The final tool in our SPC blog series, Control Charts are helpful tools for identifying and eliminating unwanted variation in production.
This blog is the third in our series on statistical process control (SPC). In the previous blogs, we discussed the basics of statistical techniques and process capability indexes. In this blog, we will be focusing on control charts and their core benefits in the manufacturing landscape.
The control chart is a helpful tool in identifying unwanted variation in a production process. The goal of statistical control is to identify process variation and to determine which variation is beyond our control. While some variations are inherent to the process, others are special or assignable. These deviations are outside of the "normal" variation that we typically see in a process. It can be identified using statistical tools to be reduced or eliminated from the normal process.
To understand control charts, let's first discuss variation. In a perfect world, there would be no variation. Whenever we turn on a machine, it would produce the exact same part every time. However, we don't live in a perfect world - machines fail, internal parts abrade, molding tools wear out, temperatures change, and materials subtly change from one production badge to the other. There are several inputs to a given production process that must work together to produce parts that meet customer requirements. By measuring key characteristics of the finished part, we can evaluate the manufacturing process. Using control charts, we can identify inherent variations from special variations (e.g. an injection mold wearing off or an improper machine setting). By eliminating these unique discrepancies, we can return the process to "normal".
The two types of control charts that are typically used in Boyd's injection molding are individual charts and x-bar R charts. As seen in the below example, both these charts feature two elements - 1) a center line which is the mean (average) for the particular data set being studied, and 2) upper and lower control limits which are statistically calculated from the same data set. Measurement data for key characteristics are collected and entered at regular intervals over time.
The control limits, calculated statistically, represent plus/minus three standard deviations from the average (mean). These lines represent the threshold at which the process output is considered to be statistically "unlikely". In other words, the control limits represent the division of the natural variation of a process from the "unlikely" variation, occurring due to special or assignable causes, that can be eliminated from the process. Data points that appear outside of these control limits or unusual data runs (data increasing or decreasing over six or more successive measurements) should be investigated to determine and eliminate the source of the assignable variation.
One of the biggest advantages of identifying and addressing variations early in the production cycle is to prevent problems before any out-of-specification parts are produced. It maximizes machine and material efficiency, which in turn lowers production cost and time. The various statistical tools employed by the process engineers at Boyd continue to play a pivotal role in meeting customer demands and consistently building high-quality parts. Learn more about Quality at Boyd or contact us.