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DMG MORI Redesigns Robotic End-Effector using Topology Optimization & Reusable Workflows
Technology Category
- Analytics & Modeling - Generative AI
- Analytics & Modeling - Predictive Analytics
- Functional Applications - Manufacturing Execution Systems (MES)
Applicable Industries
- Automotive
- Aerospace
- Consumer Goods
Applicable Functions
- Product Research & Development
Use Cases
- Predictive Maintenance
- Factory Operations Visibility & Intelligence
- Manufacturing System Automation
Services
- Software Design & Engineering Services
- System Integration
The Challenge
ADDITIVE INTELLIGENCE, DMG MORI’s additive manufacturing design consultancy, was tasked with maximizing the stiffness-to-weight ratio of the head of the robotic end-effector while improving handling precision and reducing manufacturing costs. A key design requirement was to keep the external form factor of the component unaltered. At the same time, the robot head had to house the embedded channels of the pneumatic system and the end effector’s electrical components.
About The Customer
DMG MORI is a leader in metal-cutting manufacturing equipment, producing high-quality CNC machines for over a century. The Robo2Go system is integral to the company’s factory automation offering. ADDITIVE INTELLIGENCE, DMG MORI’s additive manufacturing design consultancy, was tasked with maximizing the stiffness-to-weight ratio of the head of the robotic end-effector while improving handling precision and reducing manufacturing costs. A key design requirement was to keep the external form factor of the component unaltered. At the same time, the robot head had to house the embedded channels of the pneumatic system and the end effector’s electrical components.
The Solution
Since the engineers could not alter the external form of the part, the results of topology optimization could not be used directly as the final design. However, they could be used to vary the thickness of the outer shell. This process helped the team to grasp some of the structural benefits of topology optimization without changing the part’s exterior. The engineers of DMG MORI filled the shell with a conformal lattice to increase the stiffness of the part and create a permanent support structure for additive manufacturing. The team used nTopology’s engineering simulation capabilities to rapidly iterate between the available options and select a suitable lattice design. Instead of creating a one-off design, the engineers of DMG MORI developed a robust and reusable optimization process. Using as input the color-coded surfaces of each subsystem of the imported CAD file, the nTop workflow that the team developed can automatically rerun — even if the geometry changes due to design iterations or future projects.
Operational Impact
Quantitative Benefit
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