Automating and integrating AI in Aerospace is a complex and high-stakes endeavor, given the industry’s low tolerance for errors. Yet, it’s crucial for industry advancement to innovate processes, expedite development, production, and operations, while reducing costs and ensuring maximum safety.

Is integrating AI in Aerospace a viable strategy or merely a marketing ploy to attract investors amidst AI hype? We delve into this debate and explore existing AI use cases to assess their potential and utility.

The prospects and market overview

The global market for AI in aviation is projected to reach $5.6-5.8 billion by 2028, with an annual growth rate of 43.4%.

The development prospects and efficiency potential are significant. According to Valoir research, 40% of the aerospace industry’s workday can be automated with AI, with 20% already automated in the last two years.

Furthermore, by 2026, the global commercial aircraft fleet could generate 98 million terabytes of data annually (flight recorders, operational systems, and personnel), offering a twofold positive impact on industry development. It demands enhanced data processing efficiency while serving as fuel for model training, testing, and AI technology advancements in aviation.

9 Benefits of Focusing on AI Integration in Aerospace

1. Swift and effective resolution of complex industry challenges in the most optimal manner.

2. Providing the necessary foundation for data-driven decisions and more accurate forecasts.

3. Stimulating new, more efficient inventions and accelerating the development-to-deployment cycle.

4. Enhancing safety, particularly in areas heavily reliant on human factors.

5. Ensuring safety for both individuals (users, personnel, third parties) and equipment, software systems, and the data they contain.

6. Cost reduction.

7. Process automation of routine tasks.

8. Optimization of user experience and improved customer orientation.

9. Assisting in overcoming the top aerospace talent gap.

Are these merely theoretical advantages? No, they are entirely measurable in monetary terms. Here are just a few illustrative examples:

  • When Swiss International Airlines started using AI to improve efficiency, it reported being able to optimize more than half of its network flights and save over $5 million USD within a year (Fortune).
  • Targeting satellite constellations costs hundreds of millions of dollars, yet with AI, expenses could plummet to $10 or $15 million (Phantom Space).

10 Key AI in Aerospace Use Cases 

1/ Design, Testing, and Production Optimization:

  • Generative algorithms (GenAI) consider specific factors like aerodynamics laws and durability, enhancing design efficiency.
  • Diagnosis of potential failure points, testing automation, and modelling and simulation streamline production and scenario rehearsal.
  • AI in aerospace advances development by creating and refining full digital twins or specific models and simulations of aerospace systems, enabling real-time monitoring and optimization, streamlining design, testing, and safety protocols while reducing expenses. It harnesses machine learning and advanced analytics to track industrial data from HMI/SCADA systems, alarms, events, and environmental variables.

For instance, AI optimizes aircraft designs for different weather conditions, fine-tunes wing designs for various flight scenarios, reduces fuel consumption, manages turbulent flows during testing, refines measurement techniques, and aids in optimizing aerospace alloys and preparing autonomous vehicles, all contributing to efficiency and safety in the industry.

Notably, companies like GE Aerospace and Siemens Digital Industries Software utilize AI for developing automated aircraft engine testing systems, analysing sensor data, and detecting potential faults before they occur.

2/ Process Automation in Manufacturing

  • Robotization and automation of routine, complex, and slow procedures.
  • Automated quality control to reduce human errors and ensure compliance with safety standards.
  • Decreased waste, downtime, production delays, reduced costs, and increased productivity.
  • Detection of structural damages and durability.

AI-Powered 3D Printing

Aerospace company Relativity Space manufactures rockets almost exclusively through 3D printing. Its groundbreaking metal printer, “Stargate,” stands as the world’s largest as of 2023. Leveraging artificial intelligence and machine learning, it controls and optimizes the printing process, producing intricate geometric shapes of rocket components.

Stargate 4th generation metal 3D printer. Source: Relativity Space

3/ Optimizing Fleet and Business Management and Planning

  1. Managing aircraft schedules and coordination to prevent collisions.
  2. Fuel efficiency optimization: strategic, economic, and technological analysis, along with real-time monitoring and management of fuel expenses for specific aircraft, routes, and airlines.
  3. Planning established routes considering climate models.
  4. Crew and personnel scheduling involves planning crew composition, taking into account variables such as flight volume, reserve crew allocation, holiday schedules, layovers, stops, etc.
  5. Predictive analytics, enhanced by AI for the aerospace industry, aids in forecasting various business metrics (demand, seasonality, logistics, etc.). It helps prevent inventory shortages, optimize spare part availability, and reduce costs.

By utilizing AI to improve effectiveness, Swiss International Air Lines preserved $5.4 million in the previous year and optimized over half of its flights.

A McKinsey report indicates that AI can improve supply chain forecasting accuracy by 10-20%, leading to a 5% reduction in inventory costs and a 2-3% increase in revenue.

  1. Predictive maintenance, automated inspection and quality control

AI can analyse aircraft sensor data to predict potential engine, brake, or other critical system failures.

Various applications include:

  • Scheduling and adhering to check schedules, comparing with technical documentation
  • Continuous monitoring of systems and sensors
  • Prediction analytics regarding failures, malfunctions. AI-based systems can detect issues before they become serious, thanks to constant monitoring of various sensors and components.
  • AI-Powered Visual/Acoustic/etc. Inspections
  • Maintenance data analysis (processing existing data arrays, where humans may overlook or take longer)
  • Tuning to more optimal parameters based on collected data.

Particular benefits of this case include:

  • Reduced downtime
  • Lower product/process failure costs
  • Minimized expenses on unnecessary part replacements
  • Enabling comprehensive data collection for faster root cause understanding and analysis
  • Implementing more efficient spare parts management.

Notable examples

Airbus and Palantir Technologies offer AI airline solutions such as Skywise, a Big Data Analytics system, a specialized industry data platform that integrates in-flight, engineering, and operational data to tackle challenges in airline operations.

Rolls-Royce’s R2 Data Labs developed an Intelligent Borescope using imaging processing and computer vision to reduce engine inspection time by 75%, saving up to £100 million in inspection costs over five years.

Source:  Rolls-Royce

A notable and important example of scientific research is using ML and the Internet of Things (IoT) to forecast thermal characteristics in aircraft wing anti-icing systems.

Intelligent Prediction of Aircraft Wing Anti-Icing System (source)

4/ Assistance of AI in Flights and Flight Safety

Intelligent flight management systems:

  • Real-time air traffic forecasting and route adjustments based on AI, resolving conflicts and preventing accidents using AI algorithms, depending on current local weather, traffic, and other conditions.

Lufthansa Airlines employs AI to predict winds in Switzerland with greater precision. Improved wind forecasting has increased accuracy by 40%, helping to avoid flight delays and cancellations at Zurich Airport.

  • AI can detect anomalies in aircraft systems in real-time, preventing accidents and enhancing safety.
  • Integrating AI with spatial mapping tools enables real-time data calculation and analysis, thereby assisting in navigation and auxiliary piloting.
  • Optimizing current air traffic management: AI enhances coordination between controllers and pilots.
  • Multilevel algorithms can accurately analyse weather forecasts, enabling the use of AI in airline industry to avoid adverse weather conditions and reduce flight delays.
  • Decision support and assistance to pilots in critical situations: AI can assist pilots in emergency situations by autonomously analysing scenarios and taking control if necessary.

NASA collaborates with IBM Research to utilize generative AI for creating a geospatial foundation model, leveraging satellite data. This enables geospatial analysis to be conducted three to four times faster than traditional methods.

Flight Communications

Integration of multimodal models for processing incoming data, processing, optimizing, and delivering necessary data in the required format is essential.

This is particularly critical considering that communication issues often stem from unstable connections, poor reception, and interference. Not only do these problems cause discomfort, but they also pose a direct safety threat. Hence, any endeavour aimed at addressing this challenge is of utmost importance.

Overall, as the electromagnetic spectrum continues to be saturated with commercial and defence communication tools, radars, and household electronics, the need for more efficient and adaptive obstacle removal remains a common requirement.

Lockheed Martin AI Center emphasizes this aspect with the establishment of the Cognitive Signals and Systems team.

Real-time speech recognition software may help to interpret air traffic controller communications accurately and promptly, providing pilots with clear instructions. This technology ensures pilots stay connected with air traffic controllers and receive crucial support during critical moments.

It is crucial for such systems to seamlessly integrate into existing avionics systems to be applicable across various general aviation aircraft.

AI and Unmanned Aerial Vehicles (UAVs)

This symbiosis play a significant role, particularly in tasks requiring inspection, aided by AI technologies.

Companies like Boeing have demonstrated successful trials of collaborative autonomous flight systems.

In airspace safety, systems like Iris Automation’s collision avoidance system (detection range: 1.38 km) utilize AI for enhanced detection and situational awareness, contributing to a safer airspace.

Source: Iris Automation

5/ AI + CX: Enhancing User Interaction and Personalizing Customer and Passenger Experience

  • Analysis and management of passenger flow, queue management, ensuring necessary procedures, processes, and protocols.
  • Automating customer support and developing self-service systems – from GenAI chatbots and agents to multimodal self-service kiosks, equipped with text, tactile, audio, and visual sensors, providing real-time flight status and delay information (as seen in Singapore Changi Airport)

JetBlue calculated benefits from implementing a chatbot – the customer service trimmed chat times by 280 seconds, saving 73,000 hours of operator time.

  • Models, agents for personalized user experience – from flight recommendations to interactive personalized entertainment and service systems during flights.

Amsterdam’s Schiphol Airport analyses customer behaviour and preferences using predictive analytics to provide individual recommendations for boarding, flight information, and travel advice.

6/ Safety and Threat Detection

  • Biometric identification programs are slated for implementation in 77% of airports over the next five years. Facial recognition technology is already deployed in major airports for passenger screening during customs clearance.
  • Behavioural anomaly detection for identifying suspicious individuals.
  • Automated baggage inspection systems, including explosive detection, prohibited item detection, and 3D-CT technologies, are widely utilized in airports.

The U.S. Department of Homeland Security’s transportation security laboratory evaluates AI and machine learning technologies.

  • Cybersecurity Measures. Robust cybersecurity systems combined with AI models safeguard company data, including commercial/military secrets.

Notable examples: Cyber Assured Systems Engineering (CASE) by DARPA, Airbus Cybersecurity, Boeing Defense, Space & Security (BDS), etc.

7/ Optimizing Market Strategy, Pricing, and Revenue Management for Airlines

  • Analysis and forecasting of market metrics, demand, and business challenges using AI, Big Data, internal statistical datasets, and open data analysis.
  • Competitor monitoring and analysis.
  • Market size forecasting.
  • Optimizing pricing policies and market strategy based on data insights and predictions.

Delta Air Lines has begun using AI to assist in pricing and operational dissemination of procedures among booking agents. Delta aims to increase its asset value by 2% by utilizing AI technology to tackle complex data-intensive tasks.

8/ Simulation-based training for pilots, dispatchers, and ground staff.

From AR/VR simulations with feedback to comprehensive AI-enhanced training programs.

Simulation environments for pilot training to create realistic and complex scenarios.

Emirates Airlines intends to utilize generative AI to enhance flight attendant training, partnering with AWS for immersive extended reality platforms.

9/ AI Serving to Defence Industry Needs

From drone swarms tailored for combat to the development of aerial weapons and strengthening aircraft, AI plays a pivotal role in defence applications.

  • Battlefield reconnaissance: AI aids in local area reconnaissance and battlefield analysis.
  • Military strategic models: Systems like Gospel (Israel) and Palantir AIP (USA) contribute to global strategic management.
    • Electronic warfare (EW): Cognitive EW solutions, utilizing AI and machine learning, modernize EW capabilities.
    • Combat identification and automatic target recognition (ATR): Passive and active sensors onboard aircraft facilitate autonomous operation without GPS, communication, or pilots.
  • Drone technology: 
    • Smart multi-component drone systems, exemplified by the Ukrainian Saker Scout, demonstrate machine vision capabilities.
    • Tactical drone swarming systems

Lockheed Martin‘s AI solutions empower pilots and commanders for swift decision-making, focusing on reconnaissance, observation, and ISR operations where standard communication is compromised.

Source: Lockheed Martin

10/ Utilizing AI for Sustainability to Reduce Environmental Impact

Implementing eco-friendly production and operations, utilizing alternative energy sources and safe materials, reducing carbon footprint, and saving energy costs. Developing programs and specific technological solutions to minimize emissions and noise pollution.

Conclusion

Overall, AI holds significant potential in the aerospace industry. By enabling more efficient and precise testing, developing more accurate models and simulations, creating digital twins, predicting potential failures, optimizing design, and monitoring real-time performance, AI can enhance safety, reduce costs, and improve productivity in the aerospace sector.

 

Seeking aerospace AI solutions? Let’s chat. We’ve got what you need.

 

Author: azakharchenko