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In this article, the comparative analysis between regression methods and the hybrid PSO-ANN algorithm for predicting energy consumption in additive manufacturing processes is explored. The study found that the PSO-ANN algorithm performs better than traditional regression methods. The results demonstrate a higher correlation coefficient of 0.99 for the PSO-ANN algorithm compared to 0.95 for the regression method. The hybrid approach is believed to provide more effective and accurate predictions for energy consumption in the additive manufacturing process. The findings demonstrate the potential of incorporating sophisticated algorithms, including PSO-ANN, to enhance predictive modeling and improve energy efficiency in manufacturing processes. The use of artificial intelligence has significantly improved the prediction of energy consumption in the additive manufacturing process by using advanced algorithms to analyze process parameters. These developments contribute to more sustainable and efficient production in additive manufacturing.

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New Energy Consumption Prediction Aid Model Applied to Additive Manufacturing Process

  • 会议论文

  • 发表日期：

    2025年

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  • Kamel Bousnina,
  • Anis Hamza,
  • Noureddine Ben Yahia

摘要

In this article, the comparative analysis between regression methods and the hybrid PSO-ANN algorithm for predicting energy consumption in additive manufacturing processes is explored. The study found that the PSO-ANN algorithm performs better than traditional regression methods. The results demonstrate a higher correlation coefficient of 0.99 for the PSO-ANN algorithm compared to 0.95 for the regression method. The hybrid approach is believed to provide more effective and accurate predictions for energy consumption in the additive manufacturing process. The findings demonstrate the potential of incorporating sophisticated algorithms, including PSO-ANN, to enhance predictive modeling and improve energy efficiency in manufacturing processes. The use of artificial intelligence has significantly improved the prediction of energy consumption in the additive manufacturing process by using advanced algorithms to analyze process parameters. These developments contribute to more sustainable and efficient production in additive manufacturing.