DoE Definitions and Expectations

2017-09-09 14:11:56 viya 109

Design of experiments (DoE) is a systematic method for determining the effect of factors and their possible interactions in a design or a process toward achieving a particular output of the quality characteristics). It is used in order to quantify the source and resolution of variation and the magnitude of the error when comparing the average of the quality characteristic to the target. Figure 7.1 is an example of how these elements are arranged.

Using DoE techniques, a design or a process can be manipulated to provide a target or minimal/maximum performance of the quality characteristic average or reducing its variability or both. This is accompIished by setting factors that affect the quality characteristic to predetermined levels and analyzing the output sets of factorial, or orthogonal experimental arrays.

Reducing production variability is one of the most commonly used methods to increase the process capability index and attain six sigma quality. Variability can be addressed by using a combination of two strategies:

1. On-line control. Here the focus is on maintaining the current production processes within a specified area of variability through control charts, optimal maintenance, and calibration of production processes and equipment. This is the traditional method of maintaining quality, and was discussed in Chapter 3.


2. Off-line control. Here a proactive effort is aimed at reducing the process variability or increasing design robustness through defect analysis and design of experiments (DoE). This allows for achieving six sigma through targeting of specific process operations or design elements. This effort can be guided by many of the tools of TQM and corrective action processes discussed in Chapter 3, as well as this chapter.

On-line control methods should be instituted before attempting offline control projects. No amount of design of experiments and defect analysis can rectify a poor-quality operation that is out of control. In that case, the benefits of* off-line control improvement can only be felt temporarily, before being negated by a manufacturing operation that is out of control,where the production factors,materials,and processes are constantly changing. The sources of defects, as outlined earlier in this book, are due to the interaction between product specifications and process variability. This interaction originates from one of two sources: either the process is not centered (the process output average does not equal the target value), or the product and process variability, as measured by the standard deviation of the manufactured product characteristics, is too high. Either one or a combination of both can influence the product defect level.

It is much easier to identify, collect data,and rectify the first situation: a process average not equal to the target. Incoming materials, equipment settings, and performance can be measured, and if not equal to target, can be corrected by strict adherence to specifications. Materials properties such as geometrical tolerances, density, tensile strength, hardness, etc., can easily be measured and rectified by working with production personnel and suppliers. Equipment factors such as temperature, pressure, speed, and feed and motion accuracy can be measured by calibration gauges against original purchase specifications, and readjusted as necessary.

The calibration of production equipment is usually achieved by using an instrument or gauge that is inherently more accurate that the equipment to be calibrated. In addition, the instrument accuracy has to be certified through traceability to the National Institute of Standards and Technology (NIST). It is common to use calibration equipment whose accuracy and resolution are at least one-tenth that of the equipment being calibrated, as was shown by the gauge capability (GR&R) section in Chapter 5 of this book.

The maintenance of production variability and keeping the production average equal to the target is best accomplished by using control charts, discussed in detail in Chapter 3. Variable control charts show control of the quality characteristic average in the X chart, and its variability in the R chart. Attribute charts do not make a distinction as to defect source between the average deviation versus the variability of the process, and therefore it is more difficult to ascertain the causes of the defects.

Electronic production operations, such as those producing PCB assemblies, that are in good control and operating within six sigma quality generate a small amount of defects, normally in the range of 1-20 PPM, amounting to a few defects per working day. Individual defects can thus be analyzed using the tools of TQM and corrective action process improvements presented in Chapter 3: brainstorming, cause and effect, pareto diagrams, data collection, and sampling, etc. These tools can be used to determine the most probable cause for each defect. If a deviation of the production process was found to be the cause, the process can be adjusted accordingly.

Reducing the variability of the production process is more difficult and requires a thorough examination of the sources of variability. Some of these causes are uncontrolled factors or noise. They can be generated from the following:

External conditions, imposed by the environment under which the product is manufactured or used, such as temperature, humidity, dirt, dust, shock, vibration, human error, etc. These conditions are beyond the control of the design and manufacturing process planners. They are difficult to predict, and it is expensive to design specific characteristics to satisfy all of the possible conditions under which the product is expected to operate.

Internal conditions under which the product is stored or used, such as friction, fatigue, creep, rust, corrosion, thermal stress, etc. These conditions have to be specified correctly within the normal use of the product. However many customers will overuse the product, and expect that U will continue to operate even beyond its normal range. Therefore, the design has to be made more robust to ensure proper operation beyond advertised specifications.

DOE is focused on improving the robustness of product functionality in external and internal conditions of operation. It seeks to determine the best set of process materials and factors in order to ensure that the product characteristics average is equal to the specified nominal, and the variability of the product characteristics is as small as possible. A set of designed experiments can be performed to find such an optimum level of factors influencing the operation or manufacture of the product.

TAG:   DoE

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