Today machine learning and artificial intelligence technologies are helping to solve some of the biggest problems facing companies across the industrial spectrum.Combined with other advanced technologies such as the IoT, this is helping manufacturers gain a foothold in today’s Industry 4.0 revolution. Emphasising this trend, the theme of this year’s Hannover Messe is “Integrated Industry – Connect & Collaborate”. For Marc Siemering, Senior Vice President for trade show organiser Deutsche Messe AG,
Connectivity is critical to new business models, such as the increasing number now benefitting from the application of ML and AI.
Today machine learning and artificial intelligence technologies are helping solve some of the biggest problems facing companies across the industrial spectrum. Boosting efficiency and production output in areas such as predictive maintenance and repair, defect detection and the optimization of supply chains, they are...
Today all industry players are taking an interest in artificial intelligence. And automation industry leaders like Rockwell Automation are no exception. The company recently announced they would be investing in the Hive, a Silicon Valley incubator focused on AI applications for industrial automation. DirectIndustry...
Human-robot collaboration is fast becoming a norm in automation, particularly among smaller manufacturers. With the collaborative robot offering flexibility and affordability, the industrial world has embraced the concept of a “cobotic” future.
Mark Gray, area sales manager in the UK and Northern Ireland for...
AI is increasingly used in industry and has found a new outlet in inspection routines. Siemens Gamesa partnered with Fujitsu to finalize an inspection solution that involves AI to identify potential failures of huge wind blades.
The fibreglass blades used by modern wind turbines are massive structures that can be up to 75 meters long But the extremely arduous conditions in which they operate mean that even tiny flaws in their construction can result in a catastrophic failure. Intense inspection of completed blades is therefore necessary – a task that when carried out purely visually by a human operator can take up to six hours per blade.
But at one of Europe’s leading manufacturers of such blades Siemens Gamesa, that approach has been replaced by one involving automated inspection techniques linked to an artificial intelligence database.
Courtesy of Fujitsu
As a result, inspection routines for each new blade can now be completed in as little as 90 minutes. Human eyesight is reserved for close-up examination of possible flaws identified by the automated system, which uses non-destructive ultrasonic scanning technology.
According to Antonio de la Torre, CTO for Siemens Gamesa, this application of AI is just the most recent example of the company’s exploration of its potential:
AI projects have been done at Siemens Gamesa for a couple of years in applications like remote diagnostics of wind turbines or site-specific wind controller optimization. The use of AI technology for blade inspection as quality assurance is relatively new.
The technique is now in use for the Siemens Gamesa production sites at Aalborg in Denmark and Hull in the United Kingdom. This new application will not be the last, however, as the company is now convinced of its potential for further exploitation.
De la Torre confirms the company is currently testing AI technology as part of blade integrity management within the wind service business unit.
Wind farms can be inspected with automated drones that capture images from all rotor blades on-site. The images are analyzed by intelligent software that indicates and assesses all irregularity in the high-resolution images of the blades. This application will be launched as a product within the next year.
The application at Siemens Gamesa uses a basic AI “engine” supplied by the Japanese company Fujitsu which has been customized to the user-specific requirements.
According to Dr David Snelling, program director of artificial intelligence at Fujitsu, the company regards AI as implying the ability to learn independently from explicit programming.
This usually requires large amounts of labelled data and some form of machine learning, for instance using deep neural networks. Many AI use cases are so application-specific that even closely aligned AI engines need to be tuned or adapted to customer needs, but Fujitsu also has a number of generic AI application APIs including translation and object recognition.
Siemens Gamesa’s use of the technology also exemplifies the way that AI can be applied to the greatest effect within industry.
Dr Snelling states,
The most important areas in industry for AI are with complex processes and decision-making where much of the work is tedious or repetitive, but still requiring a significant level of judgement. The result is usually a partnership between a human and the AI, where the final call is still made by an engineer.
For him, the ideal areas for industrial AI implementation, are “non-destructive testing, predictive maintenance and process optimization.”
Moreover despite the sophisticated nature of the technology, both the necessary training and implementation can be quite straightforward.
There are two parts to an AI application – training and operations. In the Siemens Gamesa case the training required moderate levels of computing power – just a small cluster with multiple GPUs. For operational needs a normal PC or laptop is adequate.
Actually getting an implementation up and running can also be quite speedy.
We have had experience with proof of technology requiring only a week to verify the technical approach. For usable deployment a few week to a few months is sufficient.
As such he is confident that AI applications in industry will continue to expand:
This is definitely a growth time both in terms of achieving operational efficiency and exploiting whole new business strategies based on AI. Industries that do not embrace AI soon will fall behind the pack.
What makes a good cobot? Cobots that can be used for a very specific process with one particular tool can be beneficial, but are very restricted. The most efficient use of cobots would be to able to use them for a range of tasks as needed. PIAB tackles this question with Kenos, its new compact vacuum gripper. Kenos is designed so that different grippers can be changed easily and it is able to pick up products of many different shapes. This means the same cobot can perform a variety of different tasks.
Kenos’ system is made up of a modular vacuum generation injection and a gripper unit which are integrated into a separate pump unit. They are connected by a quick-change system. With the right adapter (included in the package) the unit can be used with all common cobot models. Both units have an edgeless design with a technical foam surface and rounded sides making them safe for work alongside humans.
The pump unit uses powerful COAX®SX vacuum technology and the pump itself is a small, lightweight piCHIP unit that is almost silent, making it adapted for operation in rooms with human workers. The piCHIP ensures performance even with low or fluctuating supply pressure as for the same air consumption, COAX® ejectors are up to twice as fast as other conventional ejectors for three times more flow.
The cobot also has configurable suction cups that can be used to handle most materials and fit on most machines.
Camille Rustici is a Video Journalist and the Editor-in-Chief for DirectIndustry e-magazine. She has years of experience in business issues for various media including France 24, Associated Press, Radio France…
Monica Hutchings is a Canadian writer and translator from Toronto who has worked on everything from technical descriptions to academic journals. She is also our in-house DirectIndustry English translator.