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

• Autorzy: Fang Huaiying , Ji Xiaosheng , Yang Jianhong , Yang Yuxuan , Ji Tianchen , Wei Chaoming

Identyfikatory

DOI

10.37190/ppmp/188759

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

cone crusher; coarse aggregate; instance segmentation; gradation; intelligent control

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Physicochemical Problems of Mineral Processing
• Rocznik: 2024
• Tom: Vol. 60, iss. 3
• Strony: art. no. 188759
• Opis fizyczny: Bibliogr. 30 poz., rys., tab., wykr.

Twórcy

autor

Fang Huaiying

• School of Mechatronics and Automation, Huaqiao University, Xiamen 361021, China
• Fujian Key Laboratory of Green Intelligent Drive and Transmission for Mobile Machinery, Xiamen 361021, China

autor

Ji Xiaosheng

• School of Mechatronics and Automation, Huaqiao University, Xiamen 361021, China

autor

Yang Jianhong

• School of Mechatronics and Automation, Huaqiao University, Xiamen 361021, China

autor

Yang Yuxuan

• Fujian Southern Road Machinery Co., Ltd, Quanzhou 362021, China

autor

Ji Tianchen

• School of Mechatronics and Automation, Huaqiao University, Xiamen 361021, China

autor

Wei Chaoming

• Fujian Southern Road Machinery Co., Ltd, Quanzhou 362021, China

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