Surface roughness model for turning

The effect of the cutting parameters on the surface roughness in a turning operation has been investigated using response surface methodology. The mathematical prediction models for surface roughness have been obtained for a common mild steel. The equation is used for determining the optimal cutting conditions for a required surface roughness.     

Augmented D-optimal design for effective response surface modeling and optimization

For effective response surface modeling during sequential approximate optimization (SAO), thenormalized and the augmented D-optimality criteria are presented. Thenormalized D-optimality criterion uses the normalized Fisher information matrix by its diagonal terms in order to obtain a balance among the linear-order and higher-order terms. Then, it is augmented to directly include other experimental designs or the pre-sampled designs. This augmentation enables the trust region managed sequential approximate optimization to directly use the pre-sampled designs in the overlapped trust regions in constructing the new response surface models. In order to show the effectiveness of the normalized and theaugmented D-optimality criteria, following two comparisons are performed. First, the information surface of thenormalized D-optimal design is compared with those of the original D-optimal design. Second, a trust-region managed sequential approximate optimizer having three D-optimal designs is developed and three design problems are solved. These comparisons show that thenormalized D-optimal design gives more rotatable designs than the original D-optimal design, and theaugmented D-optimal design can reduce the number of analyses by 30 % –40 % than the original D-optimal design.     

Damage identification by multi-model updating in the modal and in the time domain

Computational model updating techniques are used to adjust selected parameters of finite element models in order to make the models compatible with experimental data. This is done by minimizing the differences (residuals) of analytical and experimental data, for example, natural frequencies and mode shapes by numerical optimization procedures. For a long-time updating techniques have also been investigated with regard to their ability to localize and quantify structural damage. The success of such an approach is mainly governed by the quality of the damage model and its ability to describe the structural property changes due to damage in a physical meaningful way. Our experience has shown that due to unavoidable modelling simplifications and measurement errors the changes of the corresponding damage parameters do not always indicate structural modifications introduced by damage alone but indicate also the existence of other modelling uncertainties which may be distributed all over the structure. This means that there are two types of parameters which have to be distinguished: the damage parameters and the other parameters accounting for general modelling and test data uncertainties. Although these general parameters may be physically meaningless they are necessary to achieve a good fit of the test data and it might happen that they cannot be distinguished from the damage parameters. For complex industrial structures it is seldom possible to generate unique structural models covering all possible damage scenarios so that one has to expect, that the parameters introduced for describing the damage will not be fully consistent with the physical reality. Even then the change of such parameters identified from test data taken continuously or temporarily over the time may serve as a feature for structural health monitoring. It is well known that low-frequency modal test data or static response data are not very well suited for detecting and quantifying localized small size damage. Time domain response data from impact tests carry high-frequency information which usually is lost when experimental modal data are utilized for damage identification. Even so only little literature was found addressing the utilization of experimental time histories for model updating in conjunction with damage identification.In the present paper we summarize the methodology of computational model updating and report about our experience with damage identification using two different model updating techniques. The first is based on classical modal residuals (natural frequencies and mode shapes) which is extended to allow for simultaneous updating of two models, one for the initial undamaged structure and the second for the damaged structure using the test data of both states (multi-model updating). The second technique uses residuals composed of measured and analytical time histories. Time histories have the advantage of carrying high-frequency information which is beneficial for the detection of local damage and which usually is lost when modal residuals are used. Both techniques have been applied to the same beam structure consisting of two thin face sheets which were bonded together by an adhesive layer. It was the aim of this application to study the performance of the two techniques to localize and quantify the damage which was introduced locally in the adhesive layer.     

The use of D-optimal design for modeling and analyzing the vibration and surface roughness in the precision turning with a diamond cutting tool

Using a diamond cutting tool in the precision turning process, the vibration of tool-tip has an undesirable effect on the machined surface??s quality. The objective of this paper is to present the mathematical models for modeling and analyzing the vibration and surface roughness in the precision turning with a diamond cutting tool. Machining parameters including the spindle speed, feed rate and cutting depth were chosen as numerical factor, and the status of lubrication was regarded as the categorical factor. An experimental plan of a four-factor??s (three numerical plus one categorical) D-optimal design based on the response surface methodology was employed to carry out the experimental study. A micro-cutting test is conducted to visualize the effect of vibration of tool-tip on the performance of surface roughness. With the experimental values up to a 95% confidence interval, it is fairly well for the experimental results to present the mathematical models of the vibration and surface roughness. Results show that the spindle speed and the feed rate have the greatest influence on the longitudinal vibration amplitude, and the feed rate and the cutting depth play major roles for the transverse vibration amplitude. As the spindle speed increases, the overall vibration of tool-tip tends to more stable condition which leads to the results of the best machined surface. The effects of the feed rate and cutting depth provide the reinforcement on the overall vibration to cause the unstability of cutting process and exhibit the result of the worst machined surface.     

Optimisation of springback in bending processes using FEM simulation and response surface method

The aim of this work includes the springback optimisation of bending processes using the concept of experimental design and response surface methodology (RSM). The optimisation method includes two phases. The first involves the objective function prediction using design of experiments and response surface method, while the second is an optimisation process using a FORTRAN gradient algorithm. Springback of sheet parts during bending processes is simulated using finite element model (FEM) including damage evolution effects within the sheet. The numerical simulation of the damage evolution has been modelled by means of continuum damage approach. The Lemaitre damage model, taking into account the influence of triaxiality, has been implemented into ABAQUS/Standard code in order to predict the external fibres rupture evolution during the process and the material characteristics changes after bending. The simulation included die corner radius and punch-die clearance as the main variables.     

Selection of optimum tool geometry and cutting conditions using a surface roughness prediction model for end milling

Influence of tool geometry on the quality of surface produced is well known and hence any attempt to assess the performance of end milling should include the tool geometry. In the present work, experimental studies have been conducted to see the effect of tool geometry (radial rake angle and nose radius) and cutting conditions (cutting speed and feed rate) on the machining performance during end milling of medium carbon steel. The first and second order mathematical models, in terms of machining parameters, were developed for surface roughness prediction using response surface methodology (RSM) on the basis of experimental results. The model selected for optimization has been validated with the Chi square test. The significance of these parameters on surface roughness has been established with analysis of variance. An attempt has also been made to optimize the surface roughness prediction model using genetic algorithms (GA). The GA program gives minimum values of surface roughness and their respective optimal conditions.     

Modeling and analyzing the effects of air-cooled turning on the machinability of Ti–6Al–4V titanium alloy using the cold air gun coolant system

This study provides the mathematical models for modeling and analyzing the effects of air-cooling on the machinability of Ti–6Al–4V titanium alloy in the hard turning process. A cold air gun coolant system was used in the experiments and produced a jet of compressed cold air for cooling the cutting process. The air-cooling process seems to be a good environment friendly option for the hard turning. In this experimental investigation, the cutting speed, feed rate and cutting depth were chosen as the numerical factor; the cooling method was regarded as the categorical factor. An experimental plan of a four-factor (three numerical plus one categorical) D-optimal design based on the response surface methodology (RSM) was employed to carry out the experimental study. The mathematical models based on the RSM were proposed for modeling and analyzing the cutting temperature and surface roughness in the hard turning process under the dry cutting process and air-cooling process. Tool wear and chip formation during the cutting process were also studied. The compressed cooling air in the gas form presents better penetration of the lubricant to the cutting zone than any conventional coolants in the cutting process do. Results show that the air-cooling significantly provides lower cutting temperature, reduces the tool wear, and produces the best machined surface. The machinability performance of hard turning Ti–6Al–4V titanium alloy on the application of air-cooling is better than the application of dry cutting process. This air-cooling cutting process easily produces the wrinkled and breaking chips. Consequently, the air-cooled cutting process offers the attractive alternative of the dry cutting in the hard turning process.     

Improved tooth trajectory model for prediction of milled surface geometry

The prediction on surface roughness and surface geometry in peripheral milling operation is a crucial but tedious task. The current researches concerned are almost based on circular tooth trajectory assumption to simplify analysis and modeling, and some models derived from true tooth trajectory are not concise enough or have certain limitation when put into practice. A novel idea on account of the principle of curvature circle approximation to true tooth path curve in cutting contact point nearby is presented, and the relevant mathematical theory is established. Then by means of specific curvature circles, mathematical models and analysis methods of surface roughness are discussed systematically, which cover two situations—cutting with and without runout effect. The validity and superiority of the models are demonstrated by experimental studies and numerical simulations. Finally, based on the suggested models and analytical methods, the effects of runout, feed rate, and cutter geometry on surface roughness and geometry are studied in depth, and several significant mathematical formulas and quantitative conclusions are reached, which would contribute to the theory of milled surface studies.     

Comparative study of damped FE model updating methods

Most of finite element (FE) model updating techniques do not employ damping matrices and hence, cannot be used for accurate prediction of complex frequency response functions (FRFs) and complex mode shapes. In this paper, a detailed comparison of two approaches of obtaining damped FE model updating methods are evaluated with the objective that the FRFs obtained from damped updated FE models is able to predict the measured FRFs accurately. In the first method, damped updating FE model is obtained by complex parameter-based updating procedure, which is a single-step procedure. In the second method, damped updated model is obtained by the FE model updating with damping identification, which is a two-step procedure. In the first step, mass and stiffness matrices are updated and in the second step, damping matrix is identified using updated mass and stiffness matrices, which are obtained in the previous step. The effectiveness of both methods is evaluated by numerical examples as well as by actual experimental data. Firstly, a study is performed using a numerical simulation based on fixed–fixed beam structure with non-proportional viscous damping model. The numerical study is followed by a case involving actual measured data for the case of F-shaped test structure. The updated results have shown that the complex parameter-based FE model updating procedure gives better matching of complex FRFs with the experimental data.     

Simultaneous multi-objective optimization of stainless steel clad layer on pressure vessels using genetic algorithm

Metal cladding is a process of depositing a filler material to enhance the surface properties of base material using a suitable welding process. In this work the clad specimens are produced by surfacing a layer of filler material using weld cladding process to minimize the heat loss across the walls of the pressure vessels. It is done by depositing a low thermal conductivity austenitic stainless steel grade of 316L on structural steel plates used for boiler construction using flux cored arc welding process. The experimental study is carried out as per design of experiments availed for five factors five levels central composite design using response surface methodology. The mathematical models are developed for the prediction of clad layer height, clad layer width and depth of penetration. These models are employed in formulating fitness functions for multi-objective optimization of clad layer dimensions using genetic algorithm (GA). The set of optimal solutions suggested by response surface optimizer and genetic algorithm are compared and discussed. Conformity tests are conducted to validate the prediction capability of developed models and optimum settings. Optimum clad layer dimensions have been arrived and optimized stainless steel clad specimen has been produced. The heat transfer analysis is planned to be conducted in the next phase. The findings can be used in energy efficient design of pressure vessels.     

Response surface methodology for eicosanoic acid triboproperties in castor oil

Response surface methodology with Box-Benhken (BB) design of experiment is utilized to study the antiwear (AW) and lubricity properties of eicosanoic acid in biodegradable castor oil base-stock and the results were compared with those of zinc dialkyldithiophosphate (ZDTP) and octadecanoic acid. The designs utilize load, additive concentration and temperature to develop models for the antiwear and lubricity response in a four-ball configuration. This was done by performing statistically designed experiments, estimating the coefficients in the mathematical models, predicting the response, checking for adequacy of the model and response optimization. Comparison of predicted and experimental response values outside the design conditions and for the optimal wear conditions showed good correspondence, implying that empirical models derived from response surface approach can be used to describe the tribological behavior of the additives in castor oil base-stock. Eicosanoic acid showed good tribological properties comparable to the popular octadecanoic acid and ZDTP lubricant additives, without acidic corrosion phenomenon as in ZDTP.     

Using higher precision-based response surface designs to determine the optimal process target

The selection of the optimal process target is critically important as it directly affects the defect rate, material cost, scrap and rework costs, and the loss to customers. Within the context of the optimal process target problem, a new model is proposed in this paper and two distinct contributions to the related topic are offered. First, while most research work assumes a given process distribution with a known variance, this paper integrates response surface designs into solving the optimal process target problem, thus removing the need to make assumptions regarding process parameters. Second, typical response surface designs consider second-order fitted functions; however, this paper considers a procedure to include higher-order polynomial terms that will result in higher prediction capabilities, thereby giving a more accurate representation of the true process. A constrained nonlinear optimization scheme is used to facilitate the development of this methodology and a numerical example is provided for illustration.     

Using response surface methodology with response transformation in optimizing plasma spraying coatings

The paper aims at proposing a method to develop a robust partially stabilized zirconia (PSZ) performance for the plasma spraying process with applications of surface response methodology and fractional factorial experiment. First, with the application of analysis of variance, significant factors are screened. Appropriately choosing control factors while constructing response functions helps to develop a robust process. Second, a nonlinear response surface function is designed to explore the space of the process. This smooths the progress of developing not only a proper relation between yielded and process variables, but also an optimal parameter setting that produces desirable response values. Experimental results show that a quadratic model with the proposed two-step design make it a simple, effective, and efficient way to a robust process. Model prediction is improved by additional data transformation. Optimization of PSZ performance in the plasma spraying process has been achieved.     

Analyzing the design of vibration reduction with the rubber-layered laminates in the precision turning with a diamond cutting tool

Using a diamond cutting tool in the precision turning process, the vibration of tool-tip has an undesirable effect on the machined surface??s quality. The objective of this paper is to analyze the design of turning tool-bar combined with the rubber-layered laminates for minimizing the vibration amplitude of tool-tip in the precision turning with the diamond tool. The selected rubber materials are styrene butadiene rubber (SBR) and silicone rubber (SI). Machining parameters, including the spindle speed, feed rate, and cutting depth, were chosen as numerical factors, and the status of the rubber-layered laminates was regarded as the categorical factor. The status of the rubber-layered laminates set up three categories including the solid tool (without rubber-layered laminates), tool with SBR rubber-layered laminate, and tool with SI rubber-layered laminate. An experimental plan of a four-factor (three numerical plus one categorical) D-optimal design based on the response surface methodology was employed to carry out the experimental study. The results show that the design of the turning tool-bar combined with the rubber-layered laminates is proven to improve the damping forces of the turning tool-bar. The overall vibration on the tool-tip using the tool with the rubber-layered laminates tends to be in a more stable condition, which leads to the result of having the best machined surface. With experimental values up to a 95% confidence interval, it is fairly well for the experimental results to present the mathematical models of the surface roughness without/with the rubber-layered laminates.     

Quality chain design and optimization by multiple response surface methodology

To maintain optimal quality characteristics in the defined specification limits is a vital decision for any industry and service system. To avoid nonconformity in outputs, the stream of variations and their potential causes must be identified so that the response variables fall into desirable limits across the manufacturing or service chain. Response surface methodology is considered as a powerful technique to facilitate the analysis of the mentioned problem. This paper presents the general quality chain design problem as a mathematical program and also proposes a method to solve it using multiple response surface methodology. An example of multistage processes is analyzed by the proposed approach to show its efficacies numerically and analytically.     

考虑模型偏差的结构动力学模型修正

针对结构有限元模型修正后仍可能存在模型偏差的问题,提出用待修正参数的不确定性来表征模型偏差的有限元模型修正方法。首先,基于响应面方法识别得到待修正参数的最优值,并通过计算结果与试验结果比较获得模型偏差;然后,基于响应面模型并结合灵敏度分析计算得到模型偏差对待修正参数的影响,从而得到考虑模型偏差后待修正参数的区间;最后,通过一个悬臂梁工程实例的模型修正,验证了笔者所提出方法的可行性。结果表明,考虑模型偏差的修正可以提高模型可靠性。     

由频率响应试验识别机床的状态空间数学模型

系统的识别,已成为一个新的学科分支。关于机床动态系统的识别,一般采用频率响应试验或机械阻抗测试法。这种方法的应用取得了一定的成效。我们研究了机床动态系统的一种新的识别原理和方法,成功地完成了试验。首先,我们实测出机床的频率响应数据,经过微机曲线拟合计算,识别出系统的传递矩阵。最后,应用现代控制论,用状态图法,由传递矩阵求得系统的状态空间数学模型。     

Optimization of spring-back in creep age forming process of 7075 Al-Alclad alloy using D-optimal design of experiment method

This paper attempts to model and predict the spring-back for creep age forming of a 7075 Al-Alclad alloy using statistical analyses based on a design of experiments method. Time and temperature were chosen as effective variables for determining spring-back in the creep age-forming process. The D-optimal design of experiments method facilitated statistical analyses and the extraction of a mathematical model for determining spring-back in the experimental variables domain. The spring-back of the specimens was calculated using a numerical procedure based on the pure bending theory. Analysis of the variances for spring-back showed that temperature was the most effective variable in the creep-age forming process. Additionally, a mathematical model and the response surface of the spring-back showed that to decrease spring-back, the significant variables should be in the upper level. The spring-back in the creep age-forming process was optimized for a 7075 Al-Alclad alloy in the optimum mechanical properties region.     

基于误差反传神经网络的智能预诊方法及其应用

为实现在故障发生之前进行预测和预防.从实现智能预诊的系统功能角度出发,提出了智能预诊方法框架,建立了基于误差反传神经网络的性能衰退过程智能评估及剩余寿命动态预测模型,并对模型的有效性与预测误差等问题进行了深入分析.从实际应用的角度出发,针对信息不完备问题,实现了模型更新与动态预测.随着采集数据的不断增多,对预测模型进行适当调整,用调整后的网络模型给出剩余寿命的动态估值.提出的智能预诊方法已应用于哈尔滨汽轮机厂叶片材料疲劳测试分析系统,对叶片材料性能的分析与剩余寿命的预测证明了该方法的实用性和有效性.     

Modeling and analysis of electrode wear and white layer thickness in die-sinking EDM process through response surface methodology

In this study, an attempt has been made to model electrode wear (EW) and recast layer thickness (WLT) through response surface methodology (RSM) in a die-sinking EDM process. A central composite rotatable design (CCRD) involving three variables with five levels has been employed to establish a mathematical model between input parameters and responses. Pulse on-time, pulse off-time and pulse current were changed during the tests based on the CCRD. The results of analysis of variance (ANOVA) indicated that the proposed mathematical models obtained can adequately describe the performances within the limits of factors being studied. The experimental and predicted values were in a good agreement.