We’re excited to share that our very own L-CAS member, Dr Alexandr Klimchik, has co-authored a cutting-edge paper recently published in Nature Scientific Reports. The study, titled “Systematic analysis of geometric inaccuracy and its contributing factors in roboforming,” delves into the world of robot-assisted incremental sheet metal forming, shedding light on the challenges and potential solutions in this rapidly evolving field.

Roboforming: Flexibility Meets Precision Challenges

Roboforming, a variant of incremental sheet metal forming, has emerged as a highly versatile method for creating complex sheet metal components without the need for traditional dies. By employing robotic manipulators, this technique offers unprecedented flexibility in tool motion, enabling the production of intricate shapes that were once difficult or impossible to achieve.

However, with great flexibility comes a significant challenge: maintaining geometric accuracy. The paper highlights a critical issue in roboforming – the impact of machine compliance on the final shape of formed components. Unlike more rigid CNC machines, the serial arrangement of links and joints in robotic manipulators can lead to reduced positional accuracy when subjected to forming loads.

A Systematic Approach to Error Analysis

The research team has developed a comprehensive methodology to analyze the factors contributing to geometric inaccuracies in roboforming. Their approach combines experimental and numerical methods to quantify three main sources of error:

1. Material springback
2. Tool and tool holder deflections
3. Machine compliance

By isolating and measuring these factors, the team has provided valuable insights into the proportional contribution of each to the overall geometric deviation in roboformed components.

Key Findings and Implications

• Material Springback: Experiments using CNC machines on cone and variable wall angle cone profiles helped establish a baseline for material behavior.
• Tool Deflections: Finite element simulations were employed to estimate the deflections in tools and tool holders under forming loads.
• Robot Cartesian Stiffness: An analytical method using the Virtual Joint Model was developed to predict path deviations due to joint stiffness in robotic manipulators.

By quantifying these factors, the research paves the way for more accurate predictions of geometric errors in roboforming. This knowledge is crucial for developing compensation strategies and improving the overall accuracy of the process.

Looking Ahead

The work of Klimchik and his colleagues represents a significant step forward in understanding and potentially overcoming the limitations of roboforming. As we continue to push the boundaries of what’s possible in advanced manufacturing, studies like this are essential in bridging the gap between the flexibility of robotic systems and the precision demands of modern industry.

We’re proud of Alexandr’s contribution to this important research and look forward to seeing how these insights will shape the future of roboforming and robotic manufacturing at large.