共查询到19条相似文献，搜索用时 78 毫秒
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设计了适用于用热压法制造φ２７５大口径镶旬石钻头的石墨模具结构;给出了合理的石墨模具尺寸数据和公差;讨论了大口径孕镶金刚石钻头的配方参数;提出了大口径热压孕镶金刚石钻头的烧结工艺方法;列举了大口径热压孕镶金刚石钻头在三峡工程工地中的现场使用情况。 相似文献
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针对东北地区金3井沙河子组致密泥岩和砂砾岩互层,地层软硬交错、研磨性强,钻进机械效率低,钻头寿命短的问题,通过改进胎体配方、采用耐磨的混镶金刚石热压镶嵌齿作为切削齿、优化了钻头的切削结构、采用CFD软件进行钻头水力结构模拟与优化,研制了混镶金刚石钻头（ NR826M）。所设计的钻头在金3井共使用了3只,总进尺784.69 m,钻头平均机械钻速1.03 m/h,单只钻头最高进尺295.03 m,钻头寿命是牙轮钻头的6.5倍,机械钻速是牙轮钻头的1.5倍,为该井缩短了钻井周期40天,降低了钻井施工成本,也为该地区同类地层的钻头选型提供了更多的选择。 相似文献
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主辅磨料双切削作用金刚石钻头研究 总被引：5，自引：0，他引：5
提出了主辅磨料双切削作用金刚石钻头的定义，介绍了该类钻头的使用范围和基本配方，并对配方的合理性进行了理论计算分析，列举了典型使用实例。 相似文献
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《Geomechanics and Geoengineering》2013,8(1):53-61
In many rock engineering applications such as foundations, slopes and tunnels, the intact rock properties are not actually determined by laboratory tests, due to the requirements of high quality core samples and sophisticated test equipments. Thus, predicting the rock properties by using empirical equations has been an attractive research topic relating to rock engineering practice for many years. Soft computing techniques are now being used as alternative statistical tools. In this study, artificial neural network models were developed to predict the rock properties of the intact rock, by using sound level produced during rock drilling. A database of 832 datasets, including drill bit diameter, drill bit speed, penetration rate of the drill bit and equivalent sound level (Leq) produced during drilling for input parameters, and uniaxial compressive strength (UCS), Schmidt rebound number (SRN), dry density (ρ), P-wave velocity (Vp), tensile strength (TS), modulus of elasticity (E) and percentage porosity (n) of intact rock for output, was established. The constructed models were checked using various prediction performance indices. Goodness of the fit measures revealed that recommended ANN model fitted the data as accurately as experimental results, indicating the usefulness of artificial neural networks in predicting rock properties. 相似文献
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This paper demonstrates the applicability of cognitive systems or neural networks in predicting the drillibality of rocks
and wear factor using engineering properties of rocks. Drillability of rocks is a useful guide for evaluating the suitability
of drills for different ground operations. The wear factor of different materials subsequently helps in the selection of proper
drills for different drilling operations. Different rocks were tested for Protodyakonov index, impact strength index, shore
hardness number, Schmidt hammer number, drillability and micro bit chisels for wear factor. The data obtained from the tests
were used to train and test the neural network. Results from the analysis demonstrate that cognitive systems are an effective
tool in the prediction and suitability of drilling operations. Application of these predictive models can be a useful tool
to obtain the value of these important parameters, they can save time and help to avoid the tedious process of instrumentation. 相似文献
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Accurate prediction of ore grade is essential for many basic mine operations, including mine planning and design, pit optimization,
and ore grade control. Preference is given to the neural network over other interpolation techniques for ore grade estimation
because of its ability to learn any linear or non-linear relationship between inputs and outputs. In many cases, ensembles
of neural networks have been shown, both theoretically and empirically, to outperform a single network. The performance of
an ensemble model largely depends on the accuracy and diversity of member networks. In this study, techniques of a genetic
algorithm (GA) and k-means clustering are used for the ensemble neural network modeling of a lead–zinc deposit. Two types of ensemble neural network
modeling are investigated, a resampling-based neural ensemble and a parameter-based neural ensemble. The k-means clustering is used for selecting diversified ensemble members. The GA is used for improving accuracy by calculating
ensemble weights. Results are compared with average ensemble, weighted ensemble, best individual networks, and ordinary kriging
models. It is observed that the developed method works fairly well for predicting zinc grades, but shows no significant improvement
in predicting lead grades. It is also observed that, while a resampling-based neural ensemble model performs better than the
parameter-based neural ensemble model for predicting lead grades, the parameter-based ensemble model performs better for predicting
zinc grades. 相似文献
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为提高多种典型人工神经网络应用于降水预报的精度与稳定性并做出优选,对太湖流域湖西区丹徒、丹阳、金坛、溧阳、宜兴5站的年降水量时间序列建立基于组成成分分析的人工神经网络模型,并通过平均相对误差、平均绝对误差、均方根误差及合格率4项评价指标对比分析预报效果。该模型采用Mann-Kendall法、秩和检验法、谱分析法进行组成成分分析;建立BP网络、小波神经网络、RBF网络、GRNN网络及Elman网络模拟并预测随机成分,与确定性成分叠加得年降水量预报结果。在湖西区的研究结果表明,基于组成成分分析的人工神经网络模型的拟合及预测精度高于原始人工神经网络和线性自回归模型,GRNN网络的预测精度与稳定性高于其他4类神经网络。 相似文献
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基于差异进化算法的前馈神经网络在大坝变形监测中的应用 总被引：1，自引：0，他引：1
针对当前大坝安全监测中广泛采用的回归模型欠拟合的不足,提出了基于差异进化算法的前馈神经网络模型。差异进化算法是基于种群策略的全局优化搜索算法,具有应用简单、收敛快的优点。采用该法训练的神经网络可以有效避免常规BP(back propagation)神经网络收敛于局部极小点的缺陷。将提出的方法应用于某拱坝的变形监测,通过计算表明,应用DE(differential evotntion)神经网络模型预报大坝变形的精度比常规回归模型和BP神经网络模型均有所提高。 相似文献