INDUSTRY 4.0/SMART FACTORIES
minimise the occurrence of faulty seals, thereby eliminating the risk of a complete product batch being rejected by retail customers. Machine learning: bridging the experience gap
So far, I’ve only talked about harnessing AI to make machines smarter. The other development trajectory for AI is making people smarter. Data can be returned from physical assets - in this case highly experienced workers - and pattern recognition applied. Put simply, the skilled operator trains the machine and the machine trains the unskilled operator. In our laboratory, we are currently experimenting with AI-driven machines that ask operators to assemble products and record how they do it to discover the smartest way of performing this task, so that this technique can be taught to other operators.
Another industrial application for machine learning might be the use of AI to establish what actions the operator should be performing on the machine. If the operator’s hands move in the wrong direction, for
example, this generates an alert.
ONLY SMARTIES HAVE THE ANSWER Enterprises that are well advanced on their digital transformation journey will be best placed to harness the value of AI - whether that is for identifying and training best practices, predicting failures or monitoring running conditions. However, businesses at the start of their journey shouldn’t be deterred from exploring AI. When ordering a new machine, make sure that it has the functionality to generate data for AI purposes. You don’t have to know what data you require - you just need to know the right questions to ask your machine builder. Also, start small and take a step by step approach - human DNA has evolved over millions of years and so it is unrealistic to expect machines to emulate the human brain in a matter of months.
Tim Foreman, Research and Development Manager Omron
www.omron.co.uk
FACTORY&HANDLINGSOLUTIONS | MAY 2023 15
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