6-sigma and Materials Engineering

Every once in a while I notice a company’s website uses the term “six sigma” or “6σ” to convey good quality. When I see this I wonder if they really understand what six sigma is and what it applies to. This article is about that and, of course, how materials engineering fits in.

Sigma refers to standard deviation, a calculated value that describes the spread of data in a dataset. The larger the standard deviation, the larger the spread in the data. The figure shows two datasets of attribute measurements for two different sample populations. The y-axis shows the number of occurrences for each attribute value. Dataset 1 has a wider spread than dataset 2, so the standard deviation for dataset 1 is larger than the standard deviation for dataset 2.

Statistical process control

Sigma is used as part of statistical process control (SPC). SPC is a data-driven methodology used to monitor and control a process. The goal of SPC is to produce output that meets customer requirements, maintain output variations within acceptable limits, and minimize defects.

SPC can be used to control any process that produces a measurable output, such as the size of a component, the temperature of a machine, or the time to complete a task. It is used in a variety of industries, including manufacturing, healthcare, and service industries. The focus here is manufacturing.

With SPC, the attributes of the output of a process are measured, collected, and analyzed. Examples of attributes are part dimensions, metal hardness, or coating thickness. The data is used to identify and eliminate causes of process output variation and improve the quality of the process output. So, SPC is a quantitative approach for monitoring and controlling a production line to consistently produce output that meets specifications.

Six sigma

Standard deviation (sigma σ) is continuously calculated for the data collected. The goal, if you’re interested in having a capable process, is a small standard deviation with respect to the difference between the upper and lower specification limits of the attribute being measured. If ±6σ (12 standard deviations) is equal to the difference between the upper and lower specification limits, then only 3.4 out of 1,000,000 items produced will not meet specifications, i.e. 3.4 defects per million opportunities. Six-sigma!! This assumes the data average is centered between the upper and lower specification limits. There are other SPC calculations if the average is not centered.

SPC and materials engineering

How does materials science fit in with all this? Well, materials are used to fabricate components and join components together. Also, we want manufacturing processes to produce components and assemblies with materials that have the desired properties. So, developing and maintaining a six-sigma process and producing a “6σ” product depends on…

• Selecting materials with composition and properties that are compatible with the process, enabling good manufacturability.
• Characterize effects of incoming materials variations on process output.
• Controlling composition and properties variations of incoming materials. This includes writing good materials specifications and verifying that suppliers are capable of supplying materials and components that meet specifications.
• Selecting and controlling process conditions.
• Using failure analysis to help identify the root cause of problems.

Focus and discipline

SPC is a powerful tool for improving quality and reducing costs. As with anything else like this, if it was easy to do, everyone would be doing it. But it requires a commitment from management and employees to be successful. And it takes engineering focus and discipline to use the data to make process and design improvements.

If you’re interested in learning more, check out the video at the top of this post. It goes into more detail.

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