Moog Cuts Analysis Time 75% with the Anaconda Platform

Moog stands as one of the world’s leading designers and manufacturers of precision motion control systems, serving military and commercial aircraft, satellites, space vehicles, and defense systems across the globe. With over 11,000 employees and operations in 26 countries, this aerospace giant has built its reputation on solving the industry’s most challenging control problems, from guiding spacecraft to the International Space Station to enabling the precision flight control systems that keep helicopters airborne.

Will Tan, Chief Engineer for Mechanical Analysis at Moog’s Military Aircraft group, recognized a opportunity within his organization. The company’s engineering teams were spending days manually processing complex vibration analysis data, work that was not only time-intensive but prone to human error in safety-critical applications.

“We get a lot of pressure to do things faster, to iterate faster,” explains Tan. “The ability to leverage the newest tools available and customize them with in-house automation efforts—that’s what differentiates us from the competition.”

The Challenge: Manual Processes in Mission-Critical Analysis

Moog’s structural analysis team faced a complex challenge that’s common across the aerospace industry. When designing components for rotorcraft like helicopters and tilt-rotor aircraft, engineers must perform sophisticated vibration analysis to ensure designs can withstand the rigorous operational environments these vehicles encounter.

The process, known as modal random and harmonic analysis, simulates the mixed vibration testing environment that helicopter components experience. This analysis is critical for evaluating design robustness against fatigue, but the traditional workflow was labor-intensive and error-prone.

“It used to take probably a few days, two or three days to process all that data manually,” Tan recalls. “You export arrays of data from the finite element analysis, then use MATLAB or Excel for post-processing, and generate damage calculations in a different tool.”

The manual approach presented several challenges:

• Time-intensive processing: Each analysis required two to three days of manual data manipulation across multiple software platforms
• Subjectivity and inconsistency: Engineers had to make judgment calls about which stress peaks to include in damage calculations
• Error-prone workflows: Manual copying and pasting between tools introduced opportunities for mistakes
• Scalability limitations: With models containing millions of nodes and elements, manual processing couldn’t keep pace with engineering demands

For a company working on programs with 50-60 engineers across multiple sites, these bottlenecks had cascading effects on entire development timelines.

Finding the Right Foundation with Anaconda

Moog’s path to automation began with Will Tan’s previous experience using Anaconda for data science projects over a decade ago. When he joined Moog, no one in his structural analysis function was using the platform. Tan introduced Anaconda to his team with some encouragement, drawn by its simplicity and integrated approach.

“Anaconda is just very easy; it’s all packaged in one place, and the Anaconda Navigator is nice. I can ensure all the modules are updated and everything’s working properly,” Tan explains. “That’s the main reason—ease of use.”

The decision proved prescient as commercial software vendors began releasing APIs that allowed Python to interface directly with engineering tools like ANSYS. This created new possibilities for automated workflows that could eliminate the manual steps that plagued traditional analysis processes.

Young engineer Jacob Antony, fresh from his undergraduate studies, became instrumental in developing Moog’s automated solution. Working with Spyder IDE, available through Anaconda Navigator, and leveraging the platform’s extensive library ecosystem, the team built a sophisticated script that automates nearly the entire manual workflow.

Transforming Critical Safety Analysis

The automated solution Moog developed using Anaconda fundamentally changed how the team approaches vibration analysis. The new workflow leverages Python libraries to:

• Directly extract data from ANSYS: Using ANSYS’s Python API, the system pulls simulation results without manual export steps
• Automate complex calculations: Python handles the sophisticated mathematical processing that identifies stress peaks and calculates damage metrics
• Generate standardized outputs: The system produces consistent, repeatable results that eliminate subjective interpretation
• Visualize results effectively: Using matplotlib and other visualization libraries, the system creates clear, actionable graphics for decision-makers

“We’re able to take the data from ANSYS directly into Python using some libraries from ANSYS, which allows me to quickly retrieve all the results directly from the ANSYS files and then do post-processing in Python directly,” explains Antony. “It gives me all the flexibility and power in Python to use numpy, matplotlib, all those libraries that make it a lot quicker and a lot more robust.”

Dramatic Productivity and Quality Improvements

The results of Moog’s Anaconda-powered automation exceeded expectations across multiple dimensions:

Speed Improvements: Analysis time dropped from 2-3 days to just 8-10 hours of compute time, a reduction of over 75%. Engineers can now submit analysis runs before leaving work and have results waiting the next morning.

Error Reduction: By encoding engineering best practices and standards directly into the automated workflow, the system eliminates human error and ensures consistent application of established procedures across all analyses.

Enhanced Decision-Making: Instead of waiting a week for analysis results, Moog can now turn around design assessments within a single workday, allowing engineering teams to proceed without delays.

Scalable Quality: The automated system handles models with millions of nodes and elements while maintaining the same high-quality standards across all analyses.

“When we have updates to requirements or updates to the design, we can turn around an assessment of ‘Okay, is this acceptable? Can we proceed with releasing the blueprints and manufacturing data?’ within a workday as opposed to waiting a week,” Tan notes.

The impact extends far beyond individual productivity gains. For programs involving 50-60 engineers across multiple sites, eliminating week-long analysis bottlenecks has a compounding effect on overall program efficiency and timeline adherence.

Modernizing Engineering with Model-Based Approaches

Moog’s success with Anaconda represents part of a broader digital transformation strategy. The company is investing heavily in integrated digital systems that accelerate product development cycles through automated processes.

“With a model-based engineering approach, with the use of digital workflows, you can take all that knowledge and program it into the workflow so you can be confident the work conforms to our best practices,” Tan explains.

The Anaconda platform serves as the foundation for additional automation projects currently in development, including design exploration workflows that will enable engineers to evaluate entire product catalogs against customer requirements automatically.

Looking Forward: The Future of Aerospace Engineering

Moog’s journey with Anaconda is just beginning. The team is expanding their automated workflows to cover additional analysis types and product families, with the ultimate goal of building comprehensive evaluation systems for their entire product catalog.

“When customers tell us what they want, we can quickly access all of our legacy data and propose something that is close to meeting the requirement,” Tan explains about their vision for the future.

For Moog, Anaconda has delivered exactly what the company needed: the confidence to innovate with open source at enterprise scale, with the reliability and performance their mission-critical applications demand. As the aerospace industry continues to evolve toward faster development cycles and more complex systems, Moog’s investment in digital transformation positions them to lead the way.

Ready to accelerate your engineering workflows with trusted open source? Discover how the Anaconda Platform can transform your technical operations while maintaining the security and reliability that aerospace and defense applications require. Contact our team for a personalized demonstration.