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Today, statistical process control (SPC) is the gold standard of quality control because it helps manufacturers maximize production with minimal waste and rework. SPC was pioneered by Walter A. Shewhart at Bell Laboratories in the early 1920s and gained worldwide attention following World War II after W. Edwards Deming trained Japanese engineers in the technology. SPC uses graphical control charts to determine when a process should be adjusted. The following guidelines can help manufacturers make the best use of SPC.
It's important to narrow the focus to monitoring only those conditions that are critical for maintaining process control. Specific SPC monitoring criteria are determined by the type of facility. For example, the major environmental factors that affect the quality of silicon-chip or solar-cell manufacturing are airflow temperature, humidity, air pressure, particles, light wavelength and electromagnetic interference. Consequently, these factors should be the key monitoring criteria in these types of facilities.
The criteria should also be based on the project's requirement for manual or automated monitoring and the desired frequency. This can be determined by comparing the costs of manual monitoring with the capital and life-cycle operational costs of an automated system. If the quality specifications of the product require hourly monitoring, it makes good economic sense to use an automated system. However, when data monitoring is less frequent, the payback period on the high capital cost of automated systems may be too long. Similarly, the data from an initial period of monitoring may demonstrate that the frequency can be reduced. Therefore, monitoring should be fine-tuned according to the data as the SPC implementation period progresses.
Not only is it important to understand what to test, but also how to test it. If a fault is discovered toward the end of the process, the product must go through a long and expensive incubation process. A well-structured testing process focuses on each criterion identified above in an orderly sequence along the critical path and avoids extraneous testing.
A fishbone (cause-and-effect) diagram can be set up or another statistical tool used to help owners determine the types of issues that could be creating the problem. Troubleshooters can check off the potential causes one by one until they discover a demonstrable cause. When the test is well-structured, troubleshooting is a simple matter of using statistical tools and methods to ferret out the problem.
Brainstorming can also contribute to understanding all the potential sources of process control problems. Some can be logically eliminated without testing until the field is narrowed, after which a series of tests can be implemented. For example, if there have been temperature variations, monitoring can be established to determine the precise variation. If the problem is particle control, some of those particles can be captured and sent out for analysis. The chemical composition of the particles suggests the most probable sources. This is the most cost-effective methodology.
When troubleshooting, think outside the box or farther down the line. Particles can be blown long distances from the source because of eddy currents and other phenomena that occur around devices. Vibration is another common source of product contamination, and its source is often unexpected and sometimes distant from the process line. One must examine all potential sources of these problems with an open mind.
For example, in one facility, the source of particle contamination was believed to be a tool. However, even after cleaning, testing confirmed that the tool was contaminated with particles. Exposed electrical motors outside of the tool were running when operators opened the tool to clean it. Engineers knew from experience that motors can be a contaminant source. They asked operators a series of questions that revealed both that the tool was located beneath the motor housing and that the motor was running during the cleaning process. Multiple tools had been set up this way, but further questioning revealed that this was the one that operators could not get clean. Testing the area with a particle counter confirmed significant levels of particles. Operators were asked to turn off the motor during cleaning, and the result was a spotless tool. The motor was examined and found to be failing, which produced particles that fell into the tool during cleaning. The problem was solved by replacing the motor.
In another example, engineers could find no vibration source inside the facility. However, the time stamp coincided with the scheduled passage of a train approximately 400 feet away. Project engineers hypothesized that the train caused the disruptive vibration. A test performed with a vibration monitor confirmed that the movement of the train was the source of the vibration and, specifically, only when the train was carrying a full load in one direction. Based on these results, the facility layout was modified to avoid the effect.
Computational fluid dynamics (CFD)modeling can help to identify the source of a problem. A good CFD model creates a visual representation of what is occurring in the facility. It also lets engineers compare the results of various solutions during the design phase, thus avoiding additional construction costs to correct the problem.
Unfortunately, many CFD models are generated based on incomplete data, either because complete data are not available or because the modeler has entered only a portion of the available data. As a result, the modeler may make assumptions that are not based on field-gathered information. To ensure that the data are complete, a modeler must ask the right questions, such as what objects may be obstructing flow or altering the pattern. Field measurements should also be taken to complete the data required to develop a valid model.
It is now universally accepted that SPC can be applied to any process where output of a product meeting specifications is measured. At its full potential, this process is an enormous aid to cost effectiveness because it can allow as much conforming product to be made as possible with a minimum amount of rework or scrap.
See Also: Six Sigma: A Comprehensive Overview
Dewayne Galyon is a technical specialist and associate at SSOE Group, a global project delivery firm for architecture, engineering and construction management. Dewayne can be reached at 503-213-4250 or Dewayne.Galyon@ssoe.com.