Adaptive control method and experimental study of cone crusher based on aggregate online detection

Huaiying Fang, Xiaosheng Ji, Jianhong Yang, Yuxuan Yang, Tianchen Ji, Chaoming Wei
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Abstract

The size of the discharge outlet of a cone crusher directly impacts the size of the aggregate produced. However, the discharge outlet is still adjusted manually, which has a significant error and affects production efficiency. For this reason, this study proposed an adaptive control method for cone crushers based on aggregate online detection. Firstly, the aggregate image was segmented using an instance segmentation model and the anchor and structure of the model were optimised. Then, this study proposed an evaluation method for quickly and accurately assessing the overall segmentation effect of network models. By comparing the results with those before optimisation, the accuracy of the optimised network model was improved from 0.923 to 0.940. Finally, an adaptive control experiment was conducted based on the online aggregate detection results. The experimental results showed that the discharge particle size distribution of the cone crusher becomes more stable after intelligent control is added, with the variance of the proportion of cumulative gradation at 15 mm decreased from 34.3 to 14.4. These results indicated that the developed adaptive control system effectively controls the fine processing of coarse aggregates and significantly improves the quality of aggregate crushing and processing.
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基于骨料在线检测的圆锥破碎机自适应控制方法及实验研究
圆锥破碎机排料口的大小直接影响所生产骨料的大小。然而,排料口仍需人工调节,误差较大,影响生产效率。为此,本研究提出了一种基于骨料在线检测的圆锥破碎机自适应控制方法。首先,利用实例分割模型对骨料图像进行分割,并对模型的锚点和结构进行了优化。然后,本研究提出了一种评估方法,用于快速准确地评估网络模型的整体分割效果。通过与优化前的结果比较,优化后网络模型的准确度从 0.923 提高到 0.940。最后,根据在线聚合检测结果进行了自适应控制实验。实验结果表明,加入智能控制后,圆锥破碎机的出料粒度分布更加稳定,15 毫米处的累积级配比例方差从 34.3 降至 14.4。这些结果表明,所开发的自适应控制系统能有效控制粗骨料的精细加工,显著提高骨料破碎和加工质量。
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