This article reports on development of an adaptive framework for predicting the erosion performance of polymer composites using certain statistical and machine learning (ML) models. For this, ramie‐epoxy composites reinforced with variations (0–30 wt%) of sponge iron slag (an iron industry waste) are considered. The composites are fabricated and then subjected to high temperature solid particle erosion wear trials following Taguchi's L27 orthogonal array. The effects of different control factors on the erosion rate in an interactive environment are appraised by analysis of variance (ANOVA) which reveals the filler content as the most significant factor contributing 66.21%, followed by impact velocity (22.86%) and impingement angle (2.28%). A regression model based on the input–output parameters obtained from experimentation is constructed for prediction of erosion rate. Further, four predictive models using different machine learning algorithms are also proposed to predict the erosion rate of the composites. The feasibility and performance of each ML model is assessed using appropriate performance metrics. Among all the models, the gradient boosting machine model is found to be the most reliable model exhibiting the highest prediction accuracy and least errors.Highlights Development of novel class of composites reinforced with sponge iron slag. Database creation based on erosion wear experimentation on the composites. Data‐driven modeling for prediction of erosion rates using machine learning. Comparison of performance of different models and identifying the best one.

中文翻译：

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开发用于预测混合复合材料冲蚀磨损的机器学习模型

本文报告了使用某些统计和机器学习 (ML) 模型预测聚合物复合材料的侵蚀性能的自适应框架的开发。为此，考虑使用各种海绵铁渣（一种钢铁工业废料）（0-30 wt%）增强的苎麻环氧树脂复合材料。制造复合材料，然后按照 Taguchi's L 进行高温固体颗粒侵蚀磨损试验27正交阵。通过方差分析 (ANOVA) 评估了交互环境中不同控制因素对侵蚀速率的影响，结果表明填料含量是最重要的因素，贡献了 66.21%，其次是冲击速度 (22.86%) 和冲击角度 (2.28 %）。基于实验获得的输入输出参数构建回归模型来预测侵蚀率。此外，还提出了四种使用不同机器学习算法的预测模型来预测复合材料的侵蚀率。使用适当的性能指标评估每个机器学习模型的可行性和性能。在所有模型中，梯度提升机模型被认为是最可靠的模型，具有最高的预测精度和最少的误差。亮点 开发新型海绵铁渣增强复合材料。 基于复合材料侵蚀磨损实验的数据库创建。 使用机器学习预测侵蚀率的数据驱动建模。 比较不同模型的性能并确定最佳模型。