032062 0.827935 39.62255
3.927 12 1 2 0.994567 0.026441 0.88805 41.60667
3.927 12 2 2 0.984761 0.009439 0.881498 41.3961
3.927 12 2 2 0.984761 0.009439 0.881498 41.3961
4.712 12 1 2 0.989768 0.007635 0.905244 42.15279
4.712 12 2 2 0.972023 0.002636 0.917147 42.52541
Table – 5: comparison of results for DSP (0, 1) for different lifetime distributions
S.No Distribution
Producer’s
risk
Consumer’s risk k n1 n2
1 Rayleigh 0.0097 0.057 2 15 30
2 Generalized Exponential 0.0225 0.086 3 10 30
3 Weibull 0.009 0.069 2 7 14
4 Gamma 0.0119 0.092 2 18 36
Fig – 1: OC curve for DSP(0,1) various lifetime distributions

__________________________________________________________________________________________
CONCLUSIONS
In this paper designing of DSP (0, 1) sampling plan for
truncated life tests by using minimum angle method is
presented. The minimum sample sizes for various values of
λ/λ0 and different experiment times are calculated assuming
that the lifetime of the test items follows different lifetime
distributions. When the table values (Table 1 to Table 4) are
compared one can say that weibull distribution is
comparatively better than other distributions. It can be seen
that by applying minimum angle method minimizes
simultaneously the consumer’s and producer’s risk. This
minimum angle method plan provides better discrimination of
accepting good lots
REFERENCES
[1]. Goode, H.P., & Kao, J.H.K (1961). Sampling plans based
on the distribution. In Proceeding of the Seventh National
Symposium on Reliability and Quality Control (pp. 24-40),
Philadelphia.
[2]. Balizi, A (2003). Acceptance sampling based on truncated
life tests in the Pareto distribution of the second kind.
Advances and Applications in Statistics, 3 (1), 33-48.
[3]. Balizi & EI Masri (2004), Acceptance sampling based on
truncated life tests in the Birnbaum Saunders model. Risk
Analysis, 24(6), 1453.
[4]. Rosaiah, K., Kantam, R. R. L. (2005). Acceptance
sampling based on inverse Rayleigh distribution. Econo. Qual.
Control 20:277-286.
[5]. Tsai, T.R., & Shou (2006). Acceptance sampling based
on truncated life for generalized Rayleigh Distribution.
Journal of applied statistics, 33 (6), 595 – 600
[6]. Muhammad Aslam, (2007). Double Acceptance Sampling
Based on Truncated Life tests in Rayleigh Distribution.
European Journal of Scientific Research, Vol.17 pp.605-610.
[7]. Bush N. Leonard E.J., and Merchant M.Q.M.Jr., (1953) A
Method od Single and Double Sampling OC curves Utilizing
the Tangent of the Point of the Inflexion, (ENASR), No, PR-7,
1-77.
[8]. Srinivasa Rao (2009) “Reliability test plans for Marshall –
Olkin extended exponential distribution” Applied
mathematical sciences, Vol. 3 (2009), Number 55, 2745-2755

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