sensor reads direct-marked Data Matrix bar codes. Source: Banner Engineering
ship consistently high-quality products while keeping costs down, companies are
incorporating more automated inspections into their manufacturing processes.
Photoelectric sensors can handle simple visual inspections, but complex
inspections require machine vision. The range of machine vision products
available-from off-the-shelf $1,000 vision sensors to custom systems that run
tens of thousands of dollars-create opportunities for automation, while
reducing waste and improving productivity.
The term machine vision applies to optical systems that include an industrial
camera to capture an image and a processor to analyze operator-specified
features of that image. In low-cost vision sensors-sometimes called smart
cameras-the processor is built into the camera; in custom systems, the
processor is in a PC.
Machine vision uses complex processes, but they can be grouped into three main
categories: capture, analyze and communicate.
sensor inspects the integrity of a metal part. Source: Banner Engineering
factors are critical in capturing a good image: resolution, lighting and lens.
- Resolution. The amount of detail in the captured image-the
resolution, expressed as the pixel count-depends on how many light-sensitive
cells the imager has. Unlike a photoelectric sensor, which uses a single
photoelectric cell, a vision sensor has an imager chip containing several
thousands or even a few million light sensitive cells to capture an entire
scene. That pixel count might seem low for those familiar with consumer digital
cameras, but it provides enough information for industrial inspections. The
higher the resolution, the clearer the image, whether the camera is centimeters
or meters away from the object. The tradeoff is speed, which is why operators
should use the minimum resolution necessary for the application.
- Lighting. The lighting must be sufficiently bright and correctly
angled to create contrast that makes the feature of interest stand out from its
background. To that end, a machine vision lighting subindustry has evolved,
offering specialized lighting such as ring lights that surround the lens,
low-angle lights for revealing raised features or texture, or back lights to
emphasize an object’s silhouette.
- Lens. Lens quality determines how much the edges of the image are
distorted, how uniformly bright the image is from the center to the edges and
how accurately the camera perceives color.
sensor verifies that stamped knockouts have been completely removed from the
part. Source: Banner Engineering
purpose of a vision device is to reduce large amounts of image data to several
pieces of relevant information. Vision devices use image-processing techniques
to locate edges, find patterns or locate variations in color or brightness.
These image-processing techniques are preconfigured by an operator, but then
run over and over again at high speed during production. For example, if the
operator sets up the device to detect a full case of a product, the vision
device knows to reject a carton with an empty cell. It can make that judgment
regardless of where the empty cell is located within the camera’s field of
view. It also can inspect asymmetrical objects no matter how they are rotated
within a full 360-degree range.
Low-cost vision sensors come with built-in image processing tools. For custom
systems, image-processing algorithms are written from the ground up, or
customized to suit the application.
Tools fall into two basic groups: linear and area. Both types look for a
transition in the image, but in different ways.
Because they are faster and more precise than area tools, linear tools are the
best choice when the area of interest is predictable. For example, a vision
sensor could use a linear tool such as the edge tool to ensure that vials
rushing past on an assembly line all have their lids tightly
An area tool is the most useful when the location of the target could vary,
such as a carton of plastic bottles that could be missing one or more units
anywhere in the box. An area tool examines the entire region of interest for
any deviation from the norm.
the machine vision device determines whether the results of the image
processing are within operator-defined tolerances, it communicates with a
controller, which triggers an operator-
specified action. For example, an indictor light might go on, signaling a
worker to scrap the part, or a blower or diverter might move the part off the
Increasingly, machine vision is being integrated into networks, using
communication protocols such as Modbus TCP and EtherNet/IP. In some of these
cases, vision devices are part of closed-loop systems that self-correct, based
on data from the vision device. A closed-loop feedback system is more
technically challenging to set up than an after-the-fact inspection, but the
savings in reduced scrap and reworking can make them worthwhile.
Machine vision continues to show great growth as device costs come down and
capabilities increase. The challenge now is to make machine designers aware of
the innovations in machine vision, so they can benefit from this booming