Training
Learning in the 21st century
Dan McCarthy, contributing editor for the Association for Advancing Automation in the US, reports on training programmes available for the modern machine vision engineer
T
he automation industry is booming. But if qualified job candidates are out there, they don’t appear to hear
the noise. Skilled help is hard to find, and according to a recent study by Deloitte and Te Manufacturing Institute, the hiring crunch won’t ease up any time soon. Te study forecasts that 4.6 million manufacturing jobs will be created in the US over the next decade, but more than 2.4 million of those jobs will remain unfilled by 2028. Currently, manufacturing executives report an average of 94 days to recruit engineers and researchers. Several factors are contributing to this
shortage, including the looming retirement of baby boomers combined with a perception of manufacturing as a dirty, thankless job by the generations who might replace them. Te latter factor, at least, appears to be changing. Te 2019 L2L Manufacturing Index, which measures the American public’s perceptions of US manufacturing, found that, compared to the general population, young Americans between 18 and 22 years of age are 12 per cent less likely to view the manufacturing industry as in decline and 7 per cent more likely to consider making manufacturing a career path. Tat begs the question: how does one study for this path?
The way forward Te machine vision industry, which plays a vital role in automated manufacturing, shares in the sector’s struggle for talent. It is also contributing to the solution by seeking and finding help from a growing list of partners in academia — as demonstrated by AIA’s Vision Research and Academia page (www.
visiononline.org/vision/vision-research).
Today, students have more options than ever before when it comes to pursuing an engineering school or advanced degree in computational imaging. But while degree programmes provide
general knowledge, many cannot offer hands-on experience. Savvy machine vision companies try to address this by partnering with nearby colleges, universities, and vocational schools. Edmund Optics, for example, works closely with the Rochester Institute of Technology. Even so, it is one thing to learn how a
barcode scanning system works. It is another thing entirely to adapt and build one for a particular application, and yet another to be able to troubleshoot the system when it goes sideways. Each of these tasks represents the progressively higher levels of knowledge and expertise required for barcode readers – a comparatively common machine vision application. Yet, like any highly technical discipline, machine vision is constantly evolving new technologies and applications, oſten rapidly, and even veteran engineers can find themselves falling behind without regularly training and continuing their education. Adapting machine learning to vision
systems, for example, is a specialisation that has risen to the top of priorities for employers such as Andy Long, CEO of automation integrator Cyth Systems, who says the acceleration in the demand for machine learning is staggering. Te US Bureau of Labor Statistics agrees. It reports that demand for machine learning professionals is expected to increase 11 per cent by 2024. Another example is embedded vision,
24 Imaging and Machine Vision Europe • Yearbook 2019/2020
which lies at the intersection between machine vision and embedded electronics. Embedded vision is emerging as a discipline all of its own, with increasing adoption in consumer electronics, automotive and medical applications. Vision-guided robotics, which requires
knowledge in vision, soſtware and advanced motion controls, is another growing field that demands more specialised expertise beyond conventional machine vision system assembly.
Keeping pace with technology Machine vision curricula in academia and industry are adapting to address these industry trends. Courses in machine learning and vision-guided robotics, for example, are being added to a growing number of machine vision courses in colleges and continuing edge programmes. Te machine vision industry is following
suit, as evidenced by AIA’s Certified Vision Professional (CVP) programme. For years, CVP has provided a baseline education for machine vision engineers, according to AIA vice president Alex Shikany, who said: ‘A lot of big names in the machine vision industry use CVP certification as a level set. Tey put new employees through the programme, so they come out with a baseline, broad understanding of the technology.’ Offering two tiers of certification, CVP
attracts a broad spectrum of attendees, including recent college graduates, as well as seasoned professionals looking for a career move. Consequently, it is subject to frequent adaptation to keep pace with industry trends. Te programme is undergoing one such review now, according to Robert Huschka,
@imveurope
www.imveurope.com
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