A procedure to recognize individual discontinuities in rock mass from measurement while drilling(MWD)technology is developed,using the binary pattern of structural rock characteristics obtained from in-hole images for...A procedure to recognize individual discontinuities in rock mass from measurement while drilling(MWD)technology is developed,using the binary pattern of structural rock characteristics obtained from in-hole images for calibration.Data from two underground operations with different drilling technology and different rock mass characteristics are considered,which generalizes the application of the methodology to different sites and ensures the full operational integration of MWD data analysis.Two approaches are followed for site-specific structural model building:a discontinuity index(DI)built from variations in MWD parameters,and a machine learning(ML)classifier as function of the drilling parameters and their variability.The prediction ability of the models is quantitatively assessed as the rate of recognition of discontinuities observed in borehole logs.Differences between the parameters involved in the models for each site,and differences in their weights,highlight the site-dependence of the resulting models.The ML approach offers better performance than the classical DI,with recognition rates in the range 89%to 96%.However,the simpler DI still yields fairly accurate results,with recognition rates 70%to 90%.These results validate the adaptive MWD-based methodology as an engineering solution to predict rock structural condition in underground mining operations.展开更多
The working conditions of the MK-3 type full hydraulic tunnel drilling machine during the course of drilling were analyzed. Based on the energy balance governing equations for the drill rod, the temperature field of d...The working conditions of the MK-3 type full hydraulic tunnel drilling machine during the course of drilling were analyzed. Based on the energy balance governing equations for the drill rod, the temperature field of drill rod at the normal and non-normal working conditions was numerically obtained. The numerical results show that the maximum temperature at the head of drill rod under the normal working circumstance is insufficient to ignite the gas. But under the non-normal working condition, the local high temperature can ignite the gas easily and cause the fire. In order to prevent the gas fire, the occurrence of the non-normal operating condition must be prevented as far as possible during the drilling.展开更多
High Speed Drilling Electrical Discharge Machining (HSDEDM) uses controlled electric sparks to erode the metal in a work-piece. Through the years, HSDEDM process has widely been used in high speed drilling and in manu...High Speed Drilling Electrical Discharge Machining (HSDEDM) uses controlled electric sparks to erode the metal in a work-piece. Through the years, HSDEDM process has widely been used in high speed drilling and in manufacturing large aspect ratio holes for hard-to-machine material. The power supplies of HSDEDM providing high power applica-tions can have different topologies. In this paper, a novel Pulsed-Width-Modulated (PWM) half-bridge HSDEDM power supply that achieves Zero-Voltage-Switching (ZVS) for switches and Zero-Current-Switching (ZCS) for the dis-charge gap has been developed. This power supply has excellent features that include minimal component count and inherent protection under short circuit conditions. This topology has an energy conservation feature and removes the need for output bulk capacitors and resistances. Energy used in the erosion process will be controlled by the switched IGBTs in the half-bridge network and be transferred to the gap between the tool and work-piece. The relative tool wear and machining speed of our proposed topology have been compared with that of a normal power supply with current limiting resistances.展开更多
The identification and recording of drilling conditions are crucial for ensuring drilling safety and efficiency. However, the traditional approach of relying on the subjective determination of drilling masters based o...The identification and recording of drilling conditions are crucial for ensuring drilling safety and efficiency. However, the traditional approach of relying on the subjective determination of drilling masters based on experience formulas is slow and not suitable for rapid drilling. In this paper, we propose a drilling condition classification method based on a neural network model. The model uses an improved Bidirectional Gated Recurrent Unit (BiGRU) combined with an attention mechanism to accurately classify seven common drilling conditions simultaneously, achieving an average accuracy of 91.63%. The model also demonstrates excellent generalization ability, real-time performance, and accuracy, making it suitable for actual production. Additionally, the model has excellent expandability, which enhances its potential for further application.展开更多
Two new AlTiN coated cemented carbide drills with Al content of 40% and 55% in weight are developed for high efficiency dry drilling of 40Cr. By studying tool durability, machined hole quality, tool wear mechanism, ch...Two new AlTiN coated cemented carbide drills with Al content of 40% and 55% in weight are developed for high efficiency dry drilling of 40Cr. By studying tool durability, machined hole quality, tool wear mechanism, chip deformation, and lubrication, the dry drilling performance of the two kinds of coated drills is analyzed. Experimental results show that the AlTiN coated drills are suitable for high efficiency dry drilling and can obtain higher quality of machined holes. The tool durability of the drill with 55% Al content is 1. 3 times of that of the drill with 40% Al content at the cutting speed of 90 m/min. The wear mechanism of two AlTiN coatings are studied in experiments. During dry drilling process, oxidative wear appears in both two kinds of drills. The oxide film is formed on the top of the coated drill containing Al content of 55%. And the oxide film helps to increase its high temperature resistance and decrease the coating flaking, thus the drill is failed because of coating subsidence. The drill with less Al content is failed due to peeling and breakage. The lubricated condition in dry drilling is improved by the high Al content coating. It helps to reduce the cutting deformation and benefits to improve the quality of machined holes. The AlTiN coating with higher Al content shows longer tool life and higher quality of machined holes in high efficiency dry drilling. Its tool life increases by 30% compared with that of the coating with less Al content.展开更多
In this paper, a new forming model of the feed direction burr for drilling process is presented. The feed direction burr formation is experimented and studied. The related theories are analyzed, and the influential ...In this paper, a new forming model of the feed direction burr for drilling process is presented. The feed direction burr formation is experimented and studied. The related theories are analyzed, and the influential factors of the feed direction burrs are pointed out. Furthermore, a certain number of new measures to prevent and decrease the burr in drilling process are advanced.展开更多
钻井液流变性是钻井液流动和变形的特性,对于携带与悬浮岩屑、提高钻进速度至关重要,准确掌握钻井液流变参数是保证井眼清洁与高效钻进的前提。提出一种基于卷积神经网络(Convolu-tionalNeuralNetwork,CNN)的钻井液流变参数智能识别方法...钻井液流变性是钻井液流动和变形的特性,对于携带与悬浮岩屑、提高钻进速度至关重要,准确掌握钻井液流变参数是保证井眼清洁与高效钻进的前提。提出一种基于卷积神经网络(Convolu-tionalNeuralNetwork,CNN)的钻井液流变参数智能识别方法,通过磁力搅拌产生稳定的钻井液流动图像,利用多种数据增强方法增加图像数量并建立数据库,增强模型的鲁棒性和泛化能力。优化AlexNet卷积神经网络算法,构建钻井液流变参数识别模型。将数据库划分为训练集:验证集:测试集=7:2:1,对训练集进行迭代训练并通过验证集调整参数获得最佳拟合模型。此外,运用混淆矩阵、卷积核可视化技术和类激活技术(Gradient-weighted Class Activation Mapping,Grad-CAM)对模型进行多方位评估。结果表明:(1)钻井液流变参数识别模型对钻井液塑性黏度测试的宏精确率为95.2%,宏召回率为94.7%,宏F1值为0.95。(2)对钻井液表观黏度测试的宏精确率为91.6%,宏召回率为91.5%,宏F1值为0.90。(3)利用卷积核可视化技术和Grad-CAM对特征提取进行可视化处理,发现钻井液波纹形状和大小会影响模型流变参数识别准确度。(4)室内测试结果表明,该模型的测试误差为±2 mPa·s,在设计允许范围以内,具有较高的识别精确度和稳定性。所提出的钻井液流变参数实时智能识别方法可为安全、快速和准确地进行钻井液流变性测试提供智能化技术思路。展开更多
基金conducted under the illu MINEation project, funded by the European Union’s Horizon 2020 research and innovation program under grant agreement (No. 869379)supported by the China Scholarship Council (No. 202006370006)
文摘A procedure to recognize individual discontinuities in rock mass from measurement while drilling(MWD)technology is developed,using the binary pattern of structural rock characteristics obtained from in-hole images for calibration.Data from two underground operations with different drilling technology and different rock mass characteristics are considered,which generalizes the application of the methodology to different sites and ensures the full operational integration of MWD data analysis.Two approaches are followed for site-specific structural model building:a discontinuity index(DI)built from variations in MWD parameters,and a machine learning(ML)classifier as function of the drilling parameters and their variability.The prediction ability of the models is quantitatively assessed as the rate of recognition of discontinuities observed in borehole logs.Differences between the parameters involved in the models for each site,and differences in their weights,highlight the site-dependence of the resulting models.The ML approach offers better performance than the classical DI,with recognition rates in the range 89%to 96%.However,the simpler DI still yields fairly accurate results,with recognition rates 70%to 90%.These results validate the adaptive MWD-based methodology as an engineering solution to predict rock structural condition in underground mining operations.
基金Supported by the "863" Program(2003AA131100-02-06)the National Natural Science Foundation of China(50274061)
文摘The working conditions of the MK-3 type full hydraulic tunnel drilling machine during the course of drilling were analyzed. Based on the energy balance governing equations for the drill rod, the temperature field of drill rod at the normal and non-normal working conditions was numerically obtained. The numerical results show that the maximum temperature at the head of drill rod under the normal working circumstance is insufficient to ignite the gas. But under the non-normal working condition, the local high temperature can ignite the gas easily and cause the fire. In order to prevent the gas fire, the occurrence of the non-normal operating condition must be prevented as far as possible during the drilling.
文摘High Speed Drilling Electrical Discharge Machining (HSDEDM) uses controlled electric sparks to erode the metal in a work-piece. Through the years, HSDEDM process has widely been used in high speed drilling and in manufacturing large aspect ratio holes for hard-to-machine material. The power supplies of HSDEDM providing high power applica-tions can have different topologies. In this paper, a novel Pulsed-Width-Modulated (PWM) half-bridge HSDEDM power supply that achieves Zero-Voltage-Switching (ZVS) for switches and Zero-Current-Switching (ZCS) for the dis-charge gap has been developed. This power supply has excellent features that include minimal component count and inherent protection under short circuit conditions. This topology has an energy conservation feature and removes the need for output bulk capacitors and resistances. Energy used in the erosion process will be controlled by the switched IGBTs in the half-bridge network and be transferred to the gap between the tool and work-piece. The relative tool wear and machining speed of our proposed topology have been compared with that of a normal power supply with current limiting resistances.
基金supported by open fund(PLN2021-23)of National Key Laboratory of Oil and Gas Reservoir Geology and Exploitation(Southwest Petroleum University).
文摘The identification and recording of drilling conditions are crucial for ensuring drilling safety and efficiency. However, the traditional approach of relying on the subjective determination of drilling masters based on experience formulas is slow and not suitable for rapid drilling. In this paper, we propose a drilling condition classification method based on a neural network model. The model uses an improved Bidirectional Gated Recurrent Unit (BiGRU) combined with an attention mechanism to accurately classify seven common drilling conditions simultaneously, achieving an average accuracy of 91.63%. The model also demonstrates excellent generalization ability, real-time performance, and accuracy, making it suitable for actual production. Additionally, the model has excellent expandability, which enhances its potential for further application.
文摘Two new AlTiN coated cemented carbide drills with Al content of 40% and 55% in weight are developed for high efficiency dry drilling of 40Cr. By studying tool durability, machined hole quality, tool wear mechanism, chip deformation, and lubrication, the dry drilling performance of the two kinds of coated drills is analyzed. Experimental results show that the AlTiN coated drills are suitable for high efficiency dry drilling and can obtain higher quality of machined holes. The tool durability of the drill with 55% Al content is 1. 3 times of that of the drill with 40% Al content at the cutting speed of 90 m/min. The wear mechanism of two AlTiN coatings are studied in experiments. During dry drilling process, oxidative wear appears in both two kinds of drills. The oxide film is formed on the top of the coated drill containing Al content of 55%. And the oxide film helps to increase its high temperature resistance and decrease the coating flaking, thus the drill is failed because of coating subsidence. The drill with less Al content is failed due to peeling and breakage. The lubricated condition in dry drilling is improved by the high Al content coating. It helps to reduce the cutting deformation and benefits to improve the quality of machined holes. The AlTiN coating with higher Al content shows longer tool life and higher quality of machined holes in high efficiency dry drilling. Its tool life increases by 30% compared with that of the coating with less Al content.
文摘In this paper, a new forming model of the feed direction burr for drilling process is presented. The feed direction burr formation is experimented and studied. The related theories are analyzed, and the influential factors of the feed direction burrs are pointed out. Furthermore, a certain number of new measures to prevent and decrease the burr in drilling process are advanced.
文摘钻井液流变性是钻井液流动和变形的特性,对于携带与悬浮岩屑、提高钻进速度至关重要,准确掌握钻井液流变参数是保证井眼清洁与高效钻进的前提。提出一种基于卷积神经网络(Convolu-tionalNeuralNetwork,CNN)的钻井液流变参数智能识别方法,通过磁力搅拌产生稳定的钻井液流动图像,利用多种数据增强方法增加图像数量并建立数据库,增强模型的鲁棒性和泛化能力。优化AlexNet卷积神经网络算法,构建钻井液流变参数识别模型。将数据库划分为训练集:验证集:测试集=7:2:1,对训练集进行迭代训练并通过验证集调整参数获得最佳拟合模型。此外,运用混淆矩阵、卷积核可视化技术和类激活技术(Gradient-weighted Class Activation Mapping,Grad-CAM)对模型进行多方位评估。结果表明:(1)钻井液流变参数识别模型对钻井液塑性黏度测试的宏精确率为95.2%,宏召回率为94.7%,宏F1值为0.95。(2)对钻井液表观黏度测试的宏精确率为91.6%,宏召回率为91.5%,宏F1值为0.90。(3)利用卷积核可视化技术和Grad-CAM对特征提取进行可视化处理,发现钻井液波纹形状和大小会影响模型流变参数识别准确度。(4)室内测试结果表明,该模型的测试误差为±2 mPa·s,在设计允许范围以内,具有较高的识别精确度和稳定性。所提出的钻井液流变参数实时智能识别方法可为安全、快速和准确地进行钻井液流变性测试提供智能化技术思路。
