Simple Explanation (ELI5)

Think of two dancers passing a long metal bar: they must grab and release it only at exact moments, or the bar will bend. New robots now know precisely when to move, thanks to simulations and timing calculations.

Key Advantages & Current Limits

🟢 Better time utilization through calculated movement windows, cutting idle time. 🟢 Millimetric precision and synchronized control for more reliable forging. 🔴 Needs specialized simulation software (DEFORM‑3D), which can be expensive. 🔴 Integration into existing plants can be complex, often requiring hardware upgrades.

Future Outlook & Takeaways

In the next 3‑5 years, these synchronization algorithms could become standard, enabling faster automated presses, on‑demand production, and lower costs as simulation tools and software become more accessible.

How Do Two Forging Robot Arms Move in Sync?

Article

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In industrial forging, two‑arm robots must sync perfectly to shape long, complex pieces. Researchers examined six long‑shaft steel types used in aerospace and automotive sectors. Using DEFORM‑3D, they simulated metal stretching, recording speed and displacement at the piece ends. These data defined the "movement windows" for the grippers, i.e., moments when the arms can grab or release without disturbing deformation.

They then devised a segmented method to calculate available time based on the hydraulic press pressure curve, improving time utilization and system responsiveness. Experimental tests confirmed that both arms move with millimetric precision, even in critical phases.

The forging cycle was split into four stages: preliminary adjustment, inter‑pass compliance, execution phase, and forging completion. This framework simplifies coordinated control programming, paving the way for faster, more reliable plants.

Bonus

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The DEFORM‑3D analysis extracted local velocity curves (v(x)) and cumulative displacement (s(x)) along the billet axis, revealing non‑linear deformation gradients typical of highly ductile materials. Temporal segmentation relies on discretizing the hydraulic press pressure‑time (P‑t) curve, dividing the cycle into intervals Δt_i where pressure exceeds critical thresholds (P_thr). For each interval, the time margin t_margin = Δt_i – t_motion is computed, where t_motion is the time required by the manipulator to complete the grasp‑position trajectory. A linear optimization algorithm minimizes Σt_margin, ensuring the total margin remains positive, i.e., the system avoids overload.

Dynamically, the manipulators use high‑torque DC servomotors controlled by an adaptive PID loop that adjusts response based on high‑resolution optical encoder feedback (≤0.01°). The control sampling frequency is set at 2 kHz, allowing compensation of vibrations induced by the press's impulsive pressure. Experimental results show an average positioning error of 0.12 mm and a velocity deviation below 2 % from the theoretical profile, values within precision forging limits. This integrated approach of simulation, temporal segmentation, and advanced control marks a significant step toward full digitalization of forging lines.