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If a process variable is normally distributed, in the long run virtually all observed values should be between饾潄-3饾潏and饾潄+3饾潏, giving a process spread of 6饾潏.

a.With LSL and USL denoting the lower and upper specification limits, one commonly used process capability indexis Cp = (USL 鈥 LSL)/6饾潏. The value Cp= 1 indicates a process that is only marginally capable of meeting specifications. Ideally, Cp should exceed 1.33 (a 鈥渧ery good鈥 process). Calculate the value of Cp for each of the cork production processes described in the previous exercise, and comment.

b. The Cp index described in (a) does not take into account process location. A capability measure that does involve the process mean is Cpk = min {(饾潏USL -饾潄)/3饾潏, (饾潄鈥 LSL)/3饾潏} Calculate the value of Cpk for each of the cork production processes described in the previous exercise, and comment. (Note: In practice, m and s have to be estimated from process data; we show how to do this in Section 16.2)

c. How do Cp and Cpk compare, and when are they equal?

Short Answer

Expert verified

\(c.\mu = \frac{{USL + LSL}}{2},{C_{pk}} \le {C_P}\)

Answers of different parts of above question are:

a).\({C_P} = 1.67,{C_P} = 0.67\)

b).\({C_{pk}} = 1,{C_{pk}} = 0.67\)

c).

Step by step solution

01

Control Limit

The lower control limit is a line below the centerline that indicates the number below which any individual data point would be considered out of statistical control due to special cause variation.

Upper Control Limit (UCL) means a value greater than the maximum value of a chemical or physical parameter that can be attributed to natural fluctuations and sampling and agree upon by the Administrator and the operator prior to initiation of mining.

02

Answering the above mentioned question:

Process capability index is

\({C_p} = \frac{{USL - LSL}}{{6\sigma }}\)

Were

\({C_p} = 1\)

Indicates a that a process is only marginally capable of meeting specifications. Ideally,

\({C_p} > 1.33,\)

(a).

In Exercise 4 (a), we have that\(\sigma \)= 0.02 from which we obtain

\({C_p} = \frac{{USL - LSL}}{{6\sigma }} = \frac{{3.1 - 2.9}}{{6 \times 0.02}} = 1.67 > 1.33\)

\({C_p} = \frac{{USL - LSL}}{{6\sigma }} = \frac{{3.1 - 2.9}}{{6 \times 0.05}} = 0.67 < 1\)

(b).

The capability measure that involves process mean is

\({C_{pk}} = \min \left\{ {\frac{{USL - \mu }}{{3\sigma }},\frac{{\mu - LSL}}{{3\sigma }}} \right\}\)

\(\frac{{USL - \mu }}{{3\sigma }} = \frac{{3.1 - 3.04}}{{3 \times 0.02}} = 1\)

\({C_{pk}} = \min \left\{ {1,2.33} \right\} = 1.\)

\(\frac{{USL - \mu }}{{3\sigma }} = \frac{{3.1 - 3}}{{3 \times 0.05}} = 0.67\)

\(\frac{{\mu - LSL}}{{3\sigma }} = \frac{{3 - 2.9}}{{3 \times 0.05}} = 0.67\)

\({C_{pk}} = \min \left\{ {0.67,0.67} \right\} = 0.67\)

(c).

\({C_{pk}} \le C\)

\(\frac{{USL - LSL}}{{6\sigma }} = \min \left\{ {\frac{{USL - \mu }}{{3\sigma }},\frac{{\mu - LSL}}{{3\sigma }}} \right\}\)

\(\frac{{USL - LSL}}{{6\sigma }} = \frac{{USL - \mu }}{{3\sigma }}\)

\(\mu = USL - \frac{{USL - LSL}}{2}\)

\(\mu = \frac{{USL + LSL}}{2}\)

\(\frac{{USL - \mu }}{{3\sigma }} = \frac{{USL - {{(USL + LSL)} \mathord{\left/

{\vphantom {{(USL + LSL)} 2}} \right.

\kern-\nulldelimiterspace} 2}}}{{3\sigma }} = \frac{{2USL - LSL - USL}}{{6\sigma }} = \frac{{USL - LSL}}{{6\sigma }} = {C_P}\)

\(\begin{array}{l}\frac{{\mu - LSL}}{{3\sigma }} = {C_P}\\{C_{pk}} = \min \left\{ {{C_P},{C_P}} \right\} = {C_P}\end{array}\)

We also claimed that,

\(\begin{array}{l}{C_{pk}} \le {C_P}\\\mu - LSL < USL - \mu \\{C_{pk}} = \frac{{\mu - LSL}}{{3\sigma }}\\\mu < \frac{{LSL + USL}}{2}\\{C_{pk}} = \frac{{\mu - LSL}}{{3\sigma }} < \frac{{{{(LSL + USL)} \mathord{\left/

{\vphantom {{(LSL + USL)} {2 - LSL}}} \right.

\kern-\nulldelimiterspace} {2 - LSL}}}}{{3\sigma }} = \frac{{LSL - LSL}}{{6\sigma }} = {C_P}\end{array}\)

Hence, the final answer is

a).\({C_P} = 1.67,{C_P} = 0.67\)

b).\({C_{pk}} = 1,{C_{pk}} = 0.67\)

c).\(c.\mu = \frac{{USL + LSL}}{2},{C_{pk}} \le {C_P}\)

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