WORLD OF INDUSTRIES 06/2018 (RU)
WORLD OF INDUSTRIES 06/2018 (RU)
WORLD OF INDUSTRIES 06/2018 (RU)
- No tags were found...
Create successful ePaper yourself
Turn your PDF publications into a flip-book with our unique Google optimized e-Paper software.
Many cameras are available in housings rated to IP65/67 which<br />
effectively protects against dust, dirt, water splashes or vapours<br />
In 3D measurement systems the change in geometry of the 3D<br />
sensor will generate errors, unless the sensor’s calibration has<br />
temperature compensation included. Many other environmental<br />
conditions can be addressed by choosing the optimum components.<br />
For example vibration & shock – many modern cameras are<br />
designed with high resistance to vibration and shock. Robot or<br />
track-grade cables are available for applications where the camera<br />
moves. Lockable connectors prevent them being dislodged by<br />
vibration. Ruggedised PCs and embedded computers offer good<br />
mechanical stability. Fixed focus lenses in metal mounts with<br />
lockable screws provide shock and vibration protection. Filters can<br />
provide simple protection of the lens surface.<br />
Or what about dust, dirt and water? Many cameras are available in<br />
housings rated to IP65/67 which effectively protects against dust, dirt<br />
and water splashes. Dust, dirt, liquids or vapours can stick to the LED<br />
or the surfaces of the lens system, reducing the light reaching the<br />
sensor. This can be overcome by increasing the camera gain, by software<br />
processing of the image or by adjusting the output of the LED.<br />
These and other factors affect the quality of the images produced<br />
by the sensor which is critical since these images are used for the<br />
actual measurements.<br />
Making measurements<br />
Machine vision measurements are handled according to the system<br />
configuration. Smart cameras have image acquisition, processing<br />
and analysis capabilities embedded within them. Compact embedded<br />
vision systems designed for demanding machine vision and<br />
automation applications requiring multiple cameras provide image<br />
acquisition, processing and analysis capabilities within the processing<br />
unit. PC-based systems will have the software on the PC.<br />
The accuracy and repeatability of results depends on the particular<br />
software algorithms used and their sub-pixel accuracy.<br />
High quality software products and libraries often provide more<br />
robust software tools than cheaper or open source systems, but<br />
often differences can only be evaluated by direct comparison and<br />
with varying inspection environments. Today’s vision systems can<br />
even tolerate a limited degree of variation in product size and<br />
shape, and can recognise classes of natural product with their<br />
inevitable variations within them. Even with the most robust vision<br />
system, however, external influences can lead to poor measurement<br />
results. For example, vibrations can lead to blurry images,<br />
while variable part feeding could lead to variable image perspectives.<br />
Motion blur can arise when using too long an exposure time<br />
to image moving objects.<br />
What you see is not what you get<br />
One pitfall for the untrained machine vision user is the significant<br />
difference between the human eye and the image acquisition<br />
system. Eyes automatically adjust to deal with apparent significant<br />
dynamic range while a fixed camera is unable to see significantly<br />
bright and dark areas at the same time. Sunlight through a roof light<br />
or a shadow of a tall machine operator can change a camera’s<br />
images image where the human eye would compensate without<br />
you even knowing.<br />
Planning and specifying a machine vision system<br />
Planning, specifying and implementing a machine vision system<br />
that is fit for purpose should involve more than simply choosing the<br />
most robust machine vision components. One way is to make use of<br />
the VDI/VDE/VDMA 2632 series of standards for machine vision,<br />
published by the VDI/VDE Society Measurement and Automatic<br />
Control, developed in conjunction with VDMA Machine Vision in<br />
Germany. Following the VDI/VDE/VDMA 2632-2 process not only<br />
allows the determination of an optimised solution but ensures that<br />
if proposals are sought from several suppliers, they all follow the<br />
same terms and definitions and use a consistent terminology. This<br />
allows exact ‘like for like’ comparisons to be made.<br />
To raise awareness of how the VDI/VDE 2632-2 standard can help<br />
to smooth the successful integration of machine vision into production<br />
equipment, Stemmer Imaging holds a number of training<br />
courses, in association with the European Imaging Academy. These<br />
are ideal for end users looking to embark on a machine vision<br />
project, as attendees will learn what questions to ask suppliers, how<br />
to evaluate proposals and understand the completeness of any proposal.<br />
In this way, users can be confident that they will get a truly<br />
robust vision system.<br />
Photographs: 01 Courtesy Gefra, 02 Stemmer Imaging,<br />
ornaments fotolia<br />
www.stemmer-imaging.com<br />
Machine vision systems<br />
consist of several component<br />
parts, including illumination,<br />
lenses, camera, image acquisition and<br />
data transfer, and image processing<br />
and measurement software. All of<br />
them have to be planned and<br />
specified based on the<br />
application requirements.<br />
Mark Williamson<br />
<strong>WORLD</strong> <strong>OF</strong> <strong>INDUSTRIES</strong> <strong>2018</strong> 39