名词解释

<正>无心外圆磨床无心外圆磨床是一种生产率很高的精加工机床。无心外圆磨床进行磨削时,工件不是支承在顶尖上或夹持在卡盘中,而是直接置于砂轮和导轮之间的托板上,以工件自身外圆为定准基准,其中心略高于砂轮和导轮的中心连线。磨削时,导轮速度与砂轮速度相比较低,由于工件和导轮之间的摩擦较大,所以工件接近于导轮转速回转。从而在砂轮工件     

干式气缸套外圆无心磨削加工浅析

外圆无心磨削是工件不定心的磨削,是一种适应大批量生产的高效率磨削方法。在干式气缸套生产线上,采用外圆无心磨削来加工气缸套的外圆直径也是一种比较先进的工艺措施。本文根据多年的生产经验和实际情况,对干式气缸套外圆无心磨削的加工原理,外圆直径的成圆过程,导轮曲面及修整和无心磨削加工的产品质量与控制等方面透行分析探讨。     

在外圆磨床上进行无心磨削

有些中小型工厂,如缺少无心磨床,可在一般外圓磨床上附加一套导轮机构和托架机构,同样能实现无心磨削。机床的改装见图1所示。导轮的直径与外圆磨床的磨削轮直径及工件直径有关,一般取导轮的直径为磨削轮直径的二分之一(Φ190～Φ210mm),其宽度与磨削轮的宽度相等或稍宽。导轮可用无心磨削的废旧导轮或标准导轮改制。     

柱面贯穿无心磨削疵病的消除

无心外圆磨床是一种高效率、高精度的外圆加工设备。采用柱面贯穿磨削法时更显出其优越性。但是,如果操作调整不慎,工件将会出现多种形状误差以致造成废品。本文针对实际工作中出现的疵病现象、原因,提出简明的计算方法以便有利于消除疵病。一、工件纵进给速度太快或太慢无心磨床磨削时,工件借助托板在磨轮与导轮之间转动。由于磨轮和导轮轴线有夹角(简称幅角θ,一般取θ=0.5°～5°),所以,导轮在磨削过程     

无心磨削滚子用组合式托板的改进

轴承滚子的外圆磨削主要是在无心外圆磨床上进行的。磨削时工件放在砂轮和导轮之间,用托板支撑进行磨削,由于托板的几何形状属于长薄板,机械加工和热处理容易变形,通过研究提出了解决方案。     

磨圆锥销的限位装置

在无心磨床上磨削圆锥销时,一般的加工方法是将导轮在水平位置上旋转一个角度,该角度的大小与圆锥销的锥角α的1/2相等。导轮的轴线与磨削轮的轴线形成一个锥角(导轮外圆与磨削轮外圆之间形成同样的锥角)。在磨削过程中,工件放入磨削区域内的位置必须固定,否则工件的尺寸无法保证。通常,工件放入磨削区域的位置是凭着人的感觉确定的,这样就产生一些误差。如果工件     

M10400大型无心磨床自动送接料装置

Ｍ１０４００大型无心磨床自动送接料装置岳阳大学李荐名Ｍ１０４００磨床是我国自行设计制造的大型无心外圆磨床。该机床自重２２ｔ，占地面积达１５ｍ２。砂轮采用宽砂轮磨削，最大线速度为２９ｍ／ｓ，导轮实行无级调速。加工工件外径为１００～４００ｍｍ。它适合于大...     

无心磨削工件质量的在线控制方法

无心磨削工件质量在很大程度上取决于工艺本身。在无心磨削过程中零件的中心线是漂移的。工件上的不规则部分与刀板或导轮接触时会产生这种现象，从而导致工件的圆度误差。文章阐述无心磨削工艺的计算机模拟试验工作，它表明有可能通过导轮的运动来提高被磨工件的圆度。所列程序提供了可保证无心磨削质量的新方法。     

干式气缸套外圆无心磨削

干式气缸套外圆直径是在外圆无心磨床上加工的。在磨削过程中,将干式气缸套(图1)置于砂轮、导轮和托板三者之间。干式气缸套中心不定,其位置变化大小取决于粗磨前的原始误差。经过无心磨削后气缸套的外圆直径精度可达IT5～IT7级。圆度误差可达0.005～0.015mm,表面粗糙度值可达R_α0.8～0.2μm。     

无心外圆砂带抛光机

无心外圆砂带抛光机浙江机械工业学校（３１００１２）毛全有一、结构无心外圆砂带抛光机采用了无心接触磨削形式，如１示。ＬＮＩ互导轮２．工件３．支承轮４．托析５．接触比６．砂带７．从动轮“无心外圆砂带她光机主要由磨头部件、导轮部件、托板部件、机应组成。磨头...     

蜗杆砂轮磨齿加工参数优化

以蜗杆砂轮磨削加工20CrMnTi齿轮为研究对象,选择均匀设计试验法,研究磨削参数（砂轮线速度vs、砂轮沿齿轮轴向进给速度vw、磨削厚度ap）对齿面粗糙度的影响。采用二级逐步回归方法建立磨削参数与齿面粗糙度的回归模型,构建了以加工效率、齿面粗糙度为多目标的优化模型,采用粒子群优化算法对磨削参数进行了优化。试验结果表明,使用优化后的磨削参数加工可以提高加工效率、减小齿面粗糙度。     

圆锥滚子球基面磨削力模型及实验研究

为实现圆锥滚子球基面优质高效的磨削加工,以滚子球基面磨削原理为基础,建立圆锥滚子球基面磨削力的数学模型,提出了基于静刚度和功率来验证法向和切向磨削力的方法,系统地分析了夹持圆锥滚子的导轮盘转速差、圆锥滚子自转线速度和砂轮线速度对磨削力的影响,同时基于球基面磨削力数学模型优化磨削工艺参数。研究结果表明：所提出的磨削力模型的计算值与实验结果一致;导轮盘转速差降低、圆锥滚子自转线速度降低和砂轮线速度升高都会减小磨削力;优化后的磨削工艺参数可有效降低圆锥滚子球基面半径散差,从而再次验证了球基面磨削力模型的正确性。     

Multi-objective optimisation of surface grinding operations using scatter search approach

A scatter search based optimisation approach is developed to optimise the grinding parameters of wheel speed, work piece speed, depth of dressing and lead of dressing using a multi-objective function model with a weighted approach for the surface grinding process. The production cost and production rate are evaluated for the optimal grinding conditions, subject to the constraints such as thermal damage, machine tool stiffness, wheel wear parameters and surface finish. The results are compared with the results obtained by the ants-colony algorithm, genetic algorithm and quadratic programming techniques.     

Multi-objective optimisation of surface grinding operations using scatter search approach

A scatter search based optimisation approach is developed to optimise the grinding parameters of wheel speed, work piece speed, depth of dressing and lead of dressing using a multi-objective function model with a weighted approach for the surface grinding process. The production cost and production rate are evaluated for the optimal grinding conditions, subject to the constraints such as thermal damage, machine tool stiffness, wheel wear parameters and surface finish. The results are compared with the results obtained by the ants-colony algorithm, genetic algorithm and quadratic programming techniques.     

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.     

Constrained grinding optimization for time,cost, and surface roughness using NSGA-II

Selection of parameters in machining process significantly affects quality, productivity, and cost of a component. This paper presents an optimization procedure to determine the optimal values of wheel speed, workpiece speed, and depth of cut in a grinding process considering certain grinding conditions. Experimental studies have been carried out to obtain optimum conditions. Mathematical models have also been developed for estimating the surface roughness based on experimental investigations. A non-dominated sorting genetic algorithm (NSGA II) is then used to solve this multi-objective optimization problem. The objectives under investigation in this study are surface finish, total grinding time, and production cost subjected to the constraints of production rate and wheel wear parameters. The Pareto-optimal fronts provide a wide range of trade-off operating conditions which an appropriate operating point can be selected by a decision maker. The results show the proposed algorithm demonstrates applicability of machining optimization considering conflicting objectives.     

Multi-objective optimization for surface grinding process using a hybrid particle swarm optimization algorithm

Optimization for the surface grinding process is a problem with high complexity and nonlinearity. Hence, evolutionary algorithms are needed to apply to get the optimum solution of the problem instead of the traditional optimization algorithms. In this work, a hybrid particle swarm optimization (HPSO) algorithm which combines the dynamic neighborhood particle swarm optimization (DN-PSO) algorithm with the strategy of mutation considering constraints is presented to handle multi-objective optimization for surface grinding process. Such four process parameters as wheel speed, workpiece speed, depth of dressing, and lead of dressing are considered as the variables for optimization, and the following three objectives such as production cost, production rate, and surface roughness are used in a multi-objective function model with a weighted approach. Meanwhile, the constraints of thermal damage, wheel wear, and machine tool stiffness are considered. Computational experiments are conducted on cases of both rough grinding and finish grinding, and comparison results with the previously published results obtained by using other optimization techniques shows the efficiency of the proposed algorithm.     

Genetic Algorithm (GA) for Multivariable Surface Grinding Process Optimisation Using a Multi-objective Function Model

A genetic algorithm (GA) based optimisation procedure has been developed to optimise the surface grinding process using a multi-objective function model. The following ten process variables are considered in this work: wheel speed, workpiece speed, depth of dressing, lead of dressing, cross-feedrate, wheel diameter, wheel width, grinding ratio, wheel bond percentage, and grain size. The procedure evaluates the production cost and production rate for the optimum grinding conditions, subject to constraints such as thermal damage, wheel-wear parameters, machine-tool stiffness and surface finish. A worked example is used to illustrate how this procedure can be used to produce optimum production rate, low production cost, and fine surface quality for the surface grinding process.     

Investigation of the grinding temperature and energy partition during cylindrical grinding

In this study, the distribution of temperature and energy under the process parameter conditions and thermal physical parameters are investigated using a physics-based model via the finite element modeling (FEM) simulation and experimental validation during cylindrical grinding. A cylindrical grinding model is modeled to simulate the chip removal behavior in the grinding process and to measure the workpiece and chip temperatures by refining the temperature field. Workpiece speed affects the energy partition into chip more obviously than other grinding parameters. Reasonable selection of grinding parameters greatly reduces the energy partition into the workpiece from 80% to 50–30% or even lower. This study offers a comprehensive understanding of heating mechanisms during grinding and thus is very beneficial for process optimization.     

Multi-criteria end milling parameters optimization of AISI D2 steel using genetic algorithm

This paper focuses on using multi-criteria optimization approach in the end milling machining process of AISI D2 steel. It aims to minimize the cost caused by a poor surface roughness and the electrical energy consumption during machining. A multi-objective cost function was derived based on the energy consumption during machining, and the extra machining needed to improve the surface finish. Three machining parameters have been used to derive the cost function: feed, speed, and depth of cut. Regression analysis was used to model the surface roughness and energy consumption, and the cost function was optimized using a genetic algorithm. The optimal solutions for the feed and speed are found and presented in graphs as functions of extra machining and electrical energy cost. Machine operators can use these graphs to run the milling process under optimal conditions. It is found that the optimal values of the feed and speed decrease as the cost of extra machining increases and the optimal machining condition is achieved at a low value of depth of cut. The multi-criteria optimization approach can be applied to investigate the optimal machining parameters of conventional manufacturing processes such as turning, drilling, grinding, and advanced manufacturing processes such as electrical discharge machining.