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Process Capability Analysis IV – Improve the Process

Process Capability Analysis IV – Improve the Process

In the article Process Capability I – Overview and Indices, the main process performance indices were defined. In the article Process Capability Analysis II – Estimating Percent Defective, ways to calculate the percent defective were introduced.

Next, a way to estimate estimate the percent defective was discussed in Process Capability III – Cp vs Percent Defective. The data showed the process had a  Cp ~0.46, so even if centered, a high percentage of parts would be non-conforming! In industry, it is common to see a Cp of about 1. The paradigm is that if the process range is just contained within the tolerance range, it is satisfactory.  Such processes are marginally capable. A process mean shift away from the target or an increase in variation significantly increases the number of non-conforming parts.  Process Capability needs to be increased. At a minimum, Cp should be greater than 1.33, but larger is better.

There are two approaches to improving process capability. One is to center the process and the other is to reduce variation. Both need to be accomplished.

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Process Capability Analysis III – Cp vs Percent Defectives

Process Capability Analysis III – Cp vs Percent Defectives

Introduction

In the article Process Capability I – Overview and Indices, the main process performance indices were defined. In the article Process Capability Analysis II – Estimating Percent Defective, ways to calculate the percent defective were introduced.

In this article, the mathematical connection between the Cp index and percent defectives is made.

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Process Capability Analysis II – Percent Defective Estimates

Process Capability Analysis II – Percent Defective Estimates

Introduction

In my prior article, Process Capability I – Overview and Indices, the process capability concept was defined for prototype samples and serial production. The data is assumed to be normally distributed and Pp, Ppk, Cp, and Cpk indices were defined. Its application to one-sided and two-sided tolerances was discussed. This article provides methods to estimate the percent defective.

The Data

The engineering tolerance for a critical characteristic is 10±0.2. So the lower specification limit (LSL) is 9.8 and the upper specification limit (USL) is 10.2. A sample of 30 parts provided measurements of the critical characteristic. The sample mean ($-\bar{x}-$) was 9.951 and the sample standard deviation (s) was 0.1825.

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Process Capability Analysis I – Overview and Indices

Process Capability Analysis I – Overview and Indices

Introduction

How is a manufacturing process determined to be capable of producing parts that meet engineering requirements? Some, like the finish of a gear tooth are critical, while the roughness of a non-contact surface isn’t critical. The critical characteristics need to be identified and checked to determine if the process is capable.

This article defines the analysis concepts and indices.

Prototypes and Series Production

Process capability assessments (PCA) start during the product development process and continue into series production. Prototype parts are created by a variety of methods, ranging from hand-crafted parts to short term manufacturing in a production intent process under factory conditions. The methods that are closest to series production are the most realistic.

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Manufacturing Projects

Manufacturing Projects

How do I know your projects are valuable?

We have resolved many manufacturing issues. Why do I need DOE to do this? How can we better get to root cause? Each section below contains a 2-minute video to expand on the topic provided.

Why use Design Of Experiments?

Design of Experiments is the best tool for resolving problems when our science and experience fails us. Even if we are right, we also need to prove our approach works to others. Today that includes proof with statistical confidence. We need to be efficient to reduce excessive prototypes, scrap and downtime.

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Achieving Innovation

Achieving Innovation

We want to be more innovative – but how?

Many companies talk about their desire for Innovation. Can Design of Experiments (DOE) help in that process? Each section below contains a 2-minute video to expand on the topic provided.

Too often, we manage on hope

How often do we hear a design or process development team express optimism – “We are almost there!” And how long does that same response continue without progress? The key is to decide when we have tried long enough using informal methods. Predictability and confidence are important to achieve project success.

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