It is long overdue, but what Zapf says in terms of artificial intelligence (AI) can help by capturing accurate data and guiding engineers in design and development.
Not surprisingly, McKinsey’s November 2020 survey reveals that more than half of organizations have acquired AI in a single project, and 22% of respondents say that 5% of all company’s findings are related to AI. And in practice, 71% of respondents saw an increase of 5% or more and received AI.
But this was not always the case. Zapf says that “when used frequently in production,” AI has undergone a change over the last few years. Today, technology giants known for their expertise in AI, such as Google, IBM, and Amazon, have “developed new ways to use AI in other ways,” such as engineering.
“AI is a promising and enlightening space that can dramatically change users’ performance in engineer design, as well as collect requirements for the development of other applications,” says Katrien Wyckaert, director of solutions for Siemens Industry Software.
The result is a huge appreciation for technology that promises to make it easier for machines, sell products faster, and drive new ones.
Simple complex systems
A good example of the power of AI to recreate development is Renault. Responding to a growing number of consumers, the French automaker is developing a number of new and advanced automotive (AMT) systems — systems that not only send out but allow drivers to move gears on a computer with the click of a button.
AMTs are popular among consumers, but making them can be a daunting task. This is because the performance of AMT involves the use of three distinct components: electric motors that move gears, electric sensors that control the flow of traffic, and software that is part of the transmission control unit, which controls the engine. As a result of this crisis, it can take a whole year to experiment with machine learning, machine design, software development, and the whole system.
To address AMT development, Renault turned to Simcenter Amesim software from Siemens Digital Industries Software. The experimental technology relies on neural networks, the unparalleled “learning” AI systems in the human brain. Experts simply drag, drop, and connect images to create an image. When displayed on a screen as a play, the model shows the relationships that exist between the various components of the AMT system. Similarly, engineers are able to predict AMT performance and performance and make any effort early to start, avoiding problems of delay and delay. Instead, by using engines and transmitters as ins-ins while developing electronic devices, Renault has managed to cut its AMT development time by almost half.
Running without sacrifice
Likewise, the upcoming environmental standards are making Renault more reliant on AI. Following the forthcoming standards for carbon dioxide emissions, Renault has been working on the development and maintenance of hybrid vehicles. But hybrid engines are far more difficult to manufacture than those found in electric vehicles such as electric cars, such as conventional cars. This is because hybrid engines require engineers to perform complex tasks such as converting the required energy from several electrical sources, selecting from a wide range of construction materials, and monitoring the performance and cooling of automotive electronics.
“In order to achieve the new creation of a hybrid engine, we need to reconsider the design of the fuel engine,” says Vincent Talon, head of experiments at Renault. The problem, he adds, is a careful review of “several weapons that could affect the final effects of oil and carbon dioxide” is a long and difficult process, made more difficult and time consuming.
“Today,” says Talon, “we don’t have the time to look closely at the various forms of energy efficiency,” says Talon. “Instead, we needed to use more sophisticated methods to solve this problem.”
To learn more about AI in industry applications, go www.siemens.com/artificialintelligence.
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This was created by Insights, the hand of material contained in the MIT Technology Review. It was not written by the authors of the MIT Technology Review.