The testing and optimization of a bio-inspired textured piston for the BW-250 mud pump

Gao, Tianyu; Wang, Yumo; Xie, Bingxing; Chen, Tingkun; Xu, Jin; Zhang, Ning; Tang, Danna

doi:10.1016/j.polymertesting.2023.108296

Cited by 2 publications

(2 citation statements)

References 34 publications

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“…Zhang et al [ 155 ] designed a bionic wear-resistant shovel by arranging a groove structure on the outer surface. Likewise, Gao et al [ 156 ] designed bio-inspired pitted pistons with a pit array texture, as shown in Figure 20 b. The pit texture can provide lubrication and store abrasive particles, enhancing the wear condition of the piston surface.…”

Section: Biomimetic Soil-engaging Componentsmentioning

confidence: 99%

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Biomimetic Design of Soil-Engaging Components: A Review

Xu,

Qi,

Gao

et al. 2024

Biomimetics

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Soil-engaging components play a critical role in agricultural production and engineering construction. However, the soil-engaging components directly interacting with the soil often suffer from the problems of high resistance, adhesion, and wear, which significantly reduce the efficiency and quality of soil operations. A large number of featured studies on the design of soil-engaging components have been carried out while applying the principles of bionics extensively, and significant research results have been achieved. This review conducts a comprehensive literature survey on the application of biomimetics in the design of soil-engaging components. The focus is on performance optimization in regard to the following three aspects: draught reduction, anti-adhesion, and wear resistance. The mechanisms of various biomimetic soil-engaging components are systematically explained. Based on the literature analysis and biomimetic research, future trends in the development of biomimetic soil-engaging components are discussed from both the mechanism and application perspectives. This research is expected to provide new insights and inspiration for addressing related scientific and engineering challenges.

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Section: Biomimetic Soil-engaging Componentsmentioning

confidence: 99%

“… Other biomimetic soil-engaging components with wear-resistant characteristics. ( a ) Bionic wheels [ 153 ], ( b ) bio-inspired textured piston for mud pump [ 156 ], ( c ) biomimetic anti-abrasion cone form component used for soil cone penetrometer [ 157 ]. …”

Section: Figurementioning

confidence: 99%

Biomimetic Design of Soil-Engaging Components: A Review

Xu,

Qi,

Gao

et al. 2024

Biomimetics

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Integration of machine learning prediction and optimization for determination of the coefficient of friction of textured UHMWPE surfaces

Feng,

Liu,

van Ostayen

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part J:

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The frictional performance of water-lubricated UHMWPE is influenced by the combination of structural parameters and operating conditions. To improve the efficiency of optimal design of surface texture aimed at improving frictional performance, a novel integration of the Orthogonal Array method (OAM), machine learning (ML) prediction, and Particle Swarm Optimization (PSO) is proposed for predicting and optimizing the coefficient of friction (COF) of copper ball-textured UHMWPE surfaces using a small dataset. In order to reduce manufacturing and testing cost, decrease required training samples for ML algorithm, OAM which could efficiently acquire data set with comprehensive feature information is used to determine the parameters of test samples to generate a small but effective dataset. 25 textured samples based on L16 (44) and L9 (34) are fabricated, with the parameter set determined using OAM. COFs of the samples are tested using RTEC tribo-tester. Trend analysis is conducted to investigate the influence of force, frequency, depth and ellipse axis ratio on COF. Multi-linear Regression (MLR) and Gaussian Process Regression are employed. MLR exhibits better prediction accuracy and is integrated with PSO to minimize COF. The error between the experimental and the theoretical results obtained by the integration method of MLR and PSO is only 1.04%, demonstrating the feasibility of predicting COF and optimizing surface texture using the integrated method with a limited dataset determined by OAM.

show abstract

The testing and optimization of a bio-inspired textured piston for the BW-250 mud pump

Cited by 2 publications

References 34 publications

Biomimetic Design of Soil-Engaging Components: A Review

Biomimetic Design of Soil-Engaging Components: A Review

Integration of machine learning prediction and optimization for determination of the coefficient of friction of textured UHMWPE surfaces

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