Siemens streamlines aircraft engine part manufacturing through digital twin and AI automation

Use case typeAI platformUpdated Jun 13, 2026

Siemens, Microsoft, and Rolls-Royce collaborated to optimize the complex design and manufacturing process of an aircraft engine hydraulic pump. The initiative leverages Siemens Xcelerator portfolio delivered on Microsoft Azure, integrating AI and digital twin concepts to create a unified digital environment for design, engineering, manufacturing, and quality inspection. Digital thread technology connects all stages of production, sharing managed data across software and processes, eliminating redundancies and manual errors. Generative design, additive manufacturing, and AI-driven automation transform the traditional manufacturing process, iterating countless possibilities to arrive at the optimal design and production path. With AI-powered Siemens NX X Manufacturing software, CNC programming and toolpath generation are automated, reducing effort and enabling compliance with best practices. The resulting hydraulic pump component is lighter, stiffer, and designed for greater reliability and sustainability, supporting Rolls-Royce's focus on next-generation, high-performance aerospace engines. The project showcased up to 80% programming time reduction and major productivity improvements at Hannover Messe 2025. The approach delivers digital transformation and sustainability gains for complex, safety-critical aerospace manufacturing.

Organization
Siemens
Location
Global
Published
July 2025

Reported outcomes

−80%

timeTime & speed

−25%quantified impact−200%quantified impact

Strategic outcomes

New product / capabilityCreated a digital twin manufacturing workflowNew product / capabilityAutomated CNC programming and toolpath generationSpeed & agilityEnabled faster, more reliable production cyclesSustainability & ESGImproved part sustainability through weight reduction

Catalog median for time & speed deployments: −60% across 727 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 3 of 4

  • 1AI-Driven Optimization of Aircraft Engine Part Manufacturing
  • 2Digital Twin and Digital Thread Implementation for Aerospace
  • 3Automated CNC Programming with Generative Design
  • Complex, multi-stage process for designing and manufacturing aircraft hydraulic pumps, prone to inefficiencies and redundancies.
  • Need to balance performance, reliability, durability, and weight to support engine safety and sustainability.
  • Traditional CAD/CAM workflows caused manual effort, lengthy programming cycles, and risk of errors.
  • Lack of unified data flow between design, engineering, manufacturing, and inspection phases.
  • Integrated Siemens Xcelerator portfolio with Azure AI platform to create a digital thread and digital twin across design, engineering, and manufacturing.
  • Applied AI and generative design to optimize part performance and automate CNC programming using Siemens NX X Manufacturing software.
  • Enabled additive manufacturing, automated machining, and advanced quality inspection in a closed-loop digital process.
  • Leveraged Microsoft Azure for secure, scalable cloud infrastructure enabling real-time data sharing and process automation among partners.
  • 25% lighter component, 200% stiffer, safety factor of 9, and improved sustainability (reduced weight and fuel consumption).
  • Up to 80% reduction in CNC programming and toolpath generation time.
  • Streamlined collaboration among design, engineering, manufacturing, and quality teams.
  • Faster, more reliable production cycles and demonstrator ready for manufacture.
Architecture

The solution integrates Siemens Xcelerator software suite deployed on Microsoft Azure, connecting data and workflows from design through manufacturing and inspection. Digital thread and digital twin models orchestrate data, enabling generative design, AI-driven CNC programming (Siemens NX X) and additive manufacturing. Automated quality checks and cloud-based data management ensure continuous optimization across all production stages and partners.

Sources & evidence1
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