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Purpose

Robotic autonomous welding possesses the advantages of high efficiency and low cost. It is used for rapid prototyping and remanufacturing of large-scale components in advanced equipment. Many welding tasks involve automatic multi-layer and multi-pass motions. However, owing to errors in the multi-bead model and metal shaping uncertainties, current automatic multi-layer and multi-pass planning methods may lead to error accumulation and a significant decrease in accuracy with an increase in the number of layers and passes. The purpose of this paper is to develop an automatic multi-layer and multi-pass welding planning method that reduces error accumulation and improves the planning accuracy of robotic autonomous welding.

Design/methodology/approach

In this paper, a novel multi-layer and multi-pass welding automatic planning method based on detection feedback and flatness optimization is proposed. The results of each deposition process are assessed to show the elimination of error accumulation. Furthermore, incorporating undulation and incline, the comprehensive flatness is introduced and optimized to enhance the overall planning accuracy. Additionally, the traditional tangent overlapping model is extended to actual multi-layer and multi-pass scenarios to ensure the adaptability of the planning method.

Findings

Validation experiments for multi-layer and multi-pass welding are conducted, and the shaping results and errors compared to the planning are measured for each pass. The experimental results demonstrate that, compared to uncompensated methods, the proposed method exhibits no error accumulation and demonstrates more accurate planning performance.

Originality/value

The proposed method exhibits no error accumulation and is more accurate than existing methods. As the number of layers and passes increases, the advantage of its no error accumulation property becomes more obvious.

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