SPONSORED: AI IN VISUAL INSPECTION Under control
Keely Portway finds out how combining AI with visual inspection in manufacturing can help to reduce errors and increase efficiency
A
rtificial intelligence (AI) is increasingly being used in manufacturing and production
as a way to help automate a number of processes, ensuring maximum efficiency, minimum errors and a reduction of costs. One application is in quality control,
particularly in industries where the consequences of errors could be particularly high – examples include defence, aero, food production, medicine and automotive. A recent example of such an investment
came in the form of a new Nissan production line at its Tochigi Plant in Japan, which features what the company calls the ‘Nissan Intelligent Factory initiative’. Visual inspection is one of the many processes that will be automated, with 11 robots inspecting the body and bumper in order to achieve 100 per cent detection of dust and debris on the paint work. Another six robots conduct inspections of specifications to identify flaws. Results are stored on a centralised management system for maximum traceability.
Working together Of course, every inspection process differs depending on company size and product type, among many other factors. Additionally, though challenges to AI adoption have historically centred on the perception that it will replace human workers, it is accurate to say that in many quality control scenarios, visual inspection by human workers combined with AI technologies actually produces the best results.
Tis is the view held by Ed Goffin,
marketing manager at Pleora Technologies, a company that provides AI for manual and automated inspection across a wide range of industries. He said: ‘We have good relationships with a number of customers
Glass inspection normally involves different illumination methods.
Electronic component manufacturer, DICA has incorporated the image compare plug-in from Pleora to incorporate AI into its visual inspection process
in different types of markets – we ask them about their manufacturing processes and where they see areas in which they could use technology to help. One thing that consistently comes back to us is that there’s still a heavy reliance on visual inspection in a lot of different areas of manufacturing. No matter how much manufacturers incorporate automation and machine vision, there’s clearly still a pretty big role for humans in the inspection process.’ Tis is because humans have many skills
that lend themselves perfectly to performing visual inspection. Humans can also use their senses to aid visual inspection- in food and beverage manufacture, for example – and we have the ability to learn what sorts of things to look out for from other humans and make subjective decisions. Continued Goffin: ‘Many of the advances in machine vision and now
24 IMAGING AND MACHINE VISION EUROPE APRIL/MAY 2022
AI aim to replicate what we as humans do really well. But at the same time, we can get tired or distracted, particularly after an eight- or twelve-hour shift. Attention to detail can start to slip.’ In Goffin’s experience, one of the greatest
advantages of using AI is to add decision support to visual inspection. ‘It’s not looking to replace humans because we bring a lot of value in those types of manufacturing processes, particularly for lower volume but higher value products,’ he said. ‘Te intent is to provide an inspector or operator with the decision support tools that highlight product differences or deviations, or guide them through the manufacturing process to ensure they’re using the right components at the right time.’ Tis is particularly the case when it comes to visual inspection tasks for
@imveurope |
www.imveurope.com
Pleora Technologies
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