AI Advances in the Automotive, Aerospace, and Defense Industries

By Dick Slansky

ARC Report Abstract


Companies across all industries are examining, researching, and implementing artificial intelligence (AI) technologies and applications for their product design and manufacturing processes.  However, the automotive and aerospace & defense (A&D) industries in particular have emerged as leaders in both adopting and applying AI effectively.

Both industries have a history of early adoption of disruptive technologies to advance both product development and manufacturing processes.  Earlier research into AI and cognitive computing has resulted in real solutions being applied to real-world processes.  In addition to robotics, additive manufacturing, and other disruptive technologies, the automotive and A&D industries were relatively quick to recognize the potential of AI and readily embraced the science and technology it spawned. As both industries have developed and implemented their respective roadmaps for digital transformation, AI and the science and technologies under the AI umbrella have become significant elements.

Executives in both automotive and A&D have stated that adopting and implementing AI across their respective enterprises is a necessary step in moving their businesses forward and maintaining industry leadership in emerging sciences and technology.  These executives also indicated that AI will help advance other technologies such as augmented reality, generative design, additive manufacturing, intelligent edge devices, and more advanced robotics.  The rapid rate of adoption of AI in these industries appears to be a sign of the times and companies that have an actionable AI strategy are raising their own bars.

AI Becoming Important Part of Aerospace & Defense

Automated systems have historically been an important element of the A&D industry from the cockpit to the factory floor.  We’ve seen a steady progression from the first use of autopilots and other automated systems toward future autonomous avionics systems. Additionally, automated factory production systems have evolved from programmed control systems to machines and production systems based on predictive, AI Advances Smart%20Cockpits%20Enabled%20by%20AI.JPGprescriptive, and even autonomous self-healing systems enabled by AI/ML algorithms.

This move from automated systems to autonomous systems would result in a significant change in the cockpit, from avionics systems that previously processed information and presented it to a pilot to enable him or her to make an informed decision, to autonomous avionics systems that can make intelligent decisions.  This would further reduce, or potentially even remove the need for pilot functions in the future.  Clearly, pilots will remain in the cockpit for commercial carriers for the foreseeable future as AI systems become much more robust.  In the defense sector, however, sixth-generation military aircraft are being currently developed that will be capable of mission operations in a completely autonomous mode.  In fact, highly sophisticated autonomous drone aircraft are already a reality in the military. 

Top technology investment areas for aerospace manufacturing include advanced analytics, cloud computing, modeling and simulation, IoT platforms, and optimization and predictive analytics. AI and subsets of AI like machine learning (ML) will drive much of this technology in actual implementation.   

In the factory production areas, ML is helping to improve and optimize the production process in several ways.  These include reduced occurrence of equipment failures to keep the production rate humming and reduce expensive downtime. ML-based algorithms can access and analyze very large volumes of date from vibration sensors in machines to detect and predict machine anomalies and failures. Moreover, ML can be prescriptive to determine how to best fix and prevent problems. And, ultimately, ML algorithms can orchestrate a complete self-healing autonomous production environment of machines and assembly lines.

On the product design side, AI is powering the next generation of engineering design.  Generative design is finally becoming a real and useful element of the overall design process.  This is due largely to AI-enabling algorithms that not only help the designer produce the optimal fit, form, and function based on requirements engineering; but learns how an individual engineer goes through his or her design modeling methods and personalizes and tailors the process for that designer.

AI is an integral part of the design process for additive manufacturing (AM) in aerospace. In designing parts for aircraft, achieving the optimal weight-to-strength ratio is a primary objective, since reducing weight is an important factor in airframe structures design. Today’s PLM solutions offer function-driven generative design using AI-based algorithms to capture the functional specifications and generate and validate conceptual shapes best suited for AM fabrication. Using this generative functional design method produces the optimal lightweight design within the functional specifications.

AI and ML are being used to determine the optimal production processes in aerospace manufacturing. Prescriptive analytics combine Big Data, mathematical statistics, logic, and ML to empirically reveal the origins of the most complex production problems and then suggest decision options to solve them.  ML-based production intelligence systems use pattern recognition technology to analyze existing production data for both product and process and identify patterns of what works (best practices) and what does not (risk situations).  These patterns are translated into a form of human-readable rules that are then applied to manufacturing operations for best practices. Aerospace manufacturers have used this method to optimize advanced composite manufacturing processes.


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Keywords: Cognitive Computing, Digital Roadmaps, Generative Design, Augmented Reality, Additive Manufacturing, Advanced Robotics, Production Analytics, ARC Advisory Group.

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