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Technologies leading the fourth industrial revolution

13th February 2017


Technologies leading the fourth industrial revolution. By Dr Kevin Curran

We are in the platinum age of innovation where technology governs almost every aspect of our lives, so it’s no surprise to see technology revolutionising our manufacturing process at rapid pace. But what technological trends will drive the next evolution in manufacturing? These three technologies will take centre stage for the manufacturing industry’s greatest reform.

3D printing

Organisations are now more aware that 3D printing is a real, viable means to reduce costs through improved designs and streamlined prototyping. Open standards are key of course; open hardware and democratised production nature of 3D printing can be seen as ushering in a fourth industrial age. Manufacturers with in-house 3D printers can turn around product refinements much more quickly, therefore reducing their supply chain, removing much of the headache all manufacturing businesses experience. The aerospace and the automotive industry are just some industrial sectors that are making use of 3D printers to bypass more expensive traditional machine milling processes for parts, with more manufacturers expected to follow in the years to come as the technology becomes more affordable and widespread.

Virtual reality and manufacturing

Virtual Reality (VR) can bridge the gap between a mundane 2D experience on screen and a real one. Although its initial impact has been felt in entertainment, non-consumer areas such as manufacturing can also benefit if implemented correctly. Some giants, such as Ford and BAE, have already adopted VR in their manufacturing process; rather than having to build a physical model to see how change might impact a design, they build a VR model, explore it and see how changes to the model or the environment make an impact. 

Essentially, VR gives companies and manufacturers valuable foresight. Ford, for instance, used commercial off-the-shelf Oculus Rift headsets to improve automotive designs by simulating different road and weather conditions – daylight, snow, dusk, ice, rain and traffic – all from a driver's perspective, to lead to more satisfying end designs.

By allowing manufacturers the ability to interact with VR designs in a realistic manner before actually building real-world designs, VR can lead to more cost-effective products and allow greater agility, while still being as immersive and as good as a physical model. Through this, VR allows the identification of potential material weak spots in the early design stages when tweaks are simpler and less costly to address. VR also means designers and engineers from around the globe can see and interact with the same model, which can reduce cost and save time for large multinational companies.

The VR experience will become more realistic. For that to happen however, we need hardware components to continue to drop in price as well as continued research and development in Hi-Res LCD screens, lenses, head-mounted tracking algorithms, directional sound, controllers and VR content generation. We also need more research into proprioception – the sense of where limbs are in space – and how the eyes orientate to the scene when the head moves in order to increase the immersiveness of the environment. 

Artificial intelligence and manufacturing

The basic idea of Artificial Intelligence (AI) problem-solving is simple but its execution can be complicated. First, the AI gathers facts about a situation through sensors or human input, then the computer compares this information to stored data and decides what the information signifies. The computer runs through various possible actions and predicts which action will be most successful based on the collected information. Of course, the computer can only solve problems it is programmed to solve as it does not have any generalised analytical ability. 

AI will increasingly be used in manufacturing and there have been excellent improvements and leaps in technology with regards to perception. AI can build upon computer vision as well as a great variety of other sensing modalities – including taste, smell, sonar, Infra-red, haptic feedback, tactile sensors, and range of motion sensors. Sophisticated AI applications also include the implementation of unconscious physiological mechanisms such as the vestibulo-ocular reflex, which could allow a manufacturing plant to track visual areas of interest, such as a machine while it is moving. 

AI plays a crucial role in machine learning and adapting machine behaviour to modify existing capabilities and cope with environmental changes. AI will allow machines to learn new tasks on the fly by sequencing existing behaviours, and make much more informed manufacturing decisions. To a large extent, humans doing many repetitive automated tasks have already become replaced and as many manufacturing project processes are repetitive tasks, I see no reason why these jobs could not be made more efficient by AI. 

Kevin Curran is senior member of the IEEE

 









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