PRODUCTION TECHNOLOGY Archieven

Long story short

At GILBOS, optimizing industrial automation is key to maintaining efficiency and reliability. Our recent work on the Automated Creeling Solution (ACS) focused on improving robustness, performance, and software architecture. The ACS automates yarn handling in textile production and consists of multiple submachines, including the feed robot, package preparation unit, creel unit, and transport system. While the system functioned well under normal conditions, enhancements were needed to better handle edge cases. Additionally, a complete software redesign of the package preparation submachine was necessary to ensure long-term scalability and flexibility.

The Challenge: Improving Performance & Redesigning Software for Package Preparation

We tackled two major challenges. The first was strengthening the sequence performance and robustness of the creel unit and transport system to ensure seamless coordination and recovery from unexpected machine states. This effort was highly collaborative, as we worked closely with the team at GILBOS to refine and optimize system behavior. The second was executing a full-scale software redesign of the package preparation submachine to improve precision, adaptability, and maintainability.

The package preparation submachine plays a crucial role in securing and positioning yarn before it moves to the creel unit. However, its existing software lacked the necessary structure and flexibility for future improvements. To make the system more resilient and adaptive, we redefined its architecture from the ground up.

Our Approach: State-Driven & Modular Software Architecture

To enhance system robustness, we implemented a state-driven architecture for the creel unit and transport connection. This ensured efficient error recovery and improved the overall reliability of sequence execution, reducing downtime and improving handover performance between submachines. Throughout this process, we collaborated with GILBOS engineers to align our solutions with their operational needs and insights.

For the package preparation submachine, we introduced a modular software architecture with well-defined components for yarn tensioning, positioning, and transport coordination. This design allowed for dynamic parameter adjustments without major code changes, giving operators more control over system behavior.

By implementing an event-driven system, we optimized process efficiency and responsiveness, ensuring that every stage of yarn preparation—preparing, securing, and positioning—adapts dynamically to varying input conditions.

Results: A More Adaptive, Reliable & Scalable ACS

Our improvements have significantly enhanced the ACS, making it more robust, efficient, and flexible. The redesigned package preparation submachine now delivers precise yarn handling with minimal intervention, seamlessly integrating with the creel unit and transport system.

These upgrades have led to several key benefits. Sequence reliability has been significantly improved through enhanced state management, ensuring a smoother and more predictable operation. The software now features dynamic configuration capabilities, allowing for greater adaptability to different operational needs. Yarn positioning and tensioning have been optimized to achieve higher precision, ensuring consistent results across varying conditions. Additionally, downtime has been reduced due to the implementation of a robust event-driven architecture, improving overall system efficiency.

These enhancements ensure that GILBOS remains at the forefront of textile automation, providing a scalable and maintainable solution that can evolve with industry demands.

Why it matters to you?

Automation is not just about increasing efficiency—it’s about staying competitive in an ever-evolving industry. With improved reliability, precision, and adaptability, a well designed software framework ensures that your production lines run seamlessly, reducing downtime and improving overall output. These innovations allow you to focus on growing your business rather than troubleshooting inefficiencies.