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The media often talk about autonomous vehicles. However, it is obvious that driverless cars won’t take over our streets anytime soon. What is talked about a lot less, is that in industrial environments, autonomous applications have been integrated a lot more already. AGVs or Automated Guided Vehicles can execute different tasks in a production environment: transport, stock-taking, locating, etc. For this, they need to know where they are, where they want to go and what is the best route – taking into account other static or moving obstacles on the way.

About 200 years ago, the manufacturing industry launched out of the starting blocks with the advent of the Industrial Revolution. Notwithstanding the many discoveries and revelations since then, we are now at the eve of what might be the greatest metamorphosis in the world of industry. Industry 4.0., the Fourth Industrial Revolution, will enable interlinked appliances to sense their environment and to cooperate in order to produce the most efficient workflow.

In the aviation industry, the use of composites for manufacturing important large components is steadily increasing. Following this shift from metal to composites, the production processes for these components must be adapted as well. However, this evolution is not that obvious within the manufacturing industry as the current machining processes for composite components are insufficiently flexible and efficient.

Flanders Make can look back on a good and successful 2018. Last year, we not only succeeded in realising growth, we also continued to streamline our organisation and bring our innovation ecosystem into practice. In addition, the companies in our network created a few thousand extra jobs.

Predictive maintenance is a new way to schedule machine maintenance works. By monitoring the vibration pattern of machine parts, you can monitor the status of the machine. In this way, you know exactly when a component starts to show faults and can schedule the maintenance works accordingly.

To meet the demand of end consumers for increasingly better, smarter and personalised products, the industry must use ever better performing and more energy-efficient machines. The typical answer to this are increasingly advanced controllers in systems or components. However, current control algorithms have their limits when it comes to performing complex tasks and over time will show deviations caused by ageing machines. Continuously adjusting these algorithms is very labour-intensive and hardly efficient. By deploying learning control, control parameters are automatically and continuously adjusted so that machines continue to work optimally.