A Vision System for the E-Pedigree Era

Engineers collaborated to develop a machine vision system that could handle e-pedigree data while overcoming challenges of resolution, lighting and diverse product packaging.

By Gregory Hollows, Edmund Optics, and David Pfleger, FP Developments

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Product security concerns and regulatory pressure have led to the gradual adoption of electronic pedigree solutions throughout the industry. Secure e-pedigree files store data about each move products make through the supply chain and are intended to allow for the tracking and verification of pharmaceutical products from the time of manufacture to end use. As with many new technologies, however, e-pedigrees have a variety of issues to overcome. One if them is the creation of machine vision inspection systems that can reliably identify and decode the complex markings on bottles and caps that carry e-pedigree information without impeding the flow of manufacturing.

A 2D data matrix, widely used in the automotive and food and beverage industries, has emerged as the primary method for encoding the required information (see Figure 1). This matrix holds large amounts of data, but incorporating it into existing pharmaceutical manufacturing flows involves placement of data on a package not originally designed to accept this information. This must be done in a permanent, legible fashion while accounting for the need to use this information at various user and distribution points. Additionally, the restrictive nature of pharmaceutical packaging does not allow for much in the way of material changes to easily accommodate new markings that must have high contrast so that scanners and imaging systems can read them.

Figure 1. The 2D code (upper left of label) can contain significant amounts of information regarding content, dosage, and production history.
(Click to enlarge image) Figure 1. The 2D code (upper left of label) can contain significant amounts of information regarding content, dosage and production history.

Another challenge is that markings may be located anywhere on the bottle and cap, and in many different forms, ranging from black-on-white inkjet markings on paper to silver-on-black markings on a metallic cap. Add to this the fact that a given manufacturing line (thus the inspection system) must accommodate a variety of different bottle sizes, label placements, materials and even color schemes to handle different production runs. These many constraints create significant challenges in the design of a machine vision system that can automatically detect and read the 2D matrix.

Looking for a Solution


Packaging equipment designer and manufacturer FP Developments (Williamstown, N.J.), working with the support of Edmund Optics (Barrington, N.J.) and Cognex Corp. (Natick, Mass.) confronted these challenges to develop a machine vision system that could thoroughly and accurately inspect e-pedigree data. The system had a requirement to efficiently move hundreds of bottles a minute from a filling and labeling area to a gross packing area while ensuring that each bottle was labeled and marked correctly. The labeling on the caps, and in some instances on the bottles, needed to be read with a high degree of accuracy at all times. Further, the material handling portion of the system had to target what is called toolless changeover — that is, be able to handle a variety of different containers quickly and easily without the need to install new fixturing or make complex changes to the line.

A key consideration for manufacturing engineers that can impact the machine vision system is the placement of the 2D barcode and how it is laid down. Placement must reflect the fact that everyone from distribution companies to doctors and hospitals will need to read the barcodes. One common choice is to mark the cap skirt (Figure 2) so that labels do not need to be re-designed.

Figure 2. The cap skirt provides a convenient location for adding a 2D code without requiring label redesign but presents machine vision systems with challenges for automatic reading, including variations in orientation, bottle height, material reflectivity, and marking method.
(Click to enlarge image) Figure 2. The cap skirt provides a convenient location for adding a 2D code without requiring label redesign but presents machine vision systems with challenges for automatic reading, including variations in orientation, bottle height, material reflectivity and marking method.

The marking technique can range from imprinting a code with a printer, to burning in the code with a CO2 or YAG laser, to use of markings that can only be seen under UV. Each method has pluses and minuses that must be considered in light of factors such as the type of cap used on bottles and the speed at which they pass through the system, as well as cost, intended product use and bottle materials. A critical issue is producing enough contrast in the printing for it to be viewed from almost any angle. The higher the contrast, the more accurately the vision system can read the data.

Another issue is that the system does not know in advance exactly where in its field of view the marking will be located. Not only will label placement and bottle size vary from one product line to another, the orientation of the bottle when it reaches the inspection stage will vary. Manufacturing systems that move bottles typically have at least one stage where the bottle will roll or spin an indeterminate amount. Creating a means of ensuring that the bottles all have a consistent orientation would be too costly, so the vision system must be able to accommodate this uncertainty.

FP Developments addressed this problem by designing its system to use multiple cameras that look at all sides of the bottle simultaneously. While it can seem fairly simple to set up three or four cameras to inspect an object, it can be difficult to extract the resulting information. The marking may wrap around the edges of the object and thus have sections appear spread across two different images, each distorted by the curvature of the surface. Cognex has addressed this problem in its Omni View platform, which uses four high-resolution cameras and proprietary software algorithms to take multiple images and create a 3D surface model image of an object, then flatten it. From this flattened or unwrapped image the machine vision system can extract a host of information by using standard image analysis algorithms.

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