XHE v3.0 Electronic Handle Applicator – Advanced Software Development with AI Support and Precision Motion Control by Decimo Srl
High performance, servo-assisted architecture and advanced trajectory control. The new XHE integrates artificial intelligence to optimize adhesive handle application processes.
Introduction
The XHE electronic handle applicator, developed by Decimo Srl, is a versatile solution for automatic adhesive handle application on multipacks.
The system combines automation, motion control, and a patented applicator arm. This architecture ensures smooth dynamics, repeatable accuracy even at high speed, simpler setup, and improved operational safety.
AI doesn't write our code, it transforms and optimizes it for control.
System Architecture and Kinematic Model
The XHE architecture combines relative kinematics, motion control, and dedicated software. It was designed to maintain precision in multipack application, simplify commissioning, and handle format and speed variations with stability.
Algorithm Evolution: from Simulation to Structured Text
The algorithm was first validated through simulations in a scientific computing environment and with support from ChatGPT Team, then rewritten in Structured Text (ST) for the Sysmac Studio automation environment.
The translation and optimization, supported by AI models, allow autonomous trajectory generation based on one operational parameter: bundle width.
Key Benefits Introduced:
- Elimination of format-specific preloaded cams
- Increased adaptability to production variability
- Code optimization and simplified logic
- Reduced PLC scan time
Axis Control, Synchronization and Feedback
All movements are synchronized in real time via EtherCAT bus. Closed-loop vector inverters with encoders provide continuous feedback to the controller, ensuring repeatable stops and dynamic correction of gripper position.
This keeps the handle centered even during speed changes, with improved accuracy and smoother motion thanks to more progressive acceleration ramps.
Line Integration and Adaptive Flow Control
Since version v2.0, a dynamic flow management system has been introduced to automatically regulate production speed across three levels (low, medium, high), based on line operating conditions.
The system can receive upstream and downstream signals and adapt speed in real time. This improves production continuity, reduces stops, and handles load variation more effectively.
HMI – Human-Machine Interface
The XHE HMI (human-machine interface) is designed for practical, intuitive use. Software and manuals are developed by a specialized team and available in multiple formats.
- Dual-lane layout with 2 HMI displays (one per side), without bulky external arms.
- Operational continuity: if one display fails, the other remains available.
- Page layout inspired by smartphone interfaces, with 7" versions and 10"+ option.
- Updatable manual, also accessible online, with operating sections for format change and axis referencing.
Main Options
The XHE enables rapid format change through three main adjustments:
- Infeed side guides
- Application head width
- Application head height
Adjustments are available in manual (cranks and HMI), semi-automatic (push buttons), or automatic (encoder-equipped motors) modes. This keeps format change simple, fast, and manageable without interrupting production flow.
Version Evolution
XHE v1.0
- Cams calculated in simulation software and loaded manually
- Single HMI and rotating button panel
- Input-only flow control
- No internal lighting
- Mechanical safety switches
XHE v2.0
- Closed-loop vector inverters with encoders
- Dual-side HMIs
- LED internal lighting
- Bidirectional speed control
- Enhanced flexibility
XHE v3.0
- Automatic cam generation
- Simplified configuration
- Non-contact LED sensors
- Optimized software
Conclusions
The XHE v3.0 represents a technologically mature solution for automated adhesive handle application. It combines physical modeling, advanced programming, and industrial automation, with AI integration in motion-control processes.
Reference Text: Bruno Siciliano, Lorenzo Sciavicco, Luigi Villani, Giuseppe Oriolo, Robotics – Modelling, Planning and Control, Third Edition, McGraw-Hill.
Additional Note: The translation and optimization of the algorithm into Structured Text (ST) were carried out using ChatGPT Team by OpenAI and supported by the Sysmac Studio automation environment by Omron.
Author: Eng. Vincenzo Ripa – Decimo Srl
