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Como Calcular Cpk Manualmente

Cpk Formula:

\[ Cpk = \min\left(\frac{USL - Mean}{3 \times SD}, \frac{Mean - LSL}{3 \times SD}\right) \]

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1. What Is Cpk?

Cpk (Process Capability Index) is a statistical measure of a process's ability to produce output within specification limits. It indicates how well a process meets specifications by comparing the spread of the process data to the width of the specification limits.

2. How Does The Calculator Work?

The calculator uses the Cpk formula:

\[ Cpk = \min\left(\frac{USL - Mean}{3 \times SD}, \frac{Mean - LSL}{3 \times SD}\right) \]

Where:

Explanation: The formula calculates how many standard deviations the process mean is from the nearest specification limit, divided by 3. The minimum value between the two calculations (upper and lower) is taken as the Cpk.

3. Importance Of Cpk Calculation

Details: Cpk is crucial for quality control and process improvement. It helps manufacturers determine if their process is capable of meeting customer requirements and identifies opportunities for process optimization.

4. Using The Calculator

Tips: Enter the upper specification limit, lower specification limit, process mean, and standard deviation. All values must be valid (standard deviation > 0).

5. Frequently Asked Questions (FAQ)

Q1: What is a good Cpk value?
A: Generally, a Cpk value of 1.33 or higher is considered acceptable, with 1.67 or higher indicating excellent process capability. A value below 1.0 suggests the process may not meet specifications.

Q2: What's the difference between Cp and Cpk?
A: Cp measures potential capability assuming the process is centered, while Cpk measures actual capability accounting for process centering.

Q3: Can Cpk be negative?
A: Yes, a negative Cpk indicates the process mean is outside the specification limits.

Q4: How many data points are needed for a reliable Cpk calculation?
A: Typically, at least 30 data points are recommended for a statistically reliable Cpk calculation.

Q5: What are the limitations of Cpk?
A: Cpk assumes normally distributed data and may not be accurate for non-normal distributions. It also doesn't account for process stability over time.

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