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Real World Case

Gulf States Paper Corporation: MachineVision System in ManufacturingWith vigilance and accuracy that humans cannot match, tireless electroniceyes are inspecting and guiding an ever-widening array of industrial process.Machine vision system, the technology that join these unblinking eyes tocomputers that interpret what they see, not only steer robots placing doorson car bodies but also cull blemished vegetables from frozen-food

processing lines and make sure that drug capsules go into correctly labeled

packages.A recent tour of the manufacturing scene reveals some of the many newways in which machine vision systems are guiding production andsafeguarding quality:

At a southern sawmill, laser vision system measure the shape of eachlog and calculate how to slice it into boards for the best profit under current market conditions.

To meet dimensional standards hitherto unattainable, a battery of lasers at Mercedes-Benzs new U.S. plant quickly makes scores of

measurements of newly made vehicle bodies. Taking over an inspection job that successive miniaturizations haverendered overwhelming for humans, high- magnification machine-vision system at semiconductor plants hunt for microscopiccontamination that can turn expensive silicon wafers into trash.

Gulf States Paper Corporations new $40 million lumber mill in Moundville,Alabama, can saw 3,000 logs a day. A key part of the equipment that makesthe place tick is a $425,000 pair of machine vision systems built by

perceptron. The devices are designed to remove much of the guesswork from cutting decisions.Southern yellow- pine logs arrive at the mill by truck, mostly from Alabamatimberland owned by Gulf States paper. A huge cranes loads the logs onto aconveyor, where they must pass through another type of scanner, an X-ray.Were looking for fence wire, nails, and bullets, any of which can make amess of an expensive saw blade, explains Wood Product Manager Griff Stanley.

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Logs free of metal move to the first perceptron gauging station, wheredegree of taper and curvature are measured. The system decides how to cutthe logs cross-wise into shorter lengths before they go to the next station to

be sliced length wise into planks. An operator positions rotary saws thatautomatically make the cuts. The solution calculated for one log was sawingit into 14-foot and 12-foot lengths, plus a 1-foot throwaway section that was

judge too gnarly to mill. Next, the shortened logs are moved into a staging area where an operator uses a joystick to manipulate spiked steel rollers that feed them into a second

perceptron station. Inside, a bank of laser cameras projects thin stripes of redlight into the logs; as the light is reflected back into video sensors, thesystem determine their circumference by triangulation. From these slices of data, the computer can construct an image of the entire logs volume.By paying heed to market conditions, the second vision system calculates

the /mix of plank sizes that will yield the least waste and the most profitfrom each log. On a video display in the operators booth, up pops a colorfulcartoon image on the log in the scanner with a diagram revealing the mixedstack of planks into which the computer proposes to cut it.We produce both one and two- inch thickness of lumber here, in lengthsranging from 8 to 22 feet, Stanley says, so were paying close attentionevery day to prices and inventories of different sizes in the lumber market.For one log passing through, the best cutting solution in todays market is tomake a 12-foot one-by-four plank from one side, and a stack of two inch-thick pieces from the area between.So this log is sent along, carried by clattering conveyors first throughcheaper heads and band saws that square it off, then through rotary bladesthat automatically cut it into the planks the computer has chosen.Says Stanley: We used to make all these decisions about cutting up logsusing plain old human judgment, and theres no doubt that the machineryssmarter most of the time.

Case study questions

1. How do machine vision systems contribute to higher profits andimproved product quality and safety?

2. What are the business benefits of machine vision system to gulf states paper?

3. What other types of information system are needed to support themachine vision system at Gulf States paper? For example, how do youthink information on lumber market prices is provided to the system?