A PCB carrier can reduce variations on solder joint geometries.
Eliminating defect opportunities by minimizing process variation is a key concept of Lean manufacturing. Fixturing is often a key ingredient in that. However, many contract manufacturing customers see fixturing as an unnecessary non-recurring engineering (NRE) expense. The reality is fixturing does add cost, but it can also save money in production. More important, printed circuit board (PCB) technology is driving the need for greater use of fixturing. Consequently, the decision on whether to fixture or not is being made more frequently.
Use of a printed circuit board assembly (PCBA) carrier for fixturing has several benefits, including:
As most readers know, statistical tests calculate a mean and confidence intervals on the mean. We are all familiar with the fact that as our sample size decreases, our knowledge of the “true” mean becomes less and less certain. This is important for tests that use the mean, such as the t-test and ANOVA.
FIGURE 1 is an example of two data sets: “apples” and “oranges.” In the first experiment we had 15 samples of apples and 15 samples of oranges. Plotting the means with their calculated confidence intervals shows we cannot differentiate between apples and oranges. (Since the confidence intervals overlap, we cannot be certain both means are not equal.)
Mixed-technology designs are prime for waste elimination. Here’s how.
Taiichi Ohno developed the concept of the seven wastes (muda) in manufacturing as part of the Toyota Production System (TPS), the foundation for Lean manufacturing philosophy. They are:
The challenge created by system variation is sometimes best solved by moving test operations offline.
A paced assembly line with inline functional test balanced through careful application of Lean manufacturing principles is a model of efficiency. Achieving that level of efficiency requires careful coordination among engineering and production personnel.
Paced lines that integrate functional testers deal with several challenges, including:
Data distribution, explained.
In my December column I listed three items to watch out for when evaluating capability study results: Cp versus Pp, the distribution of data, and sample size. I hopefully cast some light on the differences between the two measures of capability, Cp and Pp.
In this column I will dive deep into the distribution of data. The thing to remember is the standard capability study assumes the data are normally distributed. This assumption of normality, while not so critical in other statistical tools, is very important in capability studies.
Cp and Pp give us predictions based on a sample of how our population will behave in the far tails of the normal curve. These measures use mean and standard deviation to create a normal distribution, and, from this, predict how many of our parts, over the entire population of parts, will fall outside the tolerance limits.
APQP techniques for identifying and eliminating bottlenecks.