Artificial intelligence (AI) in the aerospace industry

Artificial intelligence (AI) in the aerospace industry can help companies streamline manufacturing while also addressing safety concerns. Further, AI systems can analyze inputs from various …

March 16, 2022 5 minute
Artificial intelligence (AI) in the aerospace industry,Artificial Intelligence (AI)

Artificial intelligence (AI) in the aerospace industry can help companies streamline manufacturing while also addressing safety concerns. Further, AI systems can analyze inputs from various assets and process vast amounts of data faster than humans.

In this way, aerospace companies can carry out effective and fast inspections of various properties. The use of AI in aerospace can also help create a range of applications that can save or monitor fuel, boost operational performance, and assist in controlling air traffic. It has become crucial for business leaders to explore and leverage the benefits of artificial intelligence. Raytheon, General Dynamics, and Northrop Grumman have also announced AI-based development projects and product releases in recent years. Similarly, companies such as Lockheed Martin, Airbus, and Boeing are investing in AI startups through their venture arms.


Applications of Artificial Intelligence Systems in the Aerospace Industry

1. Smart Maintenance

Maintenance is essential to ensure the efficient functioning of the aircraft. Since servicing is typically conducted on a scheduled basis, the process can be time-consuming and incommodious. Unexpected glitches or malfunctions can result in a significant loss of resources and time. Since 45 percent of industry professionals see fixing unforeseen maintenance problems as a critical way to drive performance, it is no surprise that aviation businesses are increasingly looking into predictive maintenance facilitated by AI. Cloud-based applications have been used by Airbus for data processing, retrieval, and analysis to increase aircraft maintenance efficiency.

Predictive analytics sifts through data from sensors and reports, interpreting and sorting it. As a result, algorithms can detect and track possible faults in real-time and forecast the most appropriate repair times, resulting in more intelligent maintenance schedules.

2. Better fuel efficiency

Aerospace companies place a high emphasis on fuel quality, and even a tiny decrease in aircraft fuel consumption can have a massive impact on a company’s bottom line and emissions. The standard commercial flight consumes approximately 4 liters per second, 240 liters per minute, and 14,400 liters per hour. We can cut fuel consumption by 5 to 7 percent with the aid of AI systems.

AI-powered systems assist in the reduction of fuel consumption. For instance, Safety Line, a French firm, has created a machine learning tool that can optimize climb profiles for pilots before each flight. Since the climbing process uses the most fuel, optimizing this stage results in significant fuel savings.

3. Better Customer Experience

Customer satisfaction and quality of service are especially critical for commercial aviation. AI is one of the ways that airlines can improve their customer engagement and deliver exemplary customer support. Chatbots are an example: AI-based automated platforms that can respond to consumer inquiries in real-time and human-like ways. By automating customer service, online Chatbots can save customers time and effort. Some of the ways in which this can be achieved are:

  • Accurate and Personalized Suggestion on Customer’s purchase decisions
  • AI-assisted Chatbots offer efficient and friendly support.
  • 24×7 Automated Assistance
  • Streamline customer interactions

According to a SITA poll, 14 percent of airlines and 9% of airports currently use Chatbots, with 68 percent of airlines planning to deploy AI-driven Chatbots shortly.

4. Training

Artificial intelligence can be used to improve pilot training facilities by providing a suitable simulation environment to pilots. This is achieved using AI-enabled simulators in combination with virtual reality systems. These simulators may also capture and analyze practice data to create personalized training data using biometrics to monitor and predict individual performance.