Vision systems are a primary consideration for any manufacturer who is looking to improve quality or automate production. Vision systems can be thought of as computers with eyes that can identify, inspect and communicate critical information to eliminate costly errors, improve productivity and enhance customer satisfaction through the consistent delivery of quality products. Primarily used for online inspection, vision systems can perform complex or mundane repetitive tasks at high speed with high accuracy and high consistency. Errors or deviations in the manufacturing process are immediately detected and relayed, allowing control modifications to be made on the fly to reduce scrap and minimize expensive downtime. Vision systems are also deployed for non-inspection tasks, such as guiding robots to pick parts, place components, dispense liquids or weld seams.
Vision systems come in all shapes and sizes to suit any application need, but they all have the same core elements. Every vision system has one or more sensors that capture pictures for analysis and all include inspection software and a processing element that executes a user-defined program, or recipe, defining the inspection. Additionally, all vision systems will provide some way of communicating results to complementary equipment for control or operator monitoring. That said, it is important to know that there are significant and important differences between vision systems that make one more suitable over another for any given application. It is equally important to know and appreciate the importance of choosing the optimal lighting and optics for the job. Failure to do so may result in unexpected false rejects, or even worse, false positives.
There are many variants of vision systems on the market, but for the purpose of this tutorial we will classify them all into two categories – those with a single sensor embedded (also known as smart cameras) and those with one or more sensors attached (multi-camera vision systems). The decision to use one of the other is dependent not only on the number of sensors needed, but also on a number of other factors including performance, cost and the environment in which the system needs to operate. Smart cameras, for example, are generally designed to tolerate harsh operating environments better than multi-camera systems. Similarly, multi-camera systems tend to be less costly and deliver higher performance for more complex applications.
Another way to differentiate the two classes of systems is to think in terms of processing requirements. For many applications, such as in car manufacturing, it is desirable to have multiple independent points of inspection along the assembly line. Smart cameras are a good choice as they are self-contained and can be easily programmed to perform a specific task and modified if needed without affecting other inspections on the line. In this way processing is "distributed" across a number of cameras. Similarly, other parts of the production line may be better suited to a "centralized" processing approach. For example, it is not uncommon for final inspection of some assemblies to require 16 or 32 sensors. In this case, a multi-camera system may be better suited as it is less costly and easier for the operator to interact with.
Perhaps the most important consideration when selecting any vision system is software. You will need to make sure that the capabilities of the software match your application, programming and runtime needs. If they don't, you will find yourself investing more time and expense than you anticipated in trying to conform the system to your expectation. If you are new to machine vision or if your application requirements are straightforward, you should select software that is easy-to-use (i.e. doesn't require programming), includes core capabilities (i.e. pattern matching, feature finding, barcode/2D, OCR) and can interface with complementary devices using standard factory protocols. If your needs are more complex and you are comfortable with programming, you might look for a more advanced software package that offers additional flexibility and control. In either case, make sure that the software you choose is available across vision system platforms in case you need to migrate due to changing inspection requirements.
Selecting the right vision system requires some knowledge and experience. Our automation distributors will work with you to understand your requirements and recommend an appropriate system.
The human eye can see well over a wide range of lighting conditions, but a machine vision system is not as capable. You must therefore carefully light the part being inspected so that the machine vision system can clearly 'see' the features you wish to inspect.
The light must be regulated and constant so that the light changes seen by the vision system are due to changes in the parts being inspected and not changes in the light source.
You will want to select lighting that 'amplifies' the elements of the part that you want to inspect and 'attenuates' elements that you don't want to inspect. In the left picture, poor lighting makes it difficult to read the letters on this part. In the right picture, the lighting has been selected to clearly show the lettering.
Images courtesy of Microscan.
Proper lighting makes inspection faster and more accurate. Poor lighting is a major cause of failure in machine vision inspection systems.
In general, the available or ambient light is poor lighting and will not work. For example, the overhead lights in a factory can burn out, dim or be blocked, and these changes might be interpreted as part failures by the vision system.
Selecting the proper lighting requires some knowledge and experience. Our distributors and lighting vendors will be able to do an analysis of the parts you want to inspect and recommend proper lighting.
Teledyne DALSA works with the following lighting vendors:
Dark Field Lighting
All vision systems support one or more image sensors (or cameras in the case of multi-camera systems). Each sensor needs a lens that gathers light reflected (or transmitted) from the part being inspected to form an image on the sensor. The proper lens allows you to see the field-of-view you want and to place the camera at a convenient working distance from the part.
To pick the proper lens you will first need to know the field-of-view (FOV) and the working distance. The FOV is the size of the area you want to capture for inspection.
Here is a typical example: If the part to be inspected is 4" wide by 2" long, you would need a FOV that is slightly larger than 4", assuming you can position the part within this FOV. In specifying the FOV you have to also consider the camera's "aspect ratio" - the ratio of the width to length view. The sensors used with vision systems typically have a 4:3 aspect ratio, so the example 4" x 2" part would match the sensor dimension, but a 4" x 3.5" part would require a larger FOV to be entirely seen.
The working distance is approximately the distance from the front of the sensor to the part being inspected. A more exact definition takes into account the structure of the lens and the camera body.
From the FOV, working distance and the camera specifications, the focal length of the lens can be estimated. The focal length is a common way to specify lenses and is, in theory, the distance behind the lens where light rays 'from infinity' (parallel light rays) are brought to a focus. Common focal lengths for lenses in machine vision are 9mm, 12 mm, 16 mm, 25 mm, 35 mm and 55 mm. When the calculations are done, the estimated focal length will probably not exactly match any of these common values. We typically pick a focal length that is close and then adjust the working distance to get the desired FOV.
There are other important specifications for lenses, such as resolution (image detail - depends on the sensor resolution and the lens), the amount and type of optical distortion the lens introduces and how closely the lens can focus.
Given all of these issues, we recommend that you work closely with one of our distribution partners to choose the appropriate lens for your application.
A vision system's image sensor converts light from the lens into electrical signals. These signals are digitized into an array of values called “pixels” which are processed by the vision system during the inspection.
The resolution (precision) of the inspection depends upon the working distance, the field-of-view (FOV), and the number of physical pixels in the sensor. A standard VGA sensor has 640 x 480 physical pixels (width x height), and each physical pixel is about 7.4 microns square. From these numbers, resolution can be estimated for your "real world" units. We usually specify resolution as a fraction of a physical pixel, as this is independent of your particular imaging set-up.
The sensors used by vision systems are highly specialized, and hence more expensive than say, a web cam. First, it is desirable to have square physical pixels. This makes measurement calculations easier and more precise. Second, the cameras can be triggered by the vision system to take a picture based on a part-in-place signal. Third, the cameras have sophisticated exposure and fast electronic shutters that can 'freeze' the motion of most parts as they move down the line.
In addition to smart cameras (single sensor vision systems), Teledyne DALSA offers a full range of GigE (Gigabit Ethernet) and Camera Link compliant Area Scan (2D sensors) and Line Scan (1D sensors) cameras that interface with our multi-camera vision systems.
It is important to consider how parts will be presented to the vision system for inspection. If the part is not presented in a consistent way, you will not achieve the desired result. Therefore you will need to ensure that the surface of the part you want to inspect is facing the sensor at runtime.
Next you will need to decide whether the part is to be inspected whilst in motion or stationary. If the part is moving, the motion will likely need to be "frozen" by turning the light on briefly or by using the high-speed electronic shutter feature of the sensor (standard in Teledyne DALSA systems). In this case you will need to provide a trigger to the sensor to let it know when to take a picture. The trigger is typically generated by a photo-eye sensor that detects the front-edge of the part as it moves into the inspection area. If the part is stationary, for example positioned in front of the sensor by a robot, the sensor can be triggered to take a picture from a PLC or the robot itself.
Another consideration is speed. If you are inspecting parts at very high-speed you will likely need to optimize part positioning to reduce processing time. Keep in mind when designing your system that everything consumes processing bandwidth. So, when considering a vision system for high-speed inspection, you should try to determine which of your requirements are critical or just nice to have.