Advanced Imaging


Advanced Imaging Magazine

Updated: January 12th, 2011 10:01 AM CDT

Why Not Automotive?

Even today there is the perception that 'vision is hard, vision doesn't work'
A body panel stamped with a unique vehicle identifier
A body panel stamped with a unique vehicle identifier. The same information is shown in man-readable alphanumeric code, linear bar code and 2D bar code.
An engine block pin stamped with serial number and process data
An engine block pin stamped with serial number and process data. This is a portable data file, since the birth history information travels with the part.
An error-proofing application
An error-proofing application in the final assembly stage for correct assembly of drive belts on an engine. This belt is not properly seated on the pulley, as seen on the right.
Process verification application
Process verification application in the body shop to verify sealer bead diameter and location.
vision-guided robot application
In this vision-guided robot application in an engine plant, the robot picks the engine head from dunnage and assembles it to the block. The camera and lights are on the robot end effector above the part.
stamping application
The stamping application shows robotic loading of sub-assemblies into a shipping rack using vision offsets to precisely guide the part onto the hook. The camera and lights are robot-mounted on the end effector.

By Valerie Bolhouse

The automotive industry is a visible but surprisingly small percentage of the overall machine vision market. According to the Automated Imaging Association 2006 market study, automotive represented just 6.6 percent of the $1.2 billion revenue for application specific systems and 10 percent of the installations. When you consider that automotive drove machine vision development in the early days—the early-to-mid 1980s—it is surprising to see how poorly represented the automotive sector is.

There are a number of reasons for that, but many of the obstacles to implementation can be overcome with an understanding of the issues and concerns of applying this technology in automotive plants. Despite all the advances in operator interfaces and application software, most people in automotive still consider machine vision hard. It requires multi-disciplinary knowledge, and unlike robotics, is more abstract than physical. Automotive processes and equipment lack the standardization you see in semiconductor or electronics manufacturing. Each application must be custom engineered. As a result, many times less elegant, more costly alternatives to machine vision are preferred. Application of the technology, not the technology itself, often determines success or failure. And because of some poorly integrated systems, most automotive plants have a history of failed vision systems and are reluctant to risk failing again.

The automotive industry actually has four different sectors when you consider equipment and process requirements: components, stamping and body, paint, and final assembly. The components sector consists of both internal and external suppliers. The internal suppliers provide engines and transmissions, while external suppliers provide seats, electronic modules, brakes, and all the other components that make up a vehicle. The stamping and body shop form and join sheet metal panels together to form subassemblies, and then combine the subassemblies into a full body. The bodies are then transported to the paint shop, where sealer is applied and the vehicle body is treated and painted. In final assembly, the drive train and all other components are assembled to the vehicle body. The unit is then inspected, tested, and shipped.

There are product and process attributes of any manufacturing process that determine the applicability and cost of implementing machine vision:

  • The size of the product, size of the feature, and precision requirements. These all impact the vision system camera resolution requirements. The more resolution required, the more cameras are needed to solve the application. This generally has a linear relationship with cost and complexity. Handling the typical false rejects that most vision systems generate also is an issue for large parts. Many small parts can be shuttled to a reject conveyor for later analysis by an operator. You cannot do that as easily with large parts such as hoods or fenders from a car, or even a completed car body.
  • The next attribute has to do with how the product is transported. Parts located precisely in a stop station are the easiest to handle. Continuously moving lines can be accommodated, but require additional engineering and hardware, and are more complex. Parts in an uncontrolled or unfixtured location are difficult to handle. The "anything, anywhere" problem is easily solved by an operator. The vision industry has yet to deliver a robust, cost-effective solution to this problem.
  • Cycle time. Three to five seconds is ideal for most generic vision systems. There is ample time to acquire images, process them, and communicate with external devices. When cycle times become shorter than that, you have to start looking for ways to decrease the vision system cycle time, such as more hardware or more expensive systems to work faster, which increases cost. If machine cycle times are much longer than that, the vision system ends up sitting idle for the majority of the machine cycle. This hurts cost justification, because you end up paying for equipment idle time as well as the value add time.
  • Current level of automation. It is difficult to implement an island of automation in a sea of manual processes. If parts feeding and machine control are set up for a manual operation, it will require a great deal of peripheral change in the station to accommodate vision. Even if you can cost justify it, the automation cell never quite fits in with the other processes in the plant.

With these factors in mind, you can look at the four sectors of manufacturing seen in automotive, and understand the current rate of application of vision and issues with future installations.

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