FEATURE PROCESS AUTOMATION DIGITAL INTELLIGENCE IMPROVES DECISION-MAKING
The ongoing development of Artificial Intelligence and Advanced Analytics will be instrumental in the advent of the truly smart factory says Mitsubishi Electric
F
actory automation has traditionally provided the food industry with faster,
more reliable and cleaner production capacity - however, with the advent of the smart factory and commercial AI, the factory of the very near future is starting to make production efficiency improvement decisions itself. The first stage of becoming a smart
factory is digital network communication, having the right data infrastructure allows companies to create, move and use that data efficiently. The resultant homogeneous control and fast responsive manufacturing justifies the investment in automation technology. The role of the IIoT in today's factory is
to connect customer demand to a fast and flexible production facility. Once a purchase decision is made then any increase in the speed of response from the manufacturer is a competitive advantage. If the IIoT offers us one thing, then it is the ability to define customer demand instantly and adapt production to suit. Companies that can be flexible enough
to move away from large batch production can also avoid the cost of large stock holding, both at the manufacturer and throughout the distribution chain. Customisation is already a unique selling point for a large number of consumer goods, and the food industry is following suit. Customisation equals profitability in both cases. The transfer of data from a sales operation to a manufacturing site, out to the suppliers and then simultaneously back to the distribution and retail network is the key to responsive, flexible manufacturing. To achieve 'batch size one' profitably and efficiently we must have the connectivity that the IIoT offers. The ability to generate, record, transfer
and process a large amount of data also offers a higher degree of traceability for example, serialisation is already essential for many food, pharmaceutical and consumer products. Better information also allows for continuous improvement at a micro and macro level, generating multiple opportunities for increased efficiency and cost reduction. AI is still at the beginning of its journey
but we can expect it to have a substantial impact on the industrial environment over the next few years. AI is a perfect fit for manufacturing and leading companies are now integrating various AI functions into factory automation equipment.
22 JULY/AUGUST 2020 | PROCESS & CONTROL Advanced Analytics (AA) and Artificial
Intelligence (AI) technologies are extending traditional machine control architectures with more advanced data processing, learning and decision-making capacity. The objective is to deliver increased productivity, efficiency, reliability and accuracy, as well as opening up new possibilities for machine control. AI can, for example, be a driver for
increased productivity. Today, most machines are still built to work within defined margins of capability – perhaps to allow for different loads or speeds or safety ranges. AI technology using deep learning algorithms within the control system enables machines to be driven right up to and even beyond today's margins, boosting productivity without compromising reliability and quality. Applying AI principles to individual
machine processes can already help to reduce auto-adjustment times, synchronise complex systems and offer helpful suggestions to operators. It can even enable autonomous decisions to be made based on measured data in real- time, further optimising the process. Making reliable predictions based on
experience, evidence and guidelines is a fundamental function of human intelligence. AI is no different in this respect, it can contribute toward more effective predictive maintenance, monitoring the condition of components to enable replacement before damage occurs and causes unplanned downtime. We see this already, but deep learning
algorithms are pushing the boundaries further, calculating with more accuracy how long a component can run before replacement. Maybe even compensating for delivery times on replacement parts by slowing the machine down slightly to increase longevity rather than stopping
Getting the most from a Smart factory is a case of being aware of the possibilities, this is where using AI can improve performance and efficiency. Recording the condition and operating profile of devices provides valuable information, for example the status of wear parts and any contamination
[Source: Mitsubishi Electric Europe B.V.]
AI is certainly playing a key role in manufacturing, moving from vision recognition to skill learning and predictive maintenance for failure prevention. When detecting impending faults and informing operators how to fix problems for example, we see AI again coming to the fore. Today, the latest robots, such as the new MELFA FR robot series from Mitsubishi Electric are available with AI functions and can increase the yield in industries such as food and life sciences
[Source: Mitsubishi Electric Europe B.V.]
the production line completely. Similarly, the combination for AA and AI
can be a driver for increased efficiencies right across the production environment and here we get into the realm of Big Data Analysis. AA and AI technologies enable different machine states to be recorded and analysed in real time to recognise the current machine status, detect potential faults and offer recommendations for actions to the machine operator or autonomously initiate remedial actions. Reaping the maximum benefit from this
development will depend on control systems that not only embed these technologies but which also provide higher levels of connectivity. If the full spectrum of data sources on the plant floor can be connected to Edge Computing platforms for efficient processing for example and on to MIS/MES and ERP systems, then the full benefits of AA and AI are realised. What we see then, with control systems
built around AA and AI technologies, are machines that are self-learning and self- optimising. In addition to developing products that incorporate a connection to cloud based AI as a service, IBM's Watson for example, Mitsubishi Electric for one has developed several in-house AI algorithms and services under its own brand to reflect its growing importance. Other examples of developing
technology that play a role in the emergence of the smart factory include Edge computing, which can help bridge the gap between IT systems and plant level automation, increasing OEE by means of digitalisation through the analysis of extracted data (data mining) from production to enable predictive maintenance strategies, and the use of process data for traceability and consumer information, especially in the food sector.
Mitsubishi Electric
gb3a.mitsubishielectric.com
/ PROCESS&CONTROL
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