Efficient hard-rock fragmentation remains a critical challenge in mechanized mining.This study designed an adjustable-spacing mold and conducted double cutting pick indentation tests on granite.Mechanical responses an...Efficient hard-rock fragmentation remains a critical challenge in mechanized mining.This study designed an adjustable-spacing mold and conducted double cutting pick indentation tests on granite.Mechanical responses and fragmentation characteristics under varying horizontal stresses,pick spacings,and groove depths were systematically analyzed.Unidirectional stress concentration altered the rock fragmentation modes,exhibiting a dual effect on the fragmentation process.The maximum indentation force(F_(max)),indentation hardness index(IHI),indentation modulus(IM),and indentation energy(W)initially increased and then decreased with rising horizontal stress.Appropriate spacing promoted radial crack coalescence,whereas too small a spacing(20 mm)caused repetitive re-fragmentation of rock chips,and too large a spacing(50 mm)resulted in unbroken ridges.Pre-cut grooves weakened the rock,reducing F_(max) and specific energy(SE),thus improving fragmentation efficiency,although the improvement slowed beyond a 10-mm groove depth.Based on the results and rock-mass conditioning assisted fragmentation mechanism,a“stress-structure dual control”assisted fragmentation mechanism was proposed,and a“pre-drilling unloading−alternate stopping”mining scheme was exploratorily designed.This approach creates favorable conditions for rock fragmentation by reducing stress levels and rock mass integrity in target zones,providing theoretical support and an engineering paradigm for mecheanized mining of deep resources.展开更多
Machine picking in cotton is an emerging practice in India,to solve the problems of labour shortages and production costs increasing.Cotton production has been declining in recent years;however,the high density planti...Machine picking in cotton is an emerging practice in India,to solve the problems of labour shortages and production costs increasing.Cotton production has been declining in recent years;however,the high density planting system(HDPS)offers a viable method to enhance productivity by increasing plant populations per unit area,optimizing resource utilization,and facilitating machine picking.Cotton is an indeterminate plant that produce excessive vegeta-tive growth in favorable soil fertility and moisture conditions,which posing challenges for efficient machine picking.To address this issue,the application of plant growth retardants(PGRs)is essential for controlling canopy architecture.PGRs reduce internode elongation,promote regulated branching,and increase plant compactness,making cotton plants better suited for machine picking.PGRs application also optimizes photosynthates distribution between veg-etative and reproductive growth,resulting in higher yields and improved fibre quality.The integration of HDPS and PGRs applications results in an optimal plant architecture for improving machine picking efficiency.However,the success of this integration is determined by some factors,including cotton variety,environmental conditions,and geographical variations.These approaches not only address yield stagnation and labour shortages but also help to establish more effective and sustainable cotton farming practices,resulting in higher cotton productivity.展开更多
Real-time identification of rock strength and cuttability based on monitoring while cutting during excavation is essential for key procedures such as the precise adjustment of excavation parameters and the in-situ mod...Real-time identification of rock strength and cuttability based on monitoring while cutting during excavation is essential for key procedures such as the precise adjustment of excavation parameters and the in-situ modification of hard rocks.This study proposes an in-telligent approach for predicting rock strength and cuttability.A database comprising 132 data sets is established,containing cutting para-meters(such as cutting depth and pick angle),cutting responses(such as specific energy and instantaneous cutting rate),and rock mech-anical parameters collected from conical pick-cutting experiments.These parameters serve as input features for predicting the uniaxial compressive strength and tensile strength of rocks using regression fitting and machine learning methodologies.In addition,rock cuttabil-ity is classified using a combination of the analytic hierarchy process and fuzzy comprehensive evaluation method,and subsequently iden-tified through machine learning approaches.Various models are compared to determine the optimal predictive and classification models.The results indicate that the optimal model for uniaxial compressive strength and tensile strength prediction is the genetic algorithm-optimized backpropagation neural network model,and the optimal model for rock cuttability classification is the radial basis neural network model.展开更多
Water-weakening presents a promising strategy for the in-situ improvement of rock cuttability.This study unveils the influences of water saturation on the mechanical response and fragmentation characteristics of rock ...Water-weakening presents a promising strategy for the in-situ improvement of rock cuttability.This study unveils the influences of water saturation on the mechanical response and fragmentation characteristics of rock samples.A series of rock-cutting tests using conical pick indentation was conducted on three types of sandstone samples under both dry and water-saturated conditions.The relationships between cutting force and indentation depth,as well as typical cuttability indices are determined and compared for dry and water-saturated samples.The experimental results reveal that the presence of water facilitates shearing failure in rock samples,as well as alleviates the fluctuations in the cutting force-indentation depth curve Furthermore,the peak cutting force(F_(p)),cutting work(W_(p)),and specific energy(SE)undergo apparent decrease after water saturation,whereas the trend in the indentation depth at rock failure(D_(f))varies across different rock types.Additionally,the water-induced percentage reductions in F_(p)and SE correlate positively with the quartz and swelling clay content within the rocks,suggesting that the cuttability improvement due to water saturation is attributed to the combined effects of stress corrosion and frictional reduction.These findings carry significant implications for improving rock cuttability in mechanized excavation of hard rock formations.展开更多
With the continuous expansion of deep underground engineering and the growing demand for safety monitoring,microseismic monitoring has become a core method for early warning of rock mass fracture and engineering stabi...With the continuous expansion of deep underground engineering and the growing demand for safety monitoring,microseismic monitoring has become a core method for early warning of rock mass fracture and engineering stability assessment.To address problems in existing methods,such as low data processing efficiency and poor phase recognition accuracy under low signal-to-noise ratio(SNR)conditions in complex geological environments,this study proposes an intelligent phase picking model based on ResUNet.The model integrates the residual learning mechanism of ResNet with the multi-scale feature extraction capability of UNet,effectively mitigating the vanishing gradient problem in deep networks.It also achieves cross-layer fusion of shallow detail features and deep semantic features through skip connections in the encoder-decoder structure.Compared with traditional short-time average/long-time average(STA/LTA)algorithms and advanced neural network models such as PhaseNet and EQTransformer,ResUNet shows superior performance in picking P-and S-wave phases.The model was trained on 400000 labeled microseismic signals from the Stanford earthquake dataset(STEAD)and was successfully applied to the Shizhuyuan polymetallic mine in Hunan Province,China.The results demonstrate that ResUNet achieves high picking accuracy and robustness in complex geological conditions,offering reliable technical support for early warning of disasters such as rockburst in deep underground engineering.展开更多
针对菠萝采摘作业中劳动力需求大以及采摘效率低等问题,对单目视觉目标识别和定位菠萝目标的抱扭式菠萝采摘机进行了研究。该菠萝采摘机由底盘、采摘机构、运动机构、机器视觉机构和运输机构组成;机器视觉系统选用Raspberry pi 3B作为...针对菠萝采摘作业中劳动力需求大以及采摘效率低等问题,对单目视觉目标识别和定位菠萝目标的抱扭式菠萝采摘机进行了研究。该菠萝采摘机由底盘、采摘机构、运动机构、机器视觉机构和运输机构组成;机器视觉系统选用Raspberry pi 3B作为图像处理器,并装载YOLO算法作为菠萝识别的分类器。采摘机构模拟人工采摘时抱紧扭转的动作行为,通过气缸推动V型机械爪进退实现对菠萝的夹持,通过控制舵机转动完成菠萝的扭断,采摘后将果实通过运输机构运送到包装点。研制了菠萝采摘机样机,利用样机进行了实验室模拟采摘实验。实验结果证明,该菠萝采摘机的视觉系统能够快速判断菠萝的位置,采摘机构夹持定位准确,可顺利完成采摘动作。该样机的研制为菠萝采摘提供了可行的技术参考。展开更多
基金National Major Science and Technology Project for Deep Earth Exploration(No.2025ZD1008301)National Natural Science Foundation of China(No.52374153)for the financial supportthe support of the China Scholarship Council.
文摘Efficient hard-rock fragmentation remains a critical challenge in mechanized mining.This study designed an adjustable-spacing mold and conducted double cutting pick indentation tests on granite.Mechanical responses and fragmentation characteristics under varying horizontal stresses,pick spacings,and groove depths were systematically analyzed.Unidirectional stress concentration altered the rock fragmentation modes,exhibiting a dual effect on the fragmentation process.The maximum indentation force(F_(max)),indentation hardness index(IHI),indentation modulus(IM),and indentation energy(W)initially increased and then decreased with rising horizontal stress.Appropriate spacing promoted radial crack coalescence,whereas too small a spacing(20 mm)caused repetitive re-fragmentation of rock chips,and too large a spacing(50 mm)resulted in unbroken ridges.Pre-cut grooves weakened the rock,reducing F_(max) and specific energy(SE),thus improving fragmentation efficiency,although the improvement slowed beyond a 10-mm groove depth.Based on the results and rock-mass conditioning assisted fragmentation mechanism,a“stress-structure dual control”assisted fragmentation mechanism was proposed,and a“pre-drilling unloading−alternate stopping”mining scheme was exploratorily designed.This approach creates favorable conditions for rock fragmentation by reducing stress levels and rock mass integrity in target zones,providing theoretical support and an engineering paradigm for mecheanized mining of deep resources.
文摘Machine picking in cotton is an emerging practice in India,to solve the problems of labour shortages and production costs increasing.Cotton production has been declining in recent years;however,the high density planting system(HDPS)offers a viable method to enhance productivity by increasing plant populations per unit area,optimizing resource utilization,and facilitating machine picking.Cotton is an indeterminate plant that produce excessive vegeta-tive growth in favorable soil fertility and moisture conditions,which posing challenges for efficient machine picking.To address this issue,the application of plant growth retardants(PGRs)is essential for controlling canopy architecture.PGRs reduce internode elongation,promote regulated branching,and increase plant compactness,making cotton plants better suited for machine picking.PGRs application also optimizes photosynthates distribution between veg-etative and reproductive growth,resulting in higher yields and improved fibre quality.The integration of HDPS and PGRs applications results in an optimal plant architecture for improving machine picking efficiency.However,the success of this integration is determined by some factors,including cotton variety,environmental conditions,and geographical variations.These approaches not only address yield stagnation and labour shortages but also help to establish more effective and sustainable cotton farming practices,resulting in higher cotton productivity.
基金supported by the National Natural Science Foundation of China(Nos.52174099 and 52474168)the Science and Technology Innovation Program of Hunan Province,China(No.2023RC3050)+1 种基金the Natural Science Foundation of Hunan,China(No.2024JJ4064)the Open Fund of the State Key Laboratory of Safety Technology of Metal Mines(No.kfkt2023-01).
文摘Real-time identification of rock strength and cuttability based on monitoring while cutting during excavation is essential for key procedures such as the precise adjustment of excavation parameters and the in-situ modification of hard rocks.This study proposes an in-telligent approach for predicting rock strength and cuttability.A database comprising 132 data sets is established,containing cutting para-meters(such as cutting depth and pick angle),cutting responses(such as specific energy and instantaneous cutting rate),and rock mech-anical parameters collected from conical pick-cutting experiments.These parameters serve as input features for predicting the uniaxial compressive strength and tensile strength of rocks using regression fitting and machine learning methodologies.In addition,rock cuttabil-ity is classified using a combination of the analytic hierarchy process and fuzzy comprehensive evaluation method,and subsequently iden-tified through machine learning approaches.Various models are compared to determine the optimal predictive and classification models.The results indicate that the optimal model for uniaxial compressive strength and tensile strength prediction is the genetic algorithm-optimized backpropagation neural network model,and the optimal model for rock cuttability classification is the radial basis neural network model.
基金supported by financial grants from the National Natural Science Foundation of China(Grant Nos.52334003 and 52104111)the National Key R&D Program of China(Grant No.2022YFC2905600)。
文摘Water-weakening presents a promising strategy for the in-situ improvement of rock cuttability.This study unveils the influences of water saturation on the mechanical response and fragmentation characteristics of rock samples.A series of rock-cutting tests using conical pick indentation was conducted on three types of sandstone samples under both dry and water-saturated conditions.The relationships between cutting force and indentation depth,as well as typical cuttability indices are determined and compared for dry and water-saturated samples.The experimental results reveal that the presence of water facilitates shearing failure in rock samples,as well as alleviates the fluctuations in the cutting force-indentation depth curve Furthermore,the peak cutting force(F_(p)),cutting work(W_(p)),and specific energy(SE)undergo apparent decrease after water saturation,whereas the trend in the indentation depth at rock failure(D_(f))varies across different rock types.Additionally,the water-induced percentage reductions in F_(p)and SE correlate positively with the quartz and swelling clay content within the rocks,suggesting that the cuttability improvement due to water saturation is attributed to the combined effects of stress corrosion and frictional reduction.These findings carry significant implications for improving rock cuttability in mechanized excavation of hard rock formations.
基金Project(2022YFC2905100)supported by the National Key Research and Development Program of ChinaProject(52174098)supported by the National Natural Science Foundation of China。
文摘With the continuous expansion of deep underground engineering and the growing demand for safety monitoring,microseismic monitoring has become a core method for early warning of rock mass fracture and engineering stability assessment.To address problems in existing methods,such as low data processing efficiency and poor phase recognition accuracy under low signal-to-noise ratio(SNR)conditions in complex geological environments,this study proposes an intelligent phase picking model based on ResUNet.The model integrates the residual learning mechanism of ResNet with the multi-scale feature extraction capability of UNet,effectively mitigating the vanishing gradient problem in deep networks.It also achieves cross-layer fusion of shallow detail features and deep semantic features through skip connections in the encoder-decoder structure.Compared with traditional short-time average/long-time average(STA/LTA)algorithms and advanced neural network models such as PhaseNet and EQTransformer,ResUNet shows superior performance in picking P-and S-wave phases.The model was trained on 400000 labeled microseismic signals from the Stanford earthquake dataset(STEAD)and was successfully applied to the Shizhuyuan polymetallic mine in Hunan Province,China.The results demonstrate that ResUNet achieves high picking accuracy and robustness in complex geological conditions,offering reliable technical support for early warning of disasters such as rockburst in deep underground engineering.
文摘针对菠萝采摘作业中劳动力需求大以及采摘效率低等问题,对单目视觉目标识别和定位菠萝目标的抱扭式菠萝采摘机进行了研究。该菠萝采摘机由底盘、采摘机构、运动机构、机器视觉机构和运输机构组成;机器视觉系统选用Raspberry pi 3B作为图像处理器,并装载YOLO算法作为菠萝识别的分类器。采摘机构模拟人工采摘时抱紧扭转的动作行为,通过气缸推动V型机械爪进退实现对菠萝的夹持,通过控制舵机转动完成菠萝的扭断,采摘后将果实通过运输机构运送到包装点。研制了菠萝采摘机样机,利用样机进行了实验室模拟采摘实验。实验结果证明,该菠萝采摘机的视觉系统能够快速判断菠萝的位置,采摘机构夹持定位准确,可顺利完成采摘动作。该样机的研制为菠萝采摘提供了可行的技术参考。
