• Load a 2D drawing which has balloon meta data. Use balloonDigest to rapidly find balloons.
• Load vendor data and watch balloonDigest bring your 2D drawing to life, coloring FAI and SPC balloons according to the performance of the data. Each balloon becomes a clickable button which shows the data in detail.

This magic is accomplished by an initial meta data saving step that only needs to be done once. Meta data is entered for each balloon, and saved to the drawing. For detail on this and additional aspects of the workflow, click here.

FAI (First Article Inspection) dimensions are measured on small quantities of parts (typically 3), providing a rough check on part quality. SPC (Statistical Process Control) dimensions are measured on larger quantities of parts (typically 32), and give an idea of whether there will be problems when large volumes of parts are produced.

FAI and SPC dimensions are identified as such by balloon symbols. Each FAI and SPC balloon is associated with one or more measurement locations. Multiple locations for one balloon would occur for instance in the case of a part with several holes, all with the same dimension. Instead of assigning a different balloon to each hole, standard practice is to assign a single balloon to a dimension prepended with "xN", where N is the total number of holes. This is interpreted as an instruction to measure each hole separately, and to report the data separately, distinguished by location number. The location number (or letter) should uniquely identify the measurement location on the drawing.

FAI and SPC data enters balloonDigest by way of a single .csv file. This is the simplest possible format for tabular data- anything that can be opened in Excel can be exported as a .csv file. The .csv format requirements are as follows:

• Rows can be in any order.
• The data for each balloon location is given in a single row. The first entry in this row is marked "FAI" or "SPC" (the balloon type). The second entry is marked with the balloon ID. The third entry is marked with the measurement location number (or letter). Entries at and beyond the fourth entry consist of actual measurement values.
• The part number is given in a single row, with first entry marked "Part Number", and second entry containing the part number.
• The part name is given in a single row, with first entry marked "Part Name", and second entry containing the part name.
• The vendor name is given in a single row, with first entry marked "Vendor", and second entry containing the name of the vendor.
• The date is given in a single row, with first entry marked "Date", and second entry containing the date, with format YYYY.MM.DD.
• Any additional part information (for instance any process details) are given in a single row, with first entry marked "Part Info", and second entry containing a text description of the additional information.

An example of a properly formatted vendor data .csv file can be downloaded here.

For FAI data on a bilateral spec, we introduce $$F_{pk}=\min\left\{\frac{a-LSL}{USL-LSL},\frac{USL-b}{USL-LSL}\right\},$$ where $a$ and $b$ are the minimum and maxium data values, and where $LSL$ and $USL$ are the lower and upper spec limits on the dimension. The higher the value of $F_{pk}$ the better, with a best possible value of 0.5. Here are some examples:

For FAI data on a unilateral spec, we introduce $$F_{pk}=\frac{USL-b}{2\cdot USL},$$ where $b$ is the maxium data value, and where $USL$ is the upper spec limit on the dimension. As with the bilateral case, the higher the value of $F_{pk}$ the better, with a best possible value of 0.5. Here are some examples:

For SPC data on a bilateral spec, we use $$C_{pk}=\min\left\{\frac{\mu-LSL}{3\sigma},\frac{USL-\mu}{3\sigma}\right\},$$ where $\mu$ and $\sigma$ are the mean and standard deviation of the data, and where $LSL$ and $USL$ are the lower and upper spec limits on the dimension. As with $F_{pk}$, higher values of $C_{pk}$ are better. Here are some examples:

For SPC data on a unilateral spec, we use $$C_{pk}=\frac{USL-\mu}{3\sigma},$$ where $\mu$ and $\sigma$ are the mean and standard deviation of the data, and where $USL$ is the upper spec limit on the dimension. As with the bilateral case, higher values of $C_{pk}$ are better. Here are some examples:

Balloon meta data is appended to PDFs in accordance with the Adobe PDF specification. Meta Data is added as a dictionary object to the end of the PDF document as shown in the sample below. This addition has no effect on the document's visible attributes, for instance when it is opened by Preview or Adobe Acrobat Reader.

Balloon meta data can be exported as a (human readable) text file. This data can be imported and then saved to a PDF, eliminating the labor of retagging balloons when a PDF is up-reved.

Please send any questions and comments about balloonDigest to balloondigest@gmail.com