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Making machine vision

Making machine vision robust AUTOMATION What makes a machine vision system robust? Robustness in this context is more than just reliability. It is a reliability that is maintained within the natural variations of the environment in which the system is being used. number of factors come into play here, including influences A from the surrounding environment, object variations and machine vision component effects. Choosing the optimum components for a machine vision system is therefore a challenging task and benefits from the knowledge and experience offered by machine vision systems integrators and specialist suppliers. There is a very large difference between a solution that works in a demonstration lab environment and one that deals with all the variations that an industrial environment will expose the system to. Machine vision requirements Machine vision systems consist of several component parts, including illumination, lenses, camera, image acquisition and data transfer, and image processing and measurement software. The capabilities offered by machine vision have grown exponentially as technology continues to deliver improved performance in all areas. Author: Mark Williamson, Stemmer Imaging, Puchheim, Germany The overall complexity of the system is determined by the specific application requirements. Choosing the optimum components for a robust vision system should be based not only on their ability to achieve the required measurement (appropriate resolution, frame rate, measurement algorithms etc.) but also on external machine and environmental influences and conditions. Especially in an industrial environment, these can include part variations, handling, positioning, the process interface, vibrations, ambient light, temperature, dust, water, oil and electromagnetic radiation. For extremely hostile environmental conditions, it may be necessary to utilise specialist housings to protect machine vision components. A common example would be the use of camera housings in hygienic environments that require washdown capability. However, there are many applications where various environmental conditions can be accommodated using the most appropriate ‘off the shelf’ components. The environmental challenge for components The challenges posed by external factors can have implications both in terms of potential damage to the machine vision components themselves, and the effects that they might have on the actual measurements. This is perfectly illustrated in considering a vision system that has to cope with temperature variations. Many modern cameras are built to work in temperatures as low as -5 ˚C or as high as 65 ˚C without damage. However, increased temperatures can lead to more noise in the image from the camera sensor, but this can be countered by ensuring that sufficient illumination is used to improve S/N. It is also important to recognise that temperature could affect the object being measured. For example, temperature effects can cause expansion or contraction particularly in metal components, leading to variations in their actual linear and volumetric dimensions. 38 WORLD OF INDUSTRIES 2018

Many cameras are available in housings rated to IP65/67 which effectively protects against dust, dirt, water splashes or vapours In 3D measurement systems the change in geometry of the 3D sensor will generate errors, unless the sensor’s calibration has temperature compensation included. Many other environmental conditions can be addressed by choosing the optimum components. For example vibration & shock – many modern cameras are designed with high resistance to vibration and shock. Robot or track-grade cables are available for applications where the camera moves. Lockable connectors prevent them being dislodged by vibration. Ruggedised PCs and embedded computers offer good mechanical stability. Fixed focus lenses in metal mounts with lockable screws provide shock and vibration protection. Filters can provide simple protection of the lens surface. Or what about dust, dirt and water? Many cameras are available in housings rated to IP65/67 which effectively protects against dust, dirt and water splashes. Dust, dirt, liquids or vapours can stick to the LED or the surfaces of the lens system, reducing the light reaching the sensor. This can be overcome by increasing the camera gain, by software processing of the image or by adjusting the output of the LED. These and other factors affect the quality of the images produced by the sensor which is critical since these images are used for the actual measurements. Making measurements Machine vision measurements are handled according to the system configuration. Smart cameras have image acquisition, processing and analysis capabilities embedded within them. Compact embedded vision systems designed for demanding machine vision and automation applications requiring multiple cameras provide image acquisition, processing and analysis capabilities within the processing unit. PC-based systems will have the software on the PC. The accuracy and repeatability of results depends on the particular software algorithms used and their sub-pixel accuracy. High quality software products and libraries often provide more robust software tools than cheaper or open source systems, but often differences can only be evaluated by direct comparison and with varying inspection environments. Today’s vision systems can even tolerate a limited degree of variation in product size and shape, and can recognise classes of natural product with their inevitable variations within them. Even with the most robust vision system, however, external influences can lead to poor measurement results. For example, vibrations can lead to blurry images, while variable part feeding could lead to variable image perspectives. Motion blur can arise when using too long an exposure time to image moving objects. What you see is not what you get One pitfall for the untrained machine vision user is the significant difference between the human eye and the image acquisition system. Eyes automatically adjust to deal with apparent significant dynamic range while a fixed camera is unable to see significantly bright and dark areas at the same time. Sunlight through a roof light or a shadow of a tall machine operator can change a camera’s images image where the human eye would compensate without you even knowing. Planning and specifying a machine vision system Planning, specifying and implementing a machine vision system that is fit for purpose should involve more than simply choosing the most robust machine vision components. One way is to make use of the VDI/VDE/VDMA 2632 series of standards for machine vision, published by the VDI/VDE Society Measurement and Automatic Control, developed in conjunction with VDMA Machine Vision in Germany. Following the VDI/VDE/VDMA 2632-2 process not only allows the determination of an optimised solution but ensures that if proposals are sought from several suppliers, they all follow the same terms and definitions and use a consistent terminology. This allows exact ‘like for like’ comparisons to be made. To raise awareness of how the VDI/VDE 2632-2 standard can help to smooth the successful integration of machine vision into production equipment, Stemmer Imaging holds a number of training courses, in association with the European Imaging Academy. These are ideal for end users looking to embark on a machine vision project, as attendees will learn what questions to ask suppliers, how to evaluate proposals and understand the completeness of any proposal. In this way, users can be confident that they will get a truly robust vision system. Photographs: 01 Courtesy Gefra, 02 Stemmer Imaging, ornaments fotolia Machine vision systems consist of several component parts, including illumination, lenses, camera, image acquisition and data transfer, and image processing and measurement software. All of them have to be planned and specified based on the application requirements. Mark Williamson WORLD OF INDUSTRIES 2018 39