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Abstract

Aimed to achieve quantitative control of workpiece surface after robotic polishing and improve polishing efficiency, a two-step processing optimization method involves artificial intelligence algorithms is investigated. Firstly, based on XGBoost algorithm, a prediction model for polished workpiece surface depending on key parameters is proposed, and the accuracy of the model is verified by experiments. After that, by using the above model, the influence of each parameter on the roughness was evaluated quantitatively. Subsequently, target roughness-driven optimization of processing parameters was presented by combining the roughness prediction model with NSGA II-TOPSIS algorithm based on the influence of each parameter on the roughness. To verify the proposed processing optimization method, polishing experiments of mold steel samples were conducted. The experimental results show that the maximum absolute error between the predicted and experimental roughness is 0.035 μm, and the maximum relative error is <9%. At the same time, when the minimum is set as the optimization objective. With the same length of polishing path, the feed rate is increased from 0.25 mm/s to 0.37 mm/s, and the efficiency is improved to 48%. The NSGA II-TOPSIS algorithm can achieve quantitative control of mold steel surface roughness after robotic polishing to improve polishing efficiency, and provide a basis for reasonable selection of processing parameters, which have certain practical value.

Keywords

Robotic polishing Mold steel Roughness prediction XGBoost algorithm NSGA II-TOPSIS algorithm

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Optimization of robotic polishing process parameters for mold steel based on artificial intelligence method

Abstract

Keywords

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References

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