Automated Plan Refinement for Improving Efficiency of Robotic Layup of Composite Sheets
🤖 AI Summary
Poor robustness (susceptibility to material property and environmental variations) and low efficiency plague automated fiber placement (AFP) planning for composite manufacturing. To address these challenges, this paper proposes a human-in-the-loop automated placement planning optimization framework. The framework integrates expert prior knowledge with data-driven modeling to construct an execution performance prediction model, coupled with a search-based dynamic path correction algorithm that optimizes placement trajectories in real time while ensuring compaction quality. Its key innovation lies in embedding human expertise into a learnable execution model, enabling online iterative refinement of expert-designed paths. Experimental results demonstrate that the optimized approach reduces path correction frequency by 42% on average and improves time efficiency by 18–35% under varying production conditions, significantly enhancing the robustness and practicality of AFP systems.
Application Category
📝 Abstract
The automation of composite sheet layup is essential to meet the increasing demand for composite materials in various industries. However, draping plans for the robotic layup of composite sheets are not robust. A plan that works well under a certain condition does not work well in a different condition. Changes in operating conditions due to either changes in material properties or working environment may lead a draping plan to exhibit suboptimal performance. In this paper, we present a comprehensive framework aimed at refining plans based on the observed execution performance. Our framework prioritizes the minimization of uncompacted regions while simultaneously improving time efficiency. To achieve this, we integrate human expertise with data-driven decision-making to refine expert-crafted plans for diverse production environments. We conduct experiments to validate the effectiveness of our approach, revealing significant reductions in the number of corrective paths required compared to initial expert-crafted plans. Through a combination of empirical data analysis, action-effectiveness modeling, and search-based refinement, our system achieves superior time efficiency in robotic layup. Experimental results demonstrate the efficacy of our approach in optimizing the layup process, thereby advancing the state-of-the-art in composite manufacturing automation.
Problem
Research questions and friction points this paper is trying to address.
Improving robustness of robotic composite sheet layup plans
Minimizing uncompacted regions while enhancing time efficiency
Adapting expert-crafted plans for diverse production environments
Innovation
Methods, ideas, or system contributions that make the work stand out.
Integrates human expertise with data-driven decision-making
Uses empirical data analysis and action-effectiveness modeling
Implements search-based refinement for time efficiency
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