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Real-World Applications: Automation Solutions in Aerospace and Energy

POSTED 01/22/2025  | By: Roy Sarkar, A3 Contributing Editor

Manufacturers in industries like aerospace and energy are undergoing significant transformation as automation technologies reshape their tools and processes. In the past, robotic systems, automated diagnostics, and digital process monitoring led to improved quality and efficiency. Today, manufacturers are strengthening the link between complex physical parts and digital capabilities using digital twins, AI-powered inspection tools, and adaptive robotics – creating new opportunities for process enhancement.

The Shift to Automation Everywhere

Historical growth in aerospace automation was fueled by the desire to enhance measurable factors such as quality and efficiency. Josh Tuttle, business development manager at Aerobotix, explains, "In the past, the primary motivation for adopting automation was its return on investment in addressing existing problems. Companies had established product lines or manufacturing processes and identified issues that required automation solutions. Now, everyone incorporates automation from the start to improve broader aspects of their production processes."

This change in scope has shifted how manufacturers approach new projects. They make automation a planning cornerstone rather than retrofitting it into existing processes. "Manufacturers start with automation as their baseline zero," Tuttle says. We get involved before customers design their buildings to ensure mobile robotics and robotic paint booths are located appropriately to support production operations."

Automated Inspections and Digital Twins

GE Vernova wind turbine blades; Image courtesy of GE VernovaJohn Karigiannis, who leads robotics initiatives at GE Vernova, points to parts inspections as a significant automation driver across sectors. One example is GE's development of crawler robots for wind turbine blade inspection. "These blades are the size of a football field and narrow as you move down them," Karigiannis explains. "Humans simply cannot fit, so we integrated a crawler with a 360-degree field of view camera and LiDAR, running over 40 neural networks in parallel to detect potential anomalies."

Integrating digital twin technology further enhances inspection capabilities by creating virtual replicas of manufacturing systems and products. Digital twins enable engineers to conduct thorough parts analyses, find potential issues, plan maintenance activities, and maintain a digital record of the asset's condition.

Helping Address Safety and Labor Concerns

Humans remain central to the drivers of automation, with manufacturers needing to protect workers from hazardous environments. "Paint shops, particularly in aerospace and defense, use aggressive chemicals such as chromated paints,” Tuttle explains. “Robotic coating and painting systems help us remove people from these harsh environments while also reducing safety risks associated with carrying heavy equipment while wearing full protective suits."

The industry also uses automation to address shortages in skilled labor, with physically demanding roles suffering issues in attraction and retention. As Tuttle explains, "You can't find somebody that wants to work in a paint shop. People don't want to do those tasks for eight hours a day."

Rather than eliminating jobs, automation transforms them into safer, more sustainable roles. For example, collaborative robotics systems work alongside human operators to combine the precision and endurance of automation with human judgment and adaptability. "We're taking people out of the paint booth and setting them in a chair to run robots. They're still doing skilled, hands-on work, but they're in charge of preparing the part or monitoring the process instead of manipulating paint guns directly."

Many workers welcome this transformation. "Most painters you talk to are happy that they're not breaking their back by painting stuff," says Tuttle.

Adoption Challenges In Aerospace And Energy

The industry continues to navigate several challenges in adopting automation. These include integrating new automation systems with existing manufacturing processes, meeting strict regulatory requirements, and developing workforce capabilities to operate and maintain them. 

Tuttle and Karigiannis highlight three additional obstacles.

Programming for High-Mix, Low-Volume Production Runs

Unlike automotive, where robots perform the same operation thousands of times on identical components, aerospace applications often involve smaller production runs with significant variation between parts. "One of automation’s biggest cost drivers is having a human program all those production runs," Tuttle explains. "You program it for one product line, then switch to another where the part is slightly different, and it doesn't work. Manual reprogramming creates significant inefficiencies and technical bottlenecks in the manufacturing process.”

Precision Manipulation Requirements

One of aerospace automation’s most significant technical hurdles involves handling deformable materials. "Manipulation remains far from being solved," explains Karigiannis. “The accuracy requirements for manufacturing processes are extremely demanding. When reprofiling blades for wind turbines, we're talking about tolerances within one-thousandth of an inch."

This challenge extends beyond simple positioning to understanding and adapting to a material’s properties. "How is a robot going to grasp a deformable object? It can look at the part, but unless it senses and understands the dynamics of the material, it cannot perform manipulation."


 

Karigiannis says this complexity requires multimodal perception systems that combine vision, touch, and proprioception. “To be dexterous in manipulation, you need accurate localization and placement, as well as an understanding of how the part evolves over the manufacturing process. This requires a sophisticated cognitive model in addition to physical grippers and brushes.”

Security and Connectivity

The industry's stringent security requirements can hinder the adoption of modern automation technologies. "We have many systems that have historically been air-gapped," says Tuttle. “All of these innovative methods for using machine learning or predictive maintenance, which require sending data to the cloud, are often nonstarters for many of our operations."

Strict security rules often need creative engineering solutions. For example, Aerobotix developed a unique approach for a mobile robot system deployed at a location with restricted Wi-Fi connectivity: "Given the Wi-Fi block, rather than having an off-site mobile base to send commands, we put the control system on the back of the robot. It meant commands had to be entered locally, but that’s the tradeoff when you have no connectivity."

Future Automation Trends

The future of automation points toward more intelligent and adaptive systems that support the complex demands of aerospace manufacturing while avoiding technical and operational risks.

Advanced Inspection Systems

Growth in automated inspections is driven by the need for more thorough, consistent, and efficient inspection processes in challenging environments. "We're collaborating with the Department of Energy to transform inspection processes for floating wind turbines, where traditional methods pose significant challenges,” explains Karigiannis. “The state of the art sends divers to inspect mooring lines, but we’re evolving that to use multiple sensing technologies to achieve higher accuracy and reliability. Vision is not enough, so these devices combine perception, touch, and proprioception of the internal state of the robot to have an accurate cognitive model for the system."

Predictive Maintenance 

More manufacturers are adopting automation technologies that combine real-time sensor data from the field with machine learning algorithms and automated inspections to detect potential failures before they occur. These predictive maintenance systems can significantly reduce downtime and improve planning.

The GE wind turbine crawler offers an example of this technology in action. “We use similar technology for uptower inspection when blades are deployed in the field,” explains Karigiannis. “Someone climbs the turbine’s nacelle with a crawler and opens a hatch to put it in to inspect the hub and collect data.”

These types of approaches to predictive maintenance offer several advantages over manual inspections:

  • More frequent detection of issues before they become critical.
  • Improved consistency and objectivity through data-driven assessments.
  • Greater coverage of hard-to-reach and hazardous areas.
  • Broader support of analysis and compliance activities through digital documentation of component health.

Adaptive Manufacturing

The industry is moving toward automation systems that can dynamically adjust to changing manufacturing requirements without extensive reprogramming. As Tuttle explains, "Programming and hand-teaching a part you're only producing five to ten of doesn't make sense. Finding a way to do quicker programming, be it through machine learning or adaptive pathing programs, is where we’ll see significant growth.”

These technologies incorporate several capabilities:

  • Dynamic path planning systems to automatically generate and optimize movement paths based on real-time sensor data. For example, when painting aircraft components, these systems can adjust their spray patterns and paths to accommodate different part geometries while maintaining consistent coating thickness.
  • Intelligent quality control uses advanced sensors and processing algorithms to assess quality and adjust their parameters to maintain specifications.
  • Multi-modal sensing systems integrate multiple sensor types to build a comprehensive understanding of their environment. When working with composite materials, for example, robots can combine visual inspection, force sensing, and position data to ensure precise material placement and joining.
  • Hierarchical learning systems employ layered learning approaches to break problems into smaller, solvable steps. "We're moving away from learning all at one level, with huge models that are hard to build and maintain," Karigiannis explains. “Creating a hierarchy of tasks provides modularity, making it easier to change components and scale faster to suit different product lines."

Embodied Intelligence

Embodied intelligence systems learn through direct interaction with the physical world rather than relying on pre-programmed rules and explicit models. Karigiannis explains, "We’re seeing more systems with embodied cognition, going from pixels to autonomous actions by learning their world through interaction."

For example, traditional programming requires precise mathematical models for every possible part variation when training a robot to handle the variable geometry of aerospace components. With embodied intelligence, the robot learns through practice and sensory feedback, much like a human apprentice. "These systems mimic how humans establish cognition," explains Karigiannis. “We are social, which means we learn by imitation, extracting features through teaching rather than trying to build models that are often not observable."

Conservative Innovation

Although technology advances rapidly, manufacturers still carefully balance innovation with strict requirements for safety, reliability, and security. As Karigiannis explains, "We take our time because we want to see all the bugs worked out before we put it on a system that has a very expensive product or part in front of it."

The journey toward automation in emerging industries like aerospace and energy, manufacturing is ongoing, but the destination is clear: a future where deeper integration of AI and machine learning enables more flexible and adaptable manufacturing processes.