基于支持向量机的电火花加工8418钢表面粗糙度预测模型

由于电火花加工过程的复杂性,单纯通过电火花加工实验方法研究各种放电参数及非电参数对工件表面粗糙度Ra的影响不但耗费大量时间,而且实验成本较高,为此基于支持向量机提出了一种适用于电火花加工表面粗糙度预测的模型。利用遗传算法对该模型中的各参数进行优化,预测不同电火花加工参数组合下的表面粗糙度;以电火花加工8418模具钢为例,将预测值与实验值进行对比,并且通过实验验证了电火花加工8418钢表面粗糙度预测模型参数的准确性;最后进行了误差分析,模型的最大误差值为2.27%。     

弯管内表面铣削加工粗糙度仿真研究

基于一种新颖的内置导轨弯管内表面铣削加工装置,建立针对弯管内表面加工粗糙度预测的球头铣刀铣削加工仿真数学模型,并采用MATALB进行仿真加工计算与微观几何形貌绘制。通过等切削参数实验验证模型有效性,对比表面轮廓仪观测的实验加工表面与模型计算输出的表面微观几何形貌特征,二者呈现相同的变化趋势,且粗糙度值Ra相对误差最大为15.7%,在允许范围内。模型可信度高、可靠性强,对研究弯管内表面加工粗糙度的影响因素有一定理论指导意义。     

镍基高温合金锥面外圆车削表面粗糙度优化研究

以GH4169镍基高温合金外圆锥面车削加工为研究对象,通过正交实验的方法,研究切削参数对加工表面粗糙度的影响规律。并运用BP神经网络预测的方法建立了表面粗糙度经验模型。经过实验验证,该模型具有较好的预测精度。另外还对工件表面粗糙度与刀尖圆弧半径及切削深度的关系进行了研究。发现较大的刀尖圆弧半径能获得较小的表面粗糙度值,且增大刀尖圆弧半径可进一步减小切削深度对表面粗糙度的影响。该研究结果可为GH4169圆锥面车削加工提供技术指导和理论支持。     

TC4钛合金高速铣削表面粗糙度研究

TC4钛合金被广泛地用于航空航天等众多领域,为了提高钛合金零件的表面加工质量和加工效率,对TC4钛合金高速铣削表面粗糙度进行研究具有十分重要的意义。切削参数是影响TC4钛合金加工表面粗糙度的重要因素,采用了正交试验分析主轴转速n、铣削深度ap、铣削宽度ae和每齿进给量fz等4个试验因素对表面粗糙度的影响规律,运用了极差分析法绘制出铣削参数对表面粗糙度的影响趋势曲线。利用了多元线性回归分析计算出表面粗糙度的数学模型,采用F值检验法对数学模型和模型参数进行了显著性验证:FF0.01(4,11),证明了模型和参数都是高度显著的。利用了表面粗糙度预测模型对另外8组切削参数进行粗糙度预测,并将预测结果与实际实验结果时行对比,最大误差为8.9%,验证了表面粗糙度预测模型的有效性,为TC4钛合金加工提供了理论依据。     

圆柱滚子表面特性的分形研究

基于分形理论的非线性特征,对轴承中的圆柱滚子加工表面轮廓进行理论研究,探讨用分形维数来表征其加工表面的粗糙程度。通过理论计算及实验验证,得出了圆柱滚子外表面的分维表征。结果表明:表面粗糙度与分形维数是属于一种非线性关系;功率谱线平均斜率是加工表面粗糙度的有效表征;分形维数能客观地表征机械加工表面粗糙度,且大小与取样参数无关。     

弯管外表面五轴数控铣削表面粗糙度仿真

根据核电主管道弯管外表面环形曲面难加工、表面质量不均匀的特殊性,考虑球头铣刀铣削加工弯管外表面质量的因素(几何因素)进行表面粗糙度数学建模,采用计算机模拟仿真的方法对模型的粗糙度进行分析。在五轴数控铣削情况下,进行路径规划,利用球头铣刀进行加工,通过设置不同工艺参数下的铣削加工条件进行实验验证。结果显示,实验结果与仿真结果具有较高的吻合度,模型输出表面粗糙度值Ra与实测值误差不超过14.6%,在误差允许范围内。该模型可信度高、可靠性强,为弯管外表面数控加工提供理论和实验依据。     

分形维数与铣削参数的模型关联及验证

基于工程粗糙表面的微观形貌具有统计自相似分形特征,将分形几何学运用于金属材料表面形貌研究。粗糙表面的分形参数与加工条件密切相关。铣削加工过程中,切削参数会影响表面分形维数和表面粗糙度,考察了分形维数和传统表面粗糙度参数之间的关系,分别建立铣削参数与表面分形维数和表面粗糙度之间关系模型,并采用实验进行验证。实验结果表明,铣削加工表面具有分形特征;铣削表面分形维数D基本不随切削速度增加而变化,但表面粗糙度Ra会随切削速度的增加而减小;表面粗糙度与加工进给量成正相关,分形维数先增大后减小,并存在临界点;分形维数D与表面粗糙度Ra呈幂指数关系;所建立模型合理。相关研究结果可以为提高工程表面的使用性能及降低成本提供参考。     

基于遗传算法与BP神经网络的微晶玻璃点磨削工艺参数优化

通过低膨胀微晶玻璃点磨削实验,测试了加工表面粗糙度、表面硬度,分析了实验数据变化趋势。通过最小二乘拟合,建立了关于粗糙度、表面硬度的一元数值模型,并将模型预测值与实验值进行了比较,以验证模型的精确性,结果表明模型具有较高的精度。根据正交实验结果,基于BP神经网络算法和遗传算法,建立了粗糙度、表面硬度的多元数值模型并以此作为目标函数,以表面硬度最大和表面粗糙度最小作为优化目标,基于遗传算法进行了工艺参数的双目标优化,获得了一组点磨削工艺参数的最优解范围,实验验证结果表明优化结果是合理的。     

混粉大面积电火花加工机理的分析

根据电火花加工原理和特点,分析了传统大面积电火花加工很难获得良好粗糙度的原因,同时探讨了混粉电火花加工改善大面积加工表面粗糙度的原因,并用实践验证了混粉电火花加工能改善加工表面粗糙度     

微磨料气射流成形加工硅片表面粗糙度模型

微磨料气射流加工技术具有热影响区小、加工时切削力小、几乎能加工所有材料的优势,适合硬脆材料的成形加工。表面粗糙度是衡量微磨料气射流成形加工质量的重要指标之一。通过微磨料气射流成形加工硅片实验,获得表面粗糙度的实验数据。用量纲分析法归纳出微磨料气射流成形加工表面粗糙度的无量纲影响参量,获得表面粗糙度的无量纲函数通式。基于广义回归神经网络的基本结构,以无量纲函数通式中的自变量为网络的输入,因变量为网络的输出,以表面粗糙度实验数据为样本集,构建了基于量纲分析法的广义回归神经网络表面粗糙度模型。实验证明该模型能有效的预测表面粗糙度。     

2Cr13不锈钢高速铣削表面粗糙度预测模型研究

针对汽轮机叶片常用钢2Cr13不锈钢在切削加工中表面质量存在的问题,对高速铣削条件下2Cr13不锈钢表面粗糙度预测模型进行了研究。将最小二乘支持向量机原理应用到高速铣削2Cr13不锈钢的表面粗糙度预测建模中。得出的模型能方便地预测铣削参数对表面粗糙度的影响,并能利用有限的试验数据得出整个工作范围内的表面粗糙度预测值。经试验验证,应用最小二乘支持向量机原理建立的粗糙度预测模型回归预测精度高。基于最小二乘支持向量机原理建模方法适合于表面粗糙度预测。     

Kinematics prediction and experimental validation of machined surface roughness

Online monitoring of surface roughness is a desirable capability for machining processes; however, 100 % inspection of all parts is not feasible unless it can be integrated into the machining process itself through real-time monitoring of cutting conditions. One strategy is to feed these conditions into a predictive modeling kernel which would in turn give the properties of the finished part. In the case of roughness, the surface resulting from turning can be largely represented as the trace of the passing tool geometry. The question addressed herein is whether computationally intensive modeling of the surface accounting for tool nose radius is necessary for online monitoring of surface roughness. This paper presents a predictive modeling methodology wherein the tool-workpiece contact position varies under a simple cutting model, and the resulting surface roughness is estimated. It presents the concept of calculating a “pseudo-roughness” value based only on tool tip locations and to compare this value to that determined by full predictive modeling of the tool geometry. Cutting experimental data has been presented and compared to predictions for model validation. It is found that the root mean square roughness calculation is dominated by tool geometry, rather than tool position deviations and surface roughness estimation could be implemented without a computationally intensive modeling component, thereby enabling online monitoring and potentially real-time control of the part finish.     

Accurate estimation of surface roughness from texture features of the surface image using an adaptive neuro-fuzzy inference system

Accurate estimation of surface roughness of workpieces in turning operations play an important role in the manufacturing industry. This paper proposes a method using an adaptive neuro-fuzzy inference system (ANFIS) to establish the relationship between actual surface roughness and texture features of the surface image. The accurate modeling of surface roughness can effectively estimate surface roughness. The input parameters of a training model are spatial frequency, arithmetic mean value, and standard deviation of gray levels from the surface image, without involving cutting parameters (cutting speed, feed rate, and depth of cut). Experiments demonstrate the validity and effectiveness of fuzzy neural networks for modeling and estimating surface roughness. Experimental results show that the proposed ANFIS-based method outperforms the existing polynomial-network-based method in terms of training and test accuracy of surface roughness.     

Effect of roughness geometry on friction during micro V-die bending by using finite element method

Interfacial conditions such as friction and roughness tend to be the dominant process characteristics when simulating sheet metal forming processes. Therefore, accurately modeling the tool workpiece interface is essential. Additionally, the accuracy of conventional methods of modeling the interface is insufficient for microforming. This work presents a novel approach for describing friction by modeling the geometric roughness of the tool surface instead of using the conventional friction coefficient or factor in dry contact. This finite element-based model was validated in terms of loads and metal flow in two cases of micro V-die bending processes. One process modeled a conventional flat tool surface with a friction coefficient. Another process modeled a rough tool surface described as roughness geometry with zero friction. In addition to elucidating how the roughness geometry of a tool surface affects friction during microforming, this work provides fundamental information about the interfacial conditions of the contact surface as well as improved accuracy and flexibility compared to conventional friction models. Another important application of the friction calculation results in this study is in microforming applications.     

Formation of iron aluminide coatings on plain carbon steel by TIG process

Micro end-milling is widely used in many industries to produce micro products with complex 3D shapes. The accurate modeling and prediction of surface roughness are important for evaluating the productivity of the machine tools and the surface quality of the machined parts. This paper presents an accurate surface roughness model based on the kinematics of cutting process and tool geometry by considering the effects of tool run-out and minimum chip thickness. The proposed surface roughness model is validated by micro end-milling experiments with the miniaturized machine tool. The results show that the proposed surface roughness model can accurately predict both the trends and magnitude of the surface roughness in micro end-milling.     

Study on the prediction model of surface roughness for side milling operations

The paper presents a feasibility study on prediction of surface roughness in side milling operations using the different polynomial networks. A series of experiments using S45C steel plates is conducted to study the effects of the various cutting parameters on surface roughness. The different polynomial networks for predicting surface roughness are developed using the abductive modeling technique and based on the F-ratio to select their input variables. The results show that the developed models achieve high predicting capability on surface roughness, especially for the case of smaller flank wear of peripheral cutting edge. Hence, it can be concluded that the developed polynomial-network models posses promising potential in the application of predicting surface roughness in side milling operations.     

基于遗传算法的超精密切削表面粗糙度预测模型参数辨识及切削用量优化

建立易于分析各切削用量对粗糙度影响关系的表面粗糙度预测模型和最优的切削用量组合,是超精密切削加工技术的不断发展的需要。针对最小二乘法和传统优化方法的不足,提出了将遗传算法用于超精密切削表面粗糙度预测模型的参数辨识,并用于求解最优切削用量,给出了金刚石刀具超精密切削铝合金的表面粗糙度预测数学模型和切削用量优化结果,进行了遗传算法和常规优化算法的比较,结果表明遗传算法较最小二乘法和传统的优化方法更适合于粗糙度预测模型的参数辨识及保证切削用量的最优。     

基于模糊基函数网络和自适应最小二乘算法的外圆纵向磨削表面粗糙度的预测

建立了外圆纵向磨削表面粗糙度的模糊基函数网络（FBFN）预测模型，网络的训练采用自适应最小二乘算法（ALS）。ALS将最小二乘算法和遗传算法相结合，能够自主学习，不用人为干预，FBFN和粗糙度的分析模型相结合，只需少量实验数据便可完成网络的训练，自动产生模糊规则，确定隐含层的节点数。仿真和实验结果表明，FBFN网络结构非常适合粗糙度的预测和控制，采用ALS学习方法比BP算法、传统的遗传算法和正交二乘法等能产生更好的结果。     

Analytical Surface Roughness Parameters of a Theoretical Profile Consisting of Elliptical Arcs

The closed-form solutions of surface roughness parameters for a theoretical profile consisting of elliptical arcs are presented. Parabolic and simplified approximation methods are commonly used to estimate the surface roughness parameters for such machined surface profiles. The closed-form solution presented in this study reveals the range of errors of approximation methods for any elliptical arc size. Using both implicit and parametric methods, the closed-form solutions of three surface roughness parameters, R_t, R_a, and R_q, were derived. Their dimensionless expressions were also studied and a single chart was developed to present the surface roughness parameters. This research provides a guideline on the use of approximate methods. The error is smaller than 1.6% when the ratio of the feed and major semi-axis of the elliptical arc is smaller than 0.5. The closed-form expressions developed in this study can be used for the surface roughness modeling in CAD/CAM simulations.     

Regression analysis, support vector machines, and Bayesian neural network approaches to modeling surface roughness in face milling

This study examines the influence of cutting speed, feed, and depth of cut on surface roughness in face milling process. Three different modeling methodologies, namely regression analysis (RA), support vector machines (SVM), and Bayesian neural network (BNN), have been applied to data experimentally determined by means of the design of experiment. The results obtained by the models have been compared. All three models have the relative prediction error below 8%. The best prediction of surface roughness shows BNN model with the average relative prediction error of 6.1%. The research has shown that, when the training dataset is small, both BNN and SVR modeling methodologies are comparable with RA methodology and, furthermore, they can even offer better results. Regarding the influence of the examined cutting parameters on the surface roughness, it has been shown that the feed has the largest affect on it and the depth of cut the least.