Grinding and crushing of stones and other particles are associated with various significant applications. Different sectors have continuously evolved in this area. In the crushing industry, plants function under strict conditions, many of which involve grinding materials. Therefore, various factors are responsible for how the crushers perform. This research investigated the ability of the adaptive neuro fuzzy inference system (ANFIS) to simulate the effects of throw, eccentric speed, closed side setting, and the size of the particle on crusher output. The developed simulation model was adjusted and authenticated alongside the experimental data of the investigated parameters. The model's performance was computed by the use of several prediction criteria skills. The results of the study indicated that the developed ANFIS model could simulate the Cone crusher output and give a dependable forecast of the cumulative weight fraction. The researchers resolved that the model fostered was a suitable instrument for the onsite cone crusher assessment.

中文翻译：

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处理数据驱动模型的机器学习方法，用于碎石厂圆锥破碎机产量的模拟和预测

石头和其他颗粒的研磨和粉碎与各种重要应用有关。不同的部门在这一领域不断发展。在破碎行业，工厂在严格的条件下运行，其中许多涉及研磨材料。因此，影响破碎机性能的因素有很多。本研究调查了自适应神经模糊推理系统 (ANFIS) 模拟抛掷、偏心速度、封闭侧设置和颗粒尺寸对破碎机输出的影响的能力。开发的仿真模型与研究参数的实验数据一起进行了调整和验证。模型's 的表现是通过使用几种预测标准技能来计算的。研究结果表明，开发的 ANFIS 模型可以模拟圆锥破碎机的输出并给出累积重量分数的可靠预测。研究人员认为，所培养的模型是用于现场圆锥破碎机评估的合适工具。