Clay-shoveler's骨折合并颈髓损伤的诊断与治疗

目的探讨Clay-shoveler's骨折合并颈椎脊髓损伤的临床表现、影像学特点、诊断及治疗方法。方法回顾性分析2005年4月至2011年3月收治的8例Clay-shoveler's骨折合并颈椎脊髓损伤患者的临床资料并复习相关文献,总结该骨折合并颈髓损伤的特点。结果所有患者术前均有颈胸段疼痛,特点是卧床休息时疼痛明显减轻或消失,坐或行走时疼痛复发。X线或CT检查可见颈胸段棘突骨折。MRI显示颈椎间盘突出压迫相应节段脊髓。术后患者疼痛均明显减轻或消失,神经损伤症状明显好转。结论虽然Clay-shoveler's骨折发病率较低,但是患者可合并出现颈椎脊髓或神经根损伤的情况。若保守治疗无效常需要手术干预,颈后路棘突固定重建术是较好的治疗方法之一。根据患者的临床表现和影像学特点,决定是否联合颈前路减压植骨融合内固定术。

A comparison of strategic mine planning approaches for in-pit crushing and conveying, and truck/shovel systems

In a global environment where energy and labor are becoming increasingly expensive, continuous mining systems such as In-Pit Crushing and Conveying(IPCC) systems have been advanced as offering a real alternative to conventional truck haulage systems. The implementation of IPCC systems in hard rock operations in open pit mines however requires different and more comprehensive planning approaches in order to adequately reflect the practical aspects associated with these. This paper investigates the impact that these approaches may have on the implementation of IPCC systems on a basic metalliferous deposit amenable to open pit exploitation. A strategic life of mine plan to provide numerous economic indicators for each approach is analyzed and compared to traditional truck haulage systems. The mine planning and evaluation process highlights the increased overall resource recovery that may accompany the use of IPCC systems. This investigation also provides insights into the issues associated with IPCC and the scale and type of operation and orebody that is likely to provide a feasible alternative to truck haulage.

Novel Hybrid Physics‑Informed Deep Neural Network for Dynamic Load Prediction of Electric Cable Shovel

Electric cable shovel(ECS)is a complex production equipment,which is widely utilized in open-pit mines.Rational valuations of load is the foundation for the development of intelligent or unmanned ECS,since it directly influences the planning of digging trajectories and energy consumption.Load prediction of ECS mainly consists of two types of methods:physics-based modeling and data-driven methods.The former approach is based on known physical laws,usually,it is necessarily approximations of reality due to incomplete knowledge of certain processes,which introduces bias.The latter captures features/patterns from data in an end-to-end manner without dwelling on domain expertise but requires a large amount of accurately labeled data to achieve generalization,which introduces variance.In addition,some parts of load are non-observable and latent,which cannot be measured from actual system sensing,so they can’t be predicted by data-driven methods.Herein,an innovative hybrid physics-informed deep neural network(HPINN)architecture,which combines physics-based models and data-driven methods to predict dynamic load of ECS,is presented.In the proposed framework,some parts of the theoretical model are incorporated,while capturing the difficult-to-model part by training a highly expressive approximator with data.Prior physics knowledge,such as Lagrangian mechanics and the conservation of energy,is considered extra constraints,and embedded in the overall loss function to enforce model training in a feasible solution space.The satisfactory performance of the proposed framework is verified through both synthetic and actual measurement dataset.

Impulse force reductions and their effects on WBV exposures in high impact shovel loading operations

When shovels load the dump trucks with over 100-ton passes under gravity dumping conditions, they will create a large impact force on the dump truck body which generates high frequency shock waves which expose the operators to whole body vibrations （WBV）. The main cause of such truck vibrations is the large impact force due to the gravity dumping of large tonnage passes. Therefore a rigorous mathematical model has been developed for the impact force containing all the necessary factors upon which it depends. Latter, a thorough analysis shows that percentage reduction of 7.19%, 9.40%, 13.27%, 14.8%, 17.30% and 18.13% can he achieved by reducing the dumping distance to 6.33 m, 6.0 m, 5.5 m, 5.33 m, 5.0 m and 4.9 m, respectively, as compared to when the dumping distance was 7.33 m. Even more reduction in the magnitude of impact force can he observed if the shovel pass gets divided into more than two sub-passes. Therefore, these models can he used to figure out the number of sub-passes into which a single ore pass can he divided and/or the extent to which the dumping distance can he reduced which would reduce the impact force significantly enough to obtain safer yet economic operations.

Evaluation of rope shovel operators in surface coal mining using a Multi-Attribute Decision-Making model

Rope shovels are used to dig and load materials in surface mines. One of the main factors that influence the production rate and energy consumption of rope shovels is the performance of the operator. This paper presents a method for evaluating rope shovel operators using the Multi-Attribute Decision-Making （MADM） model. Data used in this research were collected from an operating surface coal mine in the southern United States. The MADM model consists of attributes, their weights of importance, and alter- natives. Shovel operators are considered the alternatives, The energy consumption model was developed with multiple regression analysis, and its variables were included in the MADM model as attributes. Preferences with respect to min/max of the defined attributes were obtained with multi-objective opti- mization. Multi-objective optimization was conducted with the overall goal of minimizing energy con- sumption and maximizing production rate. Weights of importance of the attributes were determined by the Analytical Hierarchy Process （AHP）, The overall evaluation of operators was performed by one of the MADM models, i.e., PROMETHEE If. The research results presented here may be used by mining professionals to held evaluate the performance of rode shovel operators in surface mining.

Research on Energy Efficiency Characteristics of Mining Shovel Hoisting and Slewing System Driven by Hydraulic-Electric Hybrid System

Mining shovel is a crucial piece of equipment for high-efficiency production in open-pit mining and stands as one of the largest energy consumption sources in mining.However,substantial energy waste occurs during the descent of the hoisting system or the deceleration of the slewing platform.To reduce the energy loss,an innovative hydrau-lic-electric hybrid drive system is proposed,in which a hydraulic pump/motor connected with an accumulator is added to assist the electric motor to drive the hoisting system or slewing platform,recycling kinetic and potential energy.The utilization of the kinetic and potential energy reduces the energy loss and installed power of the min-ing shovel.Meanwhile,the reliability of the mining shovel pure electric drive system also can be increased.In this paper,the hydraulic-electric hybrid driving principle is introduced,a small-scale testbed is set up to verify the feasibil-ity of the system,and a co-simulation model of the proposed system is established to clarify the system operation and energy characteristics.The test and simulation results show that,by adopting the proposed system,compared with the traditional purely electric driving system,the peak power and energy consumption of the hoisting electric motor are reduced by 36.7%and 29.7%,respectively.Similarly,the slewing electric motor experiences a significant decrease in peak power by 86.9%and a reduction in energy consumption by 59.4%.The proposed system expands the application area of the hydraulic electric hybrid drive system and provides a reference for its application in over-sized and super heavy equipment.

Underwater hydraulic shock shovel control system

The control system determines the effectiveness of an underwater hydraulic shock shovel. This paper begins by analyzing the working principles of these shovels and explains the importance of their control systems. A new type of control system’s mathematical model was built and analyzed according to those principles. Since the initial control system’s response time could not fulfill the design requirements, a PID controller was added to the control system. System response time was still slower than required, so a neural network was added to nonlinearly regulate the proportional element, integral element and derivative element coefficients of the PID controller. After these improvements to the control system, system parameters fulfilled the design requirements. The working performance of electrically-controlled parts such as the rapidly moving high speed switch valve is largely determined by the control system. Normal control methods generally can’t satisfy a shovel’s requirements, so advanced and normal control methods were combined to improve the control system, bringing good results.

Simulation Analysis of a New Type of Bionic Digging Shovel Based on DEM

The performance of a digging shovel mainly depends on the style of the shovel, while the conventional experiment methods always suffer from the problems of high lost and long period. Aiming at these problems and the characteristic that soil is composed of countless small particles, dynamic simulation analysis was performed on the resistance to a bionic digging shovel and crushing rate of the soil during the normal working process of the bionic digging shovel by EDEM through numerical simulation, calculation and comparison. The results showed that compared with the ordinary shovel, the average drag-reducing rate in the X direction was 10.41%, and the average drag-reducing rate in the Y direction was 16.28%, and the soil crushing rate was improved by 2.67%. Therefore, the bionic digging shovel has certain superiority and extension value in structure and performance. Moreover, this analysis case fully demonstrates the unique advantage of DEM method and its generalizability, and provides certain reference for similar studies.

Stress Computation of Roadheader Shovel's by using the Finite Element Method

Shovel board is an important component of the roadheader. Shovel board participate in complex and changeable tunneling work, and always under high load. In this paper, research the working condition of shovel board and analyze the shovel board by Inventor, and reach the stress and strain distribution in the shovel board bear the maximum force when working. Have some guiding on improving the shovel board structure design.

Adaptive neural network tracking control for unmanned electric shovel intelligent excavation system

This study proposes an adaptive control strategy for unmanned mining shovel digging trajectory tracking based on radial basis function neural network(RBFNN)and a class of unmanned mining shovel time-varying systems with model uncertainty and external disturbances.A new set of Lagrangian dynamics differential equations is reconstructed by utilizing the kinematic model of the electric shovel and considering external disturbances along with modeling uncertainties.This approach lays the groundwork for subsequent adaptive controllers.The proposed controller is designed to regulate the position errors of the unmanned mining electric shovel system,which is characterized by a complex structure,high load,large size,and strong coupling.It takes the deviation values and their derivatives of the lifting and pushing movements as inputs and adjusts the output torque to converge the bucket position to the desired trajectory.The controller utilizes the RBFNN in the control law to compensate for uncertainties in this type of system with large disturbances and inertia.This compensation helps eliminate the impact of external disturbances and modeling uncertainties on the unmanned mining electric shovel’s ability to follow the excavation trajectory.The consistent boundedness of the closed-loop system’s ultimate limits is proven through Lyapunov stability theory.Finally,the effectiveness of the proposed solution is validated through simulation experiments.

三七土壤参数测定及挖掘铲优化研究

针对三七挖掘过程中挖掘铲所受阻力过大的问题,对传统的平面三七挖掘铲进行了优化。首先,对三七土壤参数进行了试验测定,并通过离散元仿真得到了土壤的堆积角,经试验验证所选土壤参数的准确性;其次,采用离散元软件EDEM建立了挖掘铲的仿真模型,计算了其在挖掘过程中所受到的阻力,通过与平面铲对比验证了该结构可以降低挖掘铲在行驶过程中所受到的阻力;再次,以行进过程中所受阻力为指标对挖掘铲进行优化,分析挖掘铲的铲面弧度、铲尖角度、铲的间距和入土角度等参数对挖掘阻力的影响,结果表明:铲面弧度为116°、铲尖角度为120°、铲的间距为86 mm、入土角度为12°时,挖掘阻力最小,相较于传统挖掘铲阻力降低了23.6%;最后,对优化后的挖掘铲进行了试验验证,结果表明:平均误差为2.78%,在5%以内,满足设计需求。研究结果可为块茎类农作物挖掘铲的设计提供参考。

Toward autonomous mining:design and development of an unmanned electric shovel via point cloud-based optimal trajectory planning

With the proposal of intelligent mines,unmanned mining has become a research hotspot in recent years.In the field of autonomous excavation,environmental perception and excavation trajectory planning are two key issues because they have considerable influences on operation performance.In this study,an unmanned electric shovel(UES)is developed,and key robotization processes consisting of environment modeling and optimal excavation trajectory planning are presented.Initially,the point cloud of the material surface is collected and reconstructed by polynomial response surface(PRS)method.Then,by establishing the dynamical model of the UES,a point to point(PTP)excavation trajectory planning method is developed to improve both the mining efficiency and fill factor and to reduce the energy consumption.Based on optimal trajectory command,the UES performs autonomous excavation.The experimental results show that the proposed surface reconstruction method can accurately represent the material surface.On the basis of reconstructed surface,the PTP trajectory planning method rapidly obtains a reasonable mining trajectory with high fill factor and mining efficiency.Compared with the common excavation trajectory planning approaches,the proposed method tends to be more capable in terms of mining time and energy consumption,ensuring high-performance excavation of the UES in practical mining environment.

Multiobjective trajectory optimization of intelligent electro-hydraulic shovel

Multiobjective trajectory planning is still face challenges due to certain practical requirements and mul-tiple contradicting objectives optimized simultaneously.In this paper,a multiobjective trajectory optimization approach that sets energy consumption,execution time,and excavation volume as the objective functions is presented for the electro-hydraulic shovel(EHS).The proposed cubic polynomial S-curve is employed to plan the crowd and hoist speed of EHS.Then,a novel hybrid constrained multiobjective evolutionary algorithm based on decomposition is proposed to deal with this constrained multiobjective optimization problem.The normalization of objectives is introduced to minimize the unfavorable effect of orders of magnitude.A novel hybrid constraint handling approach based onε-constraint and the adaptive penalty function method is utilized to discover infeasible solution information and improve population diversity.Finally,the entropy weight technique for order preference by similarity to an ideal solution method is used to select the most satisfied solution from the Pareto optimal set.The performance of the proposed strategy is validated and analyzed by a series of simulation and experimental studies.Results show that the proposed approach can provide the high-quality Pareto optimal solutions and outperforms other trajectory optimization schemes investigated in this article.

煤矿湿式混凝土喷浆系统的研发与应用

针对煤矿井下巷道混凝土干喷作业存在的作业环境粉尘大、工人劳动强度高、固化强度低、喷射回弹率高等问题,研发了一种新型湿式混凝土喷浆系统。该系统由铲料搅拌机、输送泵、喷浆机器人等主要设备组成。完成了样机制造,并进行了应用试验,试验结果达到了预期目标。

深松铲对砖红壤扰动行为影响的仿真与试验

针对香蕉地深松作业相关研究较少,深松铲-土壤耦合机理尚未明确等问题,结合海南热区香蕉地砖红壤土的物理特性,利用离散元虚拟仿真试验,堆积生成了耕作层、犁底层和心土层3层土壤模型,建立了滑切深松铲-土壤耕作模型,并通过对比虚拟仿真试验,研究了滑式深松铲和国标深松铲对海南香蕉地土壤的扰动情况。结果表明:在相同的作业条件下,滑式深松铲作用下的土壤颗粒在x、y和z方向的最大运动速度均明显大于国标深松铲对土壤颗粒的作用,且滑式深松铲的扰动轮廓要明显大于国标深松铲作业的扰动轮廓,同时滑式深松铲作用下土壤蓬松度和土壤扰动系数均大于国标深松铲作用产生的深松效果,进一步验证了所设计的滑式深松铲更适用于海南香蕉地的深松作业。

基于Rocky DEM的松散润叶筒内抄板作用的模拟与优化

为深入研究松散润叶筒内叶片在抄板上的掉落过程,提升叶片的耐加工性,利用三维建模软件建立松散润叶筒三维模型,通过Rocky 2021 R2离散元仿真软件创建薄片柔性叶片进行数值模拟,对叶片在松散润叶筒内抄板上的运动进行可视化计算。根据抄板上叶片的持料量百分比曲线和落料速率曲线,将叶片的掉落过程分为两个阶段。结果表明:片状颗粒的受力区别于刚性球体间的点对点接触受力,叶片形状不可忽略;松散润叶筒转速为15 r/min时,叶片在筒体底部分布较少,筒体上半部分分布较多;抄板安装角度为80°时,板上持料量增多,抄板倾角较合理,增温增湿效果最优;松散润叶筒倾角对叶片在抄板上掉落的轴向速度影响显著。通过仿真得出叶片在抄板上掉落过程的优化参数,有助于松散润叶过程调控,指导实际工业生产。

压雪机雪铲运动仿真及分析

基于D-H矩阵原理建立压雪机雪铲运动轨迹的数学模型,文中分析了雪铲运动过程中雪铲齿尖的位移、速度、加速度与空间位置的运动关系;建立雪铲三维模型,然后将简化处理过的三维模型导入ADAMS仿真软件中,在ADAMS中增加约束和驱动等前处理工作;最后,进行运动仿真,得出雪铲齿尖的位移、速度、加速度动态曲线及雪铲轨迹图,并验证了雪铲运动学模型的正确性,对压雪机雪铲的设计和分析具有普遍适用的意义并提供了参考。

黏重黑土条件下马铃薯收获机挖掘装置设计与试验

针对东北地区黏重黑土条件下马铃薯收获机工作阻力大、挖掘铲寿命短、挖掘质量难以保证等问题,将双行升运链式马铃薯收获机挖掘装置改进并设计一种分离式防堵马铃薯挖掘装置。采用理论分析研究方法,解析马铃薯收获机关键部件挖掘铲挖掘过程,探明影响马铃薯收获机挖掘铲工作阻力的主要影响因素;结合黏重黑土条件下马铃薯收获的农艺要求,以行驶速度、铲面铲宽和铲面倾斜角度为试验因素,以挖掘铲最大工作阻力和土壤最大压缩力为试验指标,进行多因素正交仿真试验,设计得出,挖掘铲在保证良好碎土效果且不造成马铃薯损伤的最佳参数,即行驶速度1.11 m·s^(-1),铲面铲宽150 mm,铲面倾斜角度30°时,挖掘铲最大工作阻力为566 N,土壤最大压缩力为152 N。田间验证试验表明,各项指标达到要求且均优于改进前整片式挖掘铲马铃薯收获机。

基于数值模拟的钛合金锥形件激光辅助铲旋成形研究

针对中部带法兰锥形件采用传统“锻造+机加工”制备时材料利用率低、强度及韧性不足的问题,提出采用激光辅助铲旋成形工艺来实现钛合金中部带法兰锥形件的完整近净成形。根据铲旋成形原理,选取合适的激光热源模型,并基于Simufact Forming软件平台建立激光辅助铲旋有限元模型,研究了铲旋成形过程中的中部法兰变形区温度、变形行为及等效塑性应力和等效塑性应变分布规律。结果表明,采用坯料整体预热+激光辅助补热的方式,可实现坯料基体表层温度高、内层温度低,既有利于表面金属材料铲旋成形出法兰,又可避免锥壁翘起;铲旋成形时变形区材料轴向流动较多,径向流动较少,法兰形状呈“倒水滴”状;旋轮接触区等效塑性应力值较大,但法兰主体部分等效塑性应力值较小且分布均匀;基体主要变形区等效塑性应变值最大,达9~11,沿厚度方向逐渐减小,具有局部大塑性变形且变形不均匀的特点。

基于3D打印关键技术的深松铲结构设计

以深松铲为研究对象,根据土壤耕作过程需求建立深松铲铲柄轮廓曲线,同时对深松铲结构进行详细设计,即铲尖厚度为6mm、铲尖宽度为35mm、铲尖入土角度为23°、柄厚度为25mm、宽度为55mm。对深松铲耕作过程阻力进行分析,建立松土阻力计算方程,同时采用3D打印技术对深松铲进行逆向工程快速成型。松土过程仿真分析结果表明:作业过程行进速度直接影响耕作过程中深松铲阻力,当行进速度为4、4.5、5km/h时,深松铲所受阻力均值分别为410、490、575N。