In today’s manufacturing environments, more and more companies are integrating machine vision sensors onto their production lines for part inspection, identification, measurement, and guidance. Vision sensors typically offer a compact form-factor, built-in Ethernet communications for factory-wide networkability, and a price tag that makes the investment in vision technology easily cost-justifiable. Perhaps most importantly, the most advanced vision sensors available today rival the performance of higher-priced PC-based vision systems.

However, not all vision sensors are created equal, and with so many choices available today, it can be a daunting task to determine which one is right for your application. Will the sensor you’re evaluating handle the variable lighting conditions in your plant? Will it provide reliable, repeatable measurements over long periods of time? How will variations in the surface of your parts affect the sensor’s ability to read identification codes?

The backbone of machine vision technology consists of software “tools” which make decisions about a part’s quality, location, size, and identity. The types of tools available, and their robustness (i.e., ability to provide accurate results under widely-varying production situations), are key differentiators between today’s vision sensor offerings, and a solid understanding of how a vision sensor’s tools will hold up in your specific production environment is essential during evaluation.

Whether you’re brand new to machine vision, or an experienced user, this article can help you during your vision sensor selection process. It provides an overview of some of the most common vision tools available with today’s sensors, and offers tips on how to assess their performance.

Part Location Tools
Part location tools, available with virtually all vision sensors, are software tools used to find parts within the vision camera’s field of view. This is typically the first step in any vision application – from the simplest robot pick-and-place operation to the most complex assembly verification task – and the one that usually determines whether or not the application succeeds or fails.

While it sounds simple enough, locating parts in today’s production environments can be extremely challenging for vision sensors. This is because many variable conditions exist which can alter the way a part appears to a vision sensor, which is trained to recognize parts based on a reference or “model” image of the part. Variable conditions include:

  • Part rotation
  • Changes in optical scale
  • Inconsistent lighting conditions
  • Normal variations in part appearance

How can you tell if a sensor’s part location tools will be able to accurately and reliably find parts under the range of conditions in your factory?

Loose part fixturing
Line movement/vibration
Rotate the part from 0 to 360 degrees
Camera-to-part distance changes
Move the camera closer to, and away from, the part
Camera optics out of adjustment
Adjust the camera so the part appears out of focus
Part blends in with background Part has poorly defined edges
Present part against a background of a similar color
Inadequate or variable ambient lighting External sunlight
Change room lighting from high to low and open and close aperature
Robot arm or other equipment above part
Use your hand, or another object, to case a shadow over the part
Multiple, unfixtured parts moving down the line
Overlap a portion of the part with another object
Stray light bouncing off part
Shine bright light across various regions of part surface
Movement of conveyor belt Motors and other production line equipment
Slightly jumble the part around under the camera
Inconsistencies in the manufacturing process
Present multiple parts that vary in appearance from process effects

Measurement Tools
If your application involves critical dimensional measurement, you’ll want assurance that the gauging tools are not only accurate, but that they will perform with a very high degree of repeatability.Repeatability can be tested by presenting a part to the vision sensor and have it measure the part at least 25 times without changing part position, lighting, or any other variables. From this, you should be able to plot the repeabilitity of the measurements, and make sure that any variance in the results stays within the measurement tolerance.

In addition to testing for repeatability, it’s a good idea to make sure that the vision sensor has a full suite of gauging tools. This will eliminate the need to write scripted programs to develop functions that are not part of the standard offering.

Image Pre-Processing Tools
Image pre-processing tools allow the user to manipulate the raw image in order to highlight desired features or eliminate undesirable features. This ability can be a key factor in the overall performance of a vision sensor, and should be a part of the standard offering.

Look for products with a suite of image pre-processing tools that will enable you to provide a range of functions such as:

  • Improving the contrast between the edges of a part and its background
  • Filtering out extraneous or insignificant features in the image
  • Eliminating reflections that have been cast off the part surface
  • Smoothing rough textures in an image

By being able to optimize image data in its raw form, the overall accuracy and robustness of a vision sensor can be significantly improved.

Code Reading Tools
Today’s vision sensors should offer reliable, repeatable performance on 2D codes that have been poorly formed, degraded, or those that vary in position from part to part. They should perform well no matter what type of marking method your parts are marked with (dot peen, etching, hot stamping, inkjet are among the most common methods) and on a variety of part surface types, such as glass, metal, ceramic, and plastic.

To evaluate industrial code reading tools, the first thing you want to test for is the sensor’s read rate, which refers to the percentage of codes the vision sensor has read out of all the codes it has “looked at.” To do this, present a well-printed code to the vision sensor, and have it read the code hundreds of times under pristine conditions. Make sure the read rate is 100%, or you could face problems later. For example, a read rate of 99.7% means that one code out of every 350 is not read. At a production speed of 2000 parts per hour, the sensor could discard 2700 good parts per shift.

Once you’ve established the sensor’s read rate, you should run a reliability test to understand how factors like line vibration, variable lighting conditions, and excessive line speeds might be affecting reading performance. To do this, present a large sample of good, bad, and marginal codes to the vision sensor. This will provide a good assessment of how the vision sensor will withstand the range of real world conditions it will need to contend with in production mode.

When shopping around for a vision sensor, it is important to keep in mind that while a sensor may perform with a high degree of accuracy and reliability in laboratory-like conditions, it may ultimately fail on your production line. Thus, an extensive, thorough application analysis and product evaluation is essential. It’s a good idea to make sure that the representative you are working with is a full-time machine vision specialist, so that he or she is able to effectively evaluate your application and help you anticipate any unforeseen issues that may arise. Finally, it is also wise to work with a company that offers a wide range of global product support and learning services to help ensure that your investment in machine vision is a successful one.

Source: Collected


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