The implementation of AI (Artificial Intelligence) in aerospace engineering will allow businesses to develop sustainable and lightweight aircraft components.
The aerospace sector faces major challenges such as labor costs, human errors, and health and safety concerns. Along with these challenges, manufacturing and development procedures can be increasingly time-consuming due to industrial inspections that are necessary to evaluate whether a component matches the required specifications. Hence, the aerospace industry is constantly looking for effective ways to speed up development processes in order to meet the growing demand as well as deliver high-quality components.
Artificial intelligence has shown untold potential in multiple industries such as finance, advertising, retail, and healthcare and aerospace is no exception. The implementation of AI in aerospace development can allow businesses to streamline the production of various components and reduce safety issues. Also, AI systems can evaluate feedback from multiple assets and process large volumes of data in a shorter span of time as compared to manual inspections. In this manner, aerospace businesses can carry out effective and quicker inspections of multiple assets. The use of AI in aerospace will also lead to the development of multiple applications that can conserve fuel, enhance operational efficiency, and control air traffic. Hence, business leaders must be informed about AI and its benefits.
Leveraging AI in aerospace
The utilization of AI in aerospace will help businesses in the following manner:
Product design
In the aerospace industry, lightweight and durable components are always preferable for an aircraft. To develop such components, manufacturers can use generative design along with AI algorithms. Generative design is an iterative process, where engineers or designers use design goals as an input along with constraints and parameters like materials, available resources, and allocated budget to develop an optimal product design. In combination with AI, generative design software can enable product designers to explore numerous design options in a short span of time. Using this technology, designers can develop new products that are lightweight and sustainable. AI-enabled generative design coupled with 3D printing can be used to produce various aircraft components such as turbines and wings. In this manner, the implementation of AI in aerospace companies can streamline design and manufacturing procedures.
Fuel efficiency
Globally, commercial airlines consume billions of gallons of fuel every year. According to statistics, it is estimated that global fuel consumption will reach an all-time high at 97 billion gallons in 2019. Hence, conserving fuel is a major concern for the entire aerospace sector. For this purpose, multiple organizations are already manufacturing lightweight components with the help of 3D printing. AI can also help aerospace organizations in improving their fuel efficiency.
An airplane utilizes fuel at the highest rate in the climb phase. AI models can analyze how much fuel is consumed in the climb phase of different aircraft and by multiple pilots to develop climb phase profiles for every pilot. These profiles can optimize fuel consumption during the climb phase. By using AI-generated climb phase profiles, pilots can effectively conserve fuel during flights.
Operational efficiency and maintenance
Airplanes have multiple sensors that help pilots measure speed, air pressure, and altitude. These sensors can be used to collect crucial data like temperature, moisture, and pressure in different parts of an aircraft. AI models can be trained to analyze the collected data to identify abnormal behavior in aircraft components. For instance, sensors installed in turbines can collect data such as rotation speed, air pressure, and temperature of the component. The collected data can be used to train AI models about normal turbine behavior. By analyzing this data, AI models can detect when turbines stray away from their normal behavior and notify concerned personnel about possible defects. Hence, airlines can identify defective aircraft components beforehand and repair them. In this manner, the utilization of AI in aerospace companies can help business leaders improve their operational efficiency by avoiding component failures that can lead to downtimes.
Pilot training
AI coupled with virtual reality can be used to develop simulated training programs for pilots. AI-enabled simulators can be capable of generating a realistic simulation of the flying experience. Also, AI can accumulate and analyze training data to understand every pilot’s strengths and weaknesses to create a detailed report that can be presented to their trainer. The collected data can also be used to develop personalized training programs for each pilot. Personalized training programs can enable pilots to address their individual challenges more effectively compared to conventional training programs.
Air traffic management
Air traffic control is one of the core tasks of airports and airlines. However, as billions of passengers opt for air travel, air traffic control can be immensely complicated. Hence, leveraging AI for air traffic control can be an effective solution. AI-powered intelligent assistants can help pilots in making informed decisions using weather data from sensors and flight data. Using such data, AI-based assistants can suggest alternate routes to pilots in order to make air travel safer and quicker.
AI can also be used along with smart cameras to identify aircraft when they exit the runway and notify air traffic controllers. Using this data, air traffic controllers can clear the arrival runway for the next airplane. This technology can prove to be extremely helpful in low visibility conditions such as fog. In this manner, the utilization of AI in aerospace can help in managing air traffic and reducing bottlenecks on airports.
Threat identification
AI can be used to identify and categorize threats and risks with the help of machine vision, machine learning, and geospatial signal processing. For this purpose, AI models can be trained using images and videos obtained from several aerial vehicles and satellites. The accumulated images and videos can be tagged as normal or suspicious. Using this data, AI algorithms can identify threats in different scenarios. Such AI-based applications can be used for commercial, civil, as well as military purposes. Hence, by leveraging AI in aerospace, business leaders can help pilots in making informed decisions with the help of spatial and situational awareness.
Passenger identification
Security is one of the highest priorities of commercial airlines and AI can offer effective solutions to ensure the security of passengers. AI-enabled smart cameras can use facial recognition to identify suspicious people at an airport. For this purpose, AI systems can be trained with images of people with criminal records. Similarly, AI-powered smart cameras can also be used to detect malicious activity in an airport.
Customer service
The implementation of AI in aerospace industries can enable commercial airlines to offer enhanced customer service. For this purpose, commercial airlines can use AI-powered chatbots that are capable of resolving customer queries. Using chatbots, commercial airlines can provide 24/7, automatic customer support. These chatbots can guide customers while booking as well as canceling tickets. Also, AI-powered chatbots constantly learn by having interactions with various customers to improve their ability to understand a customer’s context in conversations and replicating human responses.
Although leveraging AI in aerospace organizations will simplify various business procedures, employees may still have to actively participate in most procedures. For instance, in passenger identification, facial recognition may make errors that can lead to delays and unreliable security decisions. To sum up, these AI applications cannot function autonomously and require human intervention. However, with further research and development, AI may be capable of carrying out several tasks autonomously and may become a crucial part of autopilot systems.
