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Vision not intuitive enough, say experts at SPS

Anne Wendel, director of VDMA Machine Vision, reports from SPS 2019 in Nuremberg, where vision system simplicity was discussed

Machine vision has become an indispensable part of manufacturing today, for improving product quality, efficiency and safety. However, the enormous potential of machine vision is far from being exploited completely; for that, it must become easier for the user to integrate and use the technology. During the trade show SPS 2019, which took place in Nuremberg, Germany in November, five experts discussed the question of how simple machine vision really is in a panel discussion organised by VDMA Machine Vision.

Image processing has established itself as a powerful method of checking the quality characteristics of products. In particular, the activities around Industry 4.0 have given a boost to the acceptance of this technology; automation engineers have become aware of its potential, know about the advantages of ‘seeing machines’ and want to exploit the possibilities of image processing.

However, in the opinion of many experts, the use of machine vision is still not intuitive enough, as Rainer Schönhaar, from the machine vision team at Balluff, explained: ‘In the past, three people were needed to implement systems with machine vision components: one for system planning, another for the control technology and a third for machine vision. The communication between these worlds must become easier in order to increase the user acceptance of image processing.’

Andreas Waldl, integrated vision product manager at B&R, agreed: ‘Nobody is willing to invest a lot of time for communication between the automation and vision areas any longer. The integration of image processing in automation systems must be as easy as possible in order to provide economical solutions.’

Standards key to success

Dr Klaus-Henning Noffz, director of new business development at Basler, considers the communication standard Open Platform Communication, Unified Architecture (OPC-UA) to be key to the success of machine vision in automation. There are already OPC-UA companion specifications for robotics and machine vision available, which define the communication between these two sectors for automation. ‘The willingness of the industry to develop OPC-UA together and thus establish widely accepted standards for vision hardware and software was a very important step for merging automation and machine vision.

Peter Keppler, director of corporate sales at Stemmer Imaging, agreed that OPC-UA makes the connection between these two specialist areas much closer and increases user acceptance: ‘This standard definitely helps to create new possibilities, for example in coupling machine vision and robotics.’ As an example, Keppler cited cobots, robots working closely with human co-workers. ‘Here, machine vision plays an important role in safely eliminating accidents or incidents between humans and machines. For absolutely reliable processes, it is necessary to acquire a large amount of image data, process it quickly and exchange the results between the systems involved without delay. OPC-UA offers the necessary tools for this and many other applications.’

Christian Vollrath, head of computer vision at Wenglor Sensoric, also mentioned the VDI/VDE/VDMA series of guidelines 2632 as a valuable tool for reducing communication difficulties between automation and machine vision systems. ‘You have to speak the same language and be able to name problems that occur in reality in the same way in order to find solutions,’ he said. ‘This international standard for image processing, available in several languages, defines the necessary terminology and simplifies the successful use of specifications.’

Potential for improvements

Standards are an important aspect to make it easier for automation engineers to use vision systems. However, the use of the technology will not become child's play in the foreseeable future, Keppler said: ‘A machine vision system consists of many components. From lighting, optics and camera to software, all elements must be matched to each other in order to solve a specific task. Without a certain amount of expertise, it is therefore usually not easy to make the optimal selection. This applies equally to intelligent cameras and PC systems.’

Moreover, in Keppler's opinion, attempts have been made for years to find a standard, all-in-one solution that can solve any task, that allows all communication options, and that offers the greatest possible flexibility, but is still easy to operate. ‘I think we will see a major change here towards special subsystems that can be used to solve specific tasks.’

The industry is working hard on ways to make life easier for machine vision users. Many experts are hoping for considerable simplification through the use of machine learning and deep learning methods. These techniques should accelerate the time-consuming process of teaching software what good and bad parts look like. However, the experts warn that these techniques are not self-evident: on the one hand, the images required for teaching objects must be available or procured in sufficient number and quality and, on the other hand, the user must be clear exactly how and for what purpose they are applying such methods.

Manufacturers of control equipment are facilitating the use of image processing. For example, B&R offers its own vision system that is completely integrated into the control system. ‘This makes life easier for the user because they can use the operating environment they are familiar with, and now also find options for image processing,’ said Waldl. According to him, the willingness of users to test and use the image processing offered in this way has been increasing for some time.

Intuitive user interfaces for the software are also important to make machine vision systems easier to use. According to the unanimous opinion of the discussion participants, there is still considerable potential for improvement here. ‘It is important for the user that they can quickly reach their goal in a simple way,’ Schönhaar said. ‘Which algorithms are used for this in detail is less important. The software must therefore be easy to use.’

Vollrath also sees great potential for making machine vision more intuitive during the image acquisition process: ‘If you want to detect scratches on a surface, for example, you need to know exactly what type of illumination is best suited for this purpose.’ Intelligent systems that suggest the most suitable lighting, optics and camera models to the user on the basis of the conditions are currently still utopian. Users are therefore advised to expand their knowhow, for example by attending training courses.

‘Machine vision has now established itself as an excellent tool for inspection tasks of all kinds. We have now reached a point where the aspect of how to make machine vision simpler is becoming more and more important,’ summarised Noffz. ‘Machine vision has a certain level of complexity due to the multitude of possibilities, and it remains an important task for manufacturers to make its use as easy as possible for the end-users.’

Those participating in the panel discussion were: Peter Keppler, director of corporate sales at Stemmer Imaging; Dr Klaus-Henning Noffz, director of new business development at Basler; Rainer Schönhaar, product management for machine vision at Balluff; Christian Vollrath, head of computer vision at Wenglor Sensoric; and Andreas Waldl, integrated vision product manager at B&R.

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