metalworkingmag.com
22
'26
Written on Modified on
DMG MORI integrates AI across CNC turn-mill process chain
Digital workflows, AI-assisted machining, and energy analytics improve efficiency, quality, and transparency in complex CNC manufacturing at Hannover Messe 2026.
en.dmgmori.com
Machining, aerospace manufacturing, and precision engineering are increasingly adopting AI-driven workflows to manage growing process complexity and efficiency demands. In this context, DMG MORI AG is presenting an integrated AI-supported CNC process chain at Hannover Messe 2026 (Hannover, Germany, 20–24 April 2026), demonstrating how artificial intelligence can enhance turn-mill operations from digital work preparation to energy analysis.
The showcase centers on the complete machining of a titanium rear keel bearing, a high-performance structural component used in demanding applications such as aerospace, medical technology, and energy systems. The example highlights the challenges of machining titanium, including high strength, thermal load, and complex geometries requiring multiple operations in a single setup.
AI-supported digital work preparation
The process chain begins with AI-assisted CAM systems that analyse component geometry and generate optimized machining strategies. Virtual simulations validate toolpaths using machine-specific models, reducing iteration cycles between programming, simulation, and execution.
This enables:
- Faster process planning: Automated suggestions for machining strategies
- Collision-free toolpaths: Verified through integrated simulation
- Reduced setup iterations: More robust processes from the outset
Intelligent tool management and monitoring
Efficient turn-mill operations rely on precise tool selection and condition monitoring. AI-supported tool search functions within the CAM environment help identify suitable tools and holders, while machine-level systems provide real-time visibility into tool status.
Key capabilities include:
Key capabilities include:
- Contactless tool measurement: Automatic offset generation within the workspace
- Wear and damage detection: Early identification of tool degradation
- 3D tool modeling: Improved transparency for complex setups
- Lifecycle monitoring: Tracking tool usage and performance data
These features reduce setup time and improve process stability.
AI-assisted machining and process control
During machining, process signals such as spindle load, vibration, and feed rate are continuously monitored and analysed. AI-supported systems detect deviations and respond proactively.
Examples of integrated functions include:
AI-assisted machining and process control
During machining, process signals such as spindle load, vibration, and feed rate are continuously monitored and analysed. AI-supported systems detect deviations and respond proactively.
Examples of integrated functions include:
- Machine Protection Control (MPC): Identifies abnormal conditions early to prevent damage
- AI Chip Removal: Detects and removes chip accumulations automatically to avoid interruptions
- Real-time data evaluation: Supports stable and repeatable machining processes
This approach enhances reliability, particularly in complex multi-axis operations involving high-value materials.
Integrated quality assurance during machining
In-process measurement technology is embedded directly into the machining workflow. Measurements of geometries and functional surfaces are performed during production, enabling immediate corrections.
This is particularly important for materials such as titanium, where thermal effects and mechanical stresses can lead to dimensional deviations. By integrating quality control into the process, manufacturers can:
Integrated quality assurance during machining
In-process measurement technology is embedded directly into the machining workflow. Measurements of geometries and functional surfaces are performed during production, enabling immediate corrections.
This is particularly important for materials such as titanium, where thermal effects and mechanical stresses can lead to dimensional deviations. By integrating quality control into the process, manufacturers can:
- Detect deviations early
- Adjust machining parameters in real time
- Reduce post-processing and inspection steps
Energy monitoring and resource optimization
Beyond productivity and quality, the system also focuses on energy efficiency. CELOS X applications monitor energy consumption, costs, and CO₂ emissions across machining operations.
Capabilities include:
Beyond productivity and quality, the system also focuses on energy efficiency. CELOS X applications monitor energy consumption, costs, and CO₂ emissions across machining operations.
Capabilities include:
- Real-time energy tracking: Visibility into consumption per machining step
- Energy optimization: Automatic standby and warm-up functions
- Leak detection: Identification of inefficiencies such as compressed air losses
These tools support more sustainable manufacturing by reducing non-productive energy usage.
Toward a fully integrated digital process chain
The turn-mill showcase illustrates how data from planning, machining, quality control, and energy monitoring can be combined to create a comprehensive digital representation of manufacturing processes. This aligns with DMG MORI’s Machining Transformation (MX) strategy, which emphasizes integrated data usage and automation across the production lifecycle.
By embedding AI across the CNC process chain, the solution addresses key challenges in modern manufacturing, including increasing component complexity, tighter delivery timelines, and the need for greater transparency and efficiency.
Edited by Natania Lyngdoh, Induportals Editor — Adapted by AI.
www.dmgmori.com
Toward a fully integrated digital process chain
The turn-mill showcase illustrates how data from planning, machining, quality control, and energy monitoring can be combined to create a comprehensive digital representation of manufacturing processes. This aligns with DMG MORI’s Machining Transformation (MX) strategy, which emphasizes integrated data usage and automation across the production lifecycle.
By embedding AI across the CNC process chain, the solution addresses key challenges in modern manufacturing, including increasing component complexity, tighter delivery timelines, and the need for greater transparency and efficiency.
Edited by Natania Lyngdoh, Induportals Editor — Adapted by AI.
www.dmgmori.com
