智能控制的电化学齿轮修行新工艺

提出了一种基于实时控制的电化学齿轮修形的新方法,作者设计并制作了电化学齿轮修形的加工装置,利用人工神经网络的方法在已知修形量及修形区域的情况下求取了加工施电规律,加工实例说明该方法是实用可行的。在实际生产中有较大的应用与推广价值。     

基于人工神经网络控制下的电化学齿轮修形新工艺

提出了一种基于实时控制的电化学齿轮修形的新方法;建立了电化学齿形修形加工去除规律的数学模型,并利用人工神经网络的方法在已知修形量及修形区域的情况下求取了加工施电规律。加工实例说明该方法是实用可行的,在实际生产中有较大的应用与推广价值。     

齿轮电化学齿端修形的研究

本文讨论了电化学齿端圆弧修形中金属蚀除厚度变化与电流密度分布规律之间的关系;对电化学齿端修形齿轮进行了测量,齿端修形量和修形长度都随电流的增大而线性增大,修形区域过渡圆滑,修形齿轮对称性好。     

齿轮修形及其实现方法研究

介绍了齿轮齿形修形和齿向修形两种修形方法及修形齿轮的加工方法。齿形修形利用一对啮合齿的综合刚度来确定最大修形量,并在求出修形长度之后确定修形曲线方程;齿向修形同时考虑接触变形和歪斜度等因素来确定鼓形齿的最大鼓形量,并根据有效接触齿宽求出最大鼓形量的中心距。由有限元接触分析,验证了修形齿轮可以减小啮合应力集中,使齿轮传动更平稳。     

内啮合珩齿啮合理论分析及试验

内啮合珩齿是硬齿面齿轮精加工的重要方法，技术难点之一是用齿轮式的金刚石修整滚轮对内珩轮进行修形，本文探讨了齿轮式金刚石修形轮的制作方法，应用空间啮合理论对内啮合珩齿修形及加工的啮合状况进行了分析，并用自制的金刚石修形轮在D-250-C内啮合珩齿机上进行了修形及加工试验。试验证明．自制的金刚石修形轮精度完全符合使用要求。     

齿轮式金刚石修形滚轮在珩齿加工中的应用

分析了齿轮式金刚石修形滚轮对珩磨轮的修形机理。用自制金刚石修形滚轮在内、外啮合珩齿机上进行了修形及珩齿加工试验 ,结果证明其修形精度完全可满足珩齿加工要求     

金刚石修磨轮修形环面蜗杆砂轮的理论分析及试验

环面蜗杆砂轮磨齿工艺是硬齿面齿轮精加工的重要方法,技术难点之一是用齿轮式的金刚石修整滚轮对蜗杆砂轮进行修形,本文探讨了齿轮式金刚石修形轮的制作方法,应用空间啮合理论对蜗杆砂轮磨齿修形及加工的啮合状况进行了分析,并用自制的金刚石修形轮在NZA蜗杆砂轮磨齿机上进行了修形及加工试验,试验证明,自制的金刚石修整轮精度完全符合使用要求。     

参数化修形的直齿轮副啮合性能研究

齿轮的参数化修形设计可以快速、有效地改善轮齿啮合性能。在UG中建立全参数化的直齿圆柱齿轮模型,针对某一工况进行参数化的齿廓与螺旋线联合修形,在Hypermesh和Workbench中对其进行有限元仿真,获得不同修形情况的齿轮啮合特性。对比仿真结果可知,针对同一工况,选择合适的修形方式、修形参数值可以明显改善啮合效果。同时,通过齿轮台架试验,对比修形前后的齿轮振动数据,发现经过修形的齿轮振动特性明显优于未修形齿轮。结合仿真与试验结果分析可得,螺旋线鼓形修形对于齿面偏载有较好的改善效果;齿廓修形能明显改善轮齿间的载荷分配;综合齿廓与螺旋线修形可以快速得到最佳修形方式及修形量,缩短研发周期。研究为实际工程提供了重要理论参考。     

齿向修形齿轮的数控加工技术

研究了在滚齿机和蜗杆砂轮磨齿机上加工齿向修形齿轮时刀具的运动轨迹，采用基于参数方程的方法，导出了加工小锥度修形、鼓形齿修形、锥度鼓形齿修形等多种齿向修形齿轮时刀具中心运动轨迹的计算公式可直接用行数控插补。     

基于空间点阵的齿轮精确修形方法

齿轮修形是改善啮合状态、提高传动精度和效率的一种重要工程技术,但现有的齿轮修形方式和工具性软件难以确保修形的齿面几何质量和效率。因此,提出了一种空间点阵的齿轮修形方法。首先,根据修形精度要求,将齿轮的齿面离散为规则空间点阵,并将修形量映射到对应的空间点上,再将调整后的空间点阵构建成规则的二次B样条曲线;然后,通过优化网格曲线,得到修形后的齿面,并自适应更新整个齿轮三维模型的齿面;最后,将该方法无缝集成到三维设计软件NX平台上,开发了齿轮精确修形设计系统。实例证明,该方法通过修形位置和修形量的双重控制,实现了齿面的精确修形,获得了高质量齿廓曲面,在保证精度的同时提高了齿轮修形的高效性和实用性。     

Solving the Control Problem for Electrochemical Geartooth-Profile Modification Using an Artificial Neural Network

The paper describes a new method for electrochemical tooth-profile modification based on real-time control, and establishes a mathematical model of the electrochemical tooth-profile modification process. The law is found for processing electric current using the artificial neural network, when the tooth-profile modification requirements are given. The results from an actual gear demonstrate that this technology is feasible, and has potential application in production.     

机器人用高精度RV减速器中摆线轮的优化新齿形

根据机器人用高精度RV减速器对运动精度、回差、扭转刚度、传动效率和承载能力等主要技术指标的要求,提出了机器人用RV减速器中摆线轮齿形修形应具备的条件,并在此基础上,提出了摆线轮齿形的优化设计数学模型.所提出的摆线轮优化新齿形经样机试验证明是正确和实用的.     

Development of multi-objective optimization models for electrochemical machining process

Owing to the complexity of electrochemical machining (ECM), it is very difficult to determine optimal cutting parameters for improving cutting performance. Hence, optimization of operating parameters is an important step in machining, particularly for unconventional machining procedures like ECM. A suitable selection of machining parameters for the ECM process relies heavily on the operator’s technologies and experience because of their numerous and diverse range. Machining parameters provided by the machine tool builder cannot meet the operator’s requirements. Since for an arbitrary desired machining time for a particular job, they do not provide the optimal conditions. To solve this task, multiple regression model and ANN model are developed as efficient approaches to determine the optimal machining parameters in ECM. In this paper, current, voltage, flow rate and gap are considered as machining parameters and metal removal rate and surface roughness are the objectives. Then by applying grey relational analysis, we calculate the grey grade for representing multi-objective model. Multiple regression model and ANN model have been developed to map the relationship between process parameters and objectives in terms of grade. The experimental data are divided into training and testing data. The predicted grade is found and then the percentage deviation between the experimental grade and predicted grade is calculated for each model. The average percentage deviations for the training data of the linear regression model, logarithmic transformation model, excluding interaction terms and ANN model, are 12.7, 25.6 and 3.03, respectively. The average percentage deviations for the testing data of the three models are 9.83, 26.8 and 2.67. While examining the average percentage deviations of three models, ANN is having less percentage deviation. So ANN is considered as the best prediction model. Based on the testing results of the artificial neural network, the operating parameters are optimized. Finally, ANOVA is used to identify the significance of multiple regression model and ANN model.     

Selective and localized embrittlement of metal by cathodic hydrogenation utilizing electrochemical jet

Electrochemical jet processing (EJP) is an easy-to-implement technology for creating complex microstructures based on anodic dissolution without requiring specially designed cathodes. In this study, a new electrolyte-jet-based surface modification method based on the principle of cathodic hydrogenation, namely, electrochemical jet hydrogenation (EJH), was proposed for the first time to selectively modify the material in aspects of its brittleness. In this method, a workpiece is innovatively set as the cathode, and hydrogen evolution occurs locally on the cathode surface within the jet impinging area, leading to a localized H-treatment of the cathode material. The hydrogen-material interaction can alter the surface material property and result in localized surface modification, for example, material ductile-brittle transition by hydrogen embrittlement (HE). In this research, the proposed method was validated on niobium metal. According to the results, evident localized embrittlement was achieved, and the degree of embrittlement was precisely controlled by adjusting the electrochemical parameters mainly including current density and processing time. As a selective surface modification method, EJH can be applied as an assistive technology in hybrid machining of difficult-to-machine superalloys where localized surface modification of ductile-brittle transition is expected.     

A study on using scanning acoustic microscopy and neural network techniques to evaluate the quality of resistance spot welding

This paper shows that the quality of resistance spot welds can be evaluated using scanning acoustic microscopy (SAM). Two-layered coated spot-welded samples are investigated utilising a wide-field short-pulse scanning acoustic microscope with operation frequencies of 25, 50 and 100 MHz. Geometrical parameters, e.g. nugget area, maximum axis of nugget, and minimum axis of nugget, are acquired from C-scan images of weld nuggets using mathematical morphology techniques. These parameters serve as inputs for an artificial neural network (ANN) model to evaluate the quality of spot welds. The output of the model during the training process comprises the results of nugget peeling tests and expert opinions. The ANN can provide suggestions on weld quality with a higher than 95% correctness. A JAVA computer program is developed for image processing, ANN training, and ANN testing. With this model, the computer program can render the quality of spot welds that are close to those achieved using off-line destructive method.     

A hybrid model using genetic algorithm and neural network for process parameters optimization in NC camshaft grinding

Camshaft grinding is more complex comparing with the ordinary cylindrical grinding. Since its quality is mostly influenced by more factors, how to select process parameters quickly and accurately becomes the key to improve its quality and processing efficiency. In this paper, a hybrid artificial neural network (ANN) and genetic algorithm (GA) model is proposed to optimize the process parameters. In this method, a BP neural network model is developed to map the complex nonlinear relationship between process parameters and processing requirements, and a GA is used in order to improve the accuracy and speed based on the ANN model. The results show that the hybrid ANN/GA model is an effective tool for the process parameters optimization in NC camshaft grinding.     

齿轮修形技术及工艺的发展

对齿轮修形的原理和方法进行了综合论述，讨论了齿轮修形技术的新发展，并针对传统机械修形加工成本高、工艺适应性差的缺点，论述了齿轮电化学修形新工艺，及其研究应用价值。     

阶梯孔数控电解镗孔加工的阴极设计及试验研究

NC electrochemical boring method was adopted to solve the processing problems of stepped inner holes with small inlet,large inner cavity,and large deep diameter ratio.According to the structural characteristics of stepped holes,cathodes with different spiral outlets,different rotating angles,and different taper of shunt blocks were designed.The flow fields were analyzed by FLUENT,and the results show that the cathodes with 24° conical angle,360° rotating angle,and double spiral outlets meet the design requirements.Orthogonal tests were carried out to study the influences of processing parameters on removal amount and processing clearance.Main size of the cathode is determined by the testing results,and the problems of uneven wall thickness of the stepped hole are solved by correcting the tapers of the cathodes.By the proposed method,errors of the stepped hole are small,and quality of processing surfaces is good,which meet the production requirements.     

鱼雷并行测试系统设计

分析了并行测试技术的实现方式,采用硬件实现方式对现有的鱼雷自动测试系统进行并行测试化改造,提出了改造设计原则,建立了鱼雷并行测试系统软、硬件体系结构。改造后的并行测试系统大大缩短了鱼雷测试时间,提高了测试效率。     

基于特征和知识的车身点焊智能CAPP方法研究

建立了车身零部件面向装配的特征模型,提出了装配过程中“特征演化”的概念,以简化零部件特征的定义;研究了点焊工艺知识结构,包括知识的总体结构、工艺事实和工艺条件表达,并面向知识匹配过程对知识结构进行了优化;分析了基于零部件特征和工艺知识的推理技术,包括规则匹配的算法、冲突消解的策略和产生式推理方法,采用线性模型和神经网络模型对工艺方法与工艺条件之间的映射关系进行了模拟。模拟结果表明,采用提出的方法能取得与实际相符的效果。