AUTOMOTIVE HUMAN ASSISTANCE APPLICATIONS RELEASED
Another interesting application of machine vision technology in the automotive sector is for the variety of tasks that could be categorised as ‘human assistance’. One company actively
involved in this area is German firm Stemmer Imaging, which is soon to launch its new SC-10 human assistance smart camera system, aimed specifically at the manual assembly market. According to Mark Williamson, managing director at Stemmer Imaging, although other systems focused on this market have been available for some years, they have suffered from the fact that they are ‘expensive and need dedicated integrator expertise to deploy’.
In seeking to address these limitations, he claims that the main advantage of using
the new human assistance camera is the fact that it combines the delivery of work instructions to a screen by the operator, with hints relating to where components need to be placed – in the process validating the fact that the components are fitted in place ‘and recording that all assembly steps have been completed’.
‘This replaces PC-based and paper-based instructions and paper inspection check lists and ensures all assembly steps are completed. Normal vision systems and smart cameras are aimed at automated lines and give no work step guidance to the operator. This camera combines instructions with the checking. It is unique as a simple camera,’ he says. As more and more tasks and functions related to
assembly and production become automated, Williamson predicts that the use of machine vision systems for quality control will continue to grow – and he points to the fact that set- ups such as Stemmer’s own LMI 3D vision systems and Teledyne Dalsa’s 2D vision systems already enjoy ‘good market penetration’. ‘Saying that, manual assembly is still significant. With the demand for improved quality, automating the inspection and validation of manual inspection will grow significantly. Our new camera system reduces the cost as much as 75 per cent over traditional systems, by removing the need for an integrator.
‘Evidence shows this enables a rapid increase in further adoption,’ he says.
of machine vision technologies for quality inspection is German firm MVTec Soſtware, which manufactures a large number of general purpose machine vision soſtware products – in particular the flagship Halcon and Merlic systems – commonly employed for imaging and inspection functions across a wide range of sectors, ranging from medicine, surveillance and semiconductors, to optical quality control and metrology. In terms of the automotive sector, both the
Halcon and Merlic systems have been deployed in a number of production sites, where they are primarily focussed on the inspection of the array of components produced at manufacturing facilities, including simple tasks such as surface inspection, as well as more complex 3D measurements or pose estimation applications like robotic guidance.
Robotic guidance and tracking In light of the large, and growing, number of uses for machine vision in quality inspection, it is, perhaps, a fair bet that the amount of
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automotive companies choosing to embrace such applications will rise over the next few years. Ultimately, Benevento predicts that machine vision systems could soon enable the robotic replacement of human assemblers in ‘any number of applications.’ In his view, this would provide a particular advantage in assembly operations that are dangerous or ergonomically inefficient. ‘Te use of machine vision assisted robotics
in these applications could reduce injuries and benefit workers and the bottom line,’ he says. ‘Also, as more multi-material bodies in white are introduced, machine vision systems could be used to inspect joint structure, fasteners and adhesives in areas where it is not practical for a human inspector to reach. Tis would ensure that quality is “built-in” to each body in white, rather than relying on intermittent tear downs to validate performance,’ he adds. Over the next few years, Richardson predicts
that machine vision core technologies will continue to be deployed across all aspects of automotive manufacturing for ‘error proofing, robotic guidance and tracking applications’ – with the trend in these areas increasingly on ‘improving ease-of-use and integration across the enterprise’. Richardson also believes that deep learning
technology will gradually emerge as a key innovation in automotive manufacturing quality
control – particularly in view of the capability of deep learning to ‘comprehensively analyse’ and evaluate large amounts of data, also known as Big Data – as opposed to relying on task-specific algorithms. ‘In machine vision applications, human
inspectors define and verify individual features manually, whereas emerging technologies, such as deep learning, will offer manufacturers a trainable solution for applications that are less well defined. Deep learning uses soſtware-based neural networks, which mimics the pattern recognition abilities of human intelligence to train on certain features of the object such as colour, shape, texture and surface structure,’ he says. ‘Deep learning technology is most suitable for
demanding OCR applications, locating complex surface and cosmetic defects such as scratches and dents on turned, shiny or brushed parts and identifying tiny paint defects that are not visible to the naked eye,’ he adds. Richardson also argues that machine vision, in
particular smart cameras, are a key technology for the factory of the future – largely because such devices are ‘visible on the factory network and can be easily reconfigured at a moment’s notice. Vision will provide critical functionality in smart factories as it can be used not only to control quality, but to guide or communicate change to downstream devices’. O
October/November 2018 • Imaging and Machine Vision Europe 47
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