By Markus Tarin, MoviMed
The complexity of machine vision inspection applications varies greatly depending on the speed of the operation and the detection tolerances required to ensure quality.
Implementing a machine vision application to gauge 20 cylindrical parts per second that are 2 or 3 in. long and 9 mm in diameter to micron tolerances at line speeds of 52 in. per second falls in the complex category, even for an experienced machine vision integrator such as MoviMed, based in Irvine, Calif.
Founded in 1999, MoviMed was established to meet the needs of the medical device community in Southern California. Our business quickly expanded beyond that market as two key changes occurred: Machine vision and motion technology became more refined, and custom applications for vision, motion, data acquisition and inspection became the norm for most manufacturing and research firms, irrespective of their market.
The Task at Hand
The metal cylinders in question are made out of a brass alloy. The parts are made from small metal "cups" that are drawn into a hollow cylinder. The resulting part has small features, such as a little lip, a slant angle and an undercut. "All of these features are critical for proper functioning of the system since a precise mechanical fit is of the utmost importance," says David Ritter, scientific technologist—real time systems at MoviMed. "The custom machine vision system required must gauge multiple mechanical dimensions of the part simultaneously. Some critical dimensions are overall length, part width at various critical locations, slant angle and resulting width at predetermined part lengths, and thickness of the lip and size of the undercut."
The producing machine was equipped with an '80s-era optical-gauging system. Besides obsolescence issues, the system was not measuring up to desired tolerances. Our company performed some calculations and optical simulations to approximate the old system's performance as a baseline from which to work. "It turned out that the old system was limited primarily by the optical path—mainly a custom lens design that could not reproduce details better than about 100 µm at a contrast level of at least 50%," Ritter explains. "Second, the image sensor used was a line scan sensor with only 128x1 pixels. The system used an averaging technique to try to overcome some of its limitations."
Another flaw in the original design was that critical measurements were based on indirect references derived from the tooling and part fixture and not measurements of the part itself. This invariably led to measurement drift caused by mechanical wear and tear.
What Technology to Use?
While we evaluated technology options, our customer evaluated smart cameras for this task with other machine vision integrators with expertise in the smart-camera field. It quickly became apparent that for the complex gauging task and the resolution required—combined with the part repetition and part speeds—smart cameras alone were not suitable as an overall machine vision concept. This project would push the current camera technology, available lenses and illumination, as well as required processing power, to the limits.
The company selected MoviMed for this project given its experience with highly complex, high-performance custom vision solutions. Preliminary estimates and tests with respect to processing power requirements showed that the system would have to run on a real-time operating system for deterministic, real-time image processing needs.
Difficult Optical Challenges
Several factors made this project especially challenging. "At 9 mm diameter and 2-3 in. length, the aspect ratio of the part was problematic," Ritter says. "The specs called for a measurement resolution of 1.5 µm, but even with specialized hardware, achieving the 1.5 µm target would be a formidable challenge. A 16 Mpixel camera with a telecentric lens at 1:1 magnification was specified. However, even with this configuration, our engineers concluded that sub-pixel interpolation would be necessary to increase the effective resolving power of the measurement system."
Sub-pixel interpolation is a purely mathematical method to artificially increase spatial resolution. It is sometimes not practical to design a machine vision system to the exact optical resolution needed. This could be guided by budgetary concerns or the available sensor resolution in relation to available lens choices. More often than not, a compromise between all these criteria needs to be made, especially when dealing with optical systems. You usually can expect a 5-10X increase in resolution using sub-pixel-based algorithms, as long as there are a sufficient number of pixels available for any given feature to interpolate over.
To further complicate matters, there were no standard 16 Mpixel camera lenses available that could meet all the optical specifications. It would be impossible to achieve the minimum spatial resolution of 7 µm using a single camera. So our next best solution was to use two cameras (Figure 1) and a prism that would let us fold one of the cameras out of the way for the 1X optics we needed. As a result, we had to calibrate two camera imaging spaces and combine them as one.