Multiple-response optimization for micro-endmilling process using response surface methodology

In the present trend, new fabrication methods for producing miniaturized components are gaining popularity due to the recent advancements in micro-electro mechanical systems. Micro-machining differs from the traditional machining with the small size tool, resolution of x?Cy and z stages. This paper focuses RSM for the multiple response optimization in micro-endmilling operation to achieve maximum metal removal rate (MRR) and minimum surface roughness. In this work, second-order quadratic models were developed for MRR and surface roughness, considering the spindle speed, feed rate and depth of cut as the cutting parameters, using central composite design. The developed models were used for multiple-response optimization by desirability function approach to determine the optimum machining parameters. These optimized machining parameters are validated experimentally, and it is observed that the response values are in good agreement with the predicted values.     

Multi-response optimisation of WEDM process using principal component analysis

Like many other processes, the wire electrical discharge machining (WEDM) process has several performance characteristics. Determination of the optimal process settings with respect to all these performance measures (responses) is an important issue. Taguchi’s robust design method can only be applied to optimise a single-response problem. Some researchers have attempted to optimise WEDM operations using a multi-response signal-to-noise (MRSN) ratio and constraint optimisation methods. Both these methods suffer from some weaknesses. The principal component analysis (PCA)-based approach for multi-response optimisation can effectively overcome those weaknesses. In this paper, some modifications in the PCA-based approach are suggested and two sets of experimental data published by the past researchers are analysed using this modified procedure. It is observed that the PCA-based optimisation can give better results than the constrained optimisation and MRSN ratio-based methods, which can be attributed to the fact that the possible correlation among the multiple responses is taken care in the PCA-based approach.     

Multi-response optimization of Micro-EDM process parameters on AISI304 steel using TOPSIS

The Technique for order preference by similarity to ideal solution (TOPSIS) method of optimization is used to analyze the process parameters of the micro-Electrical discharge machining (micro-EDM) of an AISI 304 steel with multi-performance characteristics. The Taguchi method of experimental design L27 is performed to obtain the optimal parameters for inputs, including feed rate, current, pulse on time, and gap voltage. Several output responses, such as the material removal rate, electrode wear rate, overcut, taper angle, and circularity at entry and exit points, are analyzed for the optimal conditions. Among all the investigated parameters, feed rate exerts a greater influence on the hole quality. ANOVA is employed to identify the contribution of each experiment. The optimal level of parameter setting is maintained at a feed rate of 4 μm/s, a current of 10 A, a pulse on time of 10 μs, and a gap voltage of 10 V. Scanning electron microscope analysis is conducted to examine the hole quality. The experimental results indicate that the optimal level of the process parameter setting over the overall performance of the micro-EDM is improved through TOPSIS.     

Evaluation of the effects of machining parameters on MQL based surface grinding process using response surface methodology

Journal of Mechanical Science and Technology - Grinding is a precision machining process widely used for close tolerance and good surface finish. Due to aggregate of geometrically undefined cutting...     

Multi-response optimization on single-point incremental forming of hyperbolic shape Al-1050/Cu bimetal using response surface methodology

In present work, experimental and numerical investigations were carried out on single-point incremental forming of explosive bonded clad sheets. The sheets were produced by explosion welding from 1050 aluminum alloy and C10100 copper alloy sheets. A generatrix of hyperbolic curve was utilized as profile of final shapes formed by SPIF process. During some investigations, the interaction and main effect of the process parameters viz. tool diameter, step down, rotational speed, and sheet arrangement were evaluated on the fracture depth and wall thickness at fracture using ANOVA method. For experimentation, a customized design table was built with three quantify and one qualify factors in two levels. The design table totally provides four input factors and two responses in 12 runs. The responses are fracture depth and wall thickness. A multi-response optimization was conducted to find optimum values for input parameters using response surface methodology (RSM) and the confirmatory experiment revealed the reliability of RSM in this regard. Moreover, predictive models were presented in confidence interval of 95% to formulate the relationship between the responses and the input factors using RSM approach. Additionally, a finite element analysis was carried out on the SPIF based on optimal input parameters to depict reaction force changing, thickness variation, and stress distribution.     

Optimization of abrasive flow polishing process parameters for static blade ring based on response surface methodology

Static blade ring process technology is a key part for gas turbine manufacturing, and the surface quality of the static blade ring has great influence on a gas turbine. To improve surface roughness of static blade ring, abrasive flow polishing process technology is studied. First, the range of extrusion pressure is obtained by using ANSYS software to analyze the blade deformation. Then a simplified model of surface roughness is estabalished according to experimental results and ANOVA’s results. At the same time, the optimal polishing parameters are confirmed through the response surface methodology. Finally, the polishing experiment is carried out by using the optimal polishing parameters. The experimental results demonstrate that the surface roughness of static blade ring decreased greatly (nearly 14.7%) compared with result using normal parameters.     

Optimisation of machining parameters for turning operations based on response surface methodology

Design of experiments has been used to study the effect of the main turning parameters such as feed rate, tool nose radius, cutting speed and depth of cut on the surface roughness of AISI 410 steel. A mathematical prediction model of the surface roughness has been developed in terms of above parameters. The effect of these parameters on the surface roughness has been investigated by using Response Surface Methodology (RSM). Response surface contours were constructed for determining the optimum conditions for a required surface roughness. The developed prediction equation shows that the feed rate is the main factor followed by tool nose radius influences the surface roughness. The surface roughness was found to increase with the increase in the feed and it decreased with increase in the tool nose radius. The verification experiment is carried out to check the validity of the developed model that predicted surface roughness within 6% error.     

基于响应面分析法的货叉关键尺寸参数优化设计

叉车货叉作为物流设备的重要组成部件,受载情况复杂多变,易产生高应力区域,甚至导致疲劳破坏。货叉的传统设计方法是通过经验法或实验法来确定货叉关键尺寸参数,该方法可靠性低,设计周期长。采用响应面分析法(Response Surface Methodology,RSM)对货叉关键尺寸参数进行优化。首先将货叉结构中各个加强筋的主要尺寸离散为7个关键尺寸参数,采用有限元灵敏度分析法得到了各个参数对货叉力学性能的响应关系;然后以货叉小变形、低应力和轻量化作为优化目标,采用响应面分析法中的中心组合设计(Central Composite Design,CCD)对80组样本进行了优化,在不增加货叉质量的情况下,最大应力减小了8.8%。基于响应面分析法的货叉关键尺寸参数优化设计方法已应用于诺力某型号叉车,取得了良好的优化效果,为类似的结构设计提供了一种有效可行的设计方法。     

Surface crack density and recast layer thickness analysis in WEDM process through response surface methodology

In this work, quantitative assessment of surface damage in terms of parameters like surface crack density and recast layer thickness in wire electrical discharge machining (WEDM) process has been undertaken. The effect of processing conditions on crack formation is studied using scanning electron microscope. Surface crack density and recast layer thickness analysis in terms of machining parameters such as pulse on time, pulse off time, peak current, spark gap voltage significantly deteriorate the microstructure of machined samples, which produces the deeper, wider overlapping craters, pock marks, globules of debris and micro cracks. The microstructure analysis of WEDM surface was based upon the theory of electrical discharge phase and metallurgical physics. It is found that the pulse on time, pulse off time and peak current are the most dominating parameters for both surface crack density and recast layer thickness.     

A study of optimization of injection molding process parameters for SGF and PTFE reinforced PC composites using neural network and response surface methodology

This study analyzed variations of mechanical characteristics that depend on the injection molding techniques during the blending of short glass fiber and polytetrafluoroethylene reinforced polycarbonate composites. A hybrid method including back-propagation neural network (BPNN), genetic algorithm (GA), and response surface methodology (RSM) are proposed to determine an optimal parameter setting of the injection molding process. The specimens are prepared under different injection molding processing conditions based on a Taguchi orthogonal array table. The results of 18 experimental runs were utilized to train the BPNN predicting ultimate strength, flexural strength, and impact resistance. Simultaneously, the RSM and GA approaches were individually applied to search for an optimal setting. In addition, the analysis of variance was implemented to identify significant factors for the injection molding process parameters and the result of BPNN integrating GA was also compared with RSM approach. The results show that the RSM and BPNN/GA methods are both effective tools for the optimization of injection molding process parameters.     

基于盘铣加工钛合金表面残余应力的工艺参数优化

为探索低速盘铣钛合金时铣削参数与加工表面残余应力的关系,设计完成了铣削速度、切削深度、进给速度三因素三水平的正交实验。实验结果表明,在所选用切削参数范围内,已加工表面残余应力均表现为压应力,铣削速度的变化对其影响最显著,进给速度次之,切削深度影响最小。提出将支持向量机回归技术应用于工艺参数优化,建立了盘铣加工钛合金表面残余应力的支持向量机模型,并通过实验以及与指数型回归经验模型的对比,验证了该方法的实用性和合理性。     

Multi-response optimization of machining parameters in hot turning using grey analysis

This paper envisages the multi-response optimization of machining parameters in hot turning of stainless steel (type 316) based on Taguchi technique. The workpiece heated with liquid petroleum gas flame burned with oxygen was machined under different parameters, i.e., cutting speed, feed rate, depth of cut, and workpiece temperature on a conventional lathe. The effect of cutting speed, feed rate, depth of cut, and workpiece temperature on surface roughness, tool life, and metal removal rate have been optimized by conducting multi-response analysis. From the grey analysis, a grey relational grade is obtained and based on this value an optimum level of cutting parameters has been identified. Furthermore, using analysis of variance method, significant contributions of process parameters have been determined. Experimental results reveal that feed rate and cutting speed are the dominant variables on multiple performance analysis and can be further improved by the hot turning process.     

Optimization of tool geometry parameters for turning operations based on the response surface methodology

This investigation focuses on the influence of tool geometry on the surface finish obtained in turning of AISI 1040 steel. In order to find out the effect of tool geometry parameters on the surface roughness during turning, response surface methodology (RSM) was used and a prediction model was developed related to average surface roughness (Ra) using experimental data. The results indicated that the tool nose radius was the dominant factor on the surface roughness. In addition, a good agreement between the predicted and measured surface roughness was observed. Therefore, the developed model can be effectively used to predict the surface roughness on the machining of AISI 1040 steel with in 95% confidence intervals ranges of parameters studied.     

Multi-response optimization of machining parameters on red mud-based aluminum metal matrix composites in turning process

This paper presents the findings of an experimental investigation into the effects of cutting speed, feed rate, depth of cut, and nose radius in computer numerical control (CNC) turning operation performed on red mud-based aluminum metal matrix composites. This paper investigates optimization design of a turning process performed on red mud-based aluminum metal matrix composites. The major performance characteristics selected to evaluate the process are surface roughness, power consumption, and vibration, and the corresponding turning parameters are cutting speed, feed, depth of cut, and nose radius. Taguchi-based grey analysis, which uses grey relational grade as performance index, is specifically adopted to determine the optimal combination of turning parameters. The principal component analysis (PCA) is applied to evaluate the weighting values corresponding to various performance characteristics. L9 orthogonal array design has been used for conducting the experiments. The outcome of confirmation experiments reveals that grey relational analysis coupled with PCA can effectively be used to obtain the optimal combination of turning parameters. Hence, this confirms that the proposed approach in this study can be a useful tool to improve the turning performance of red mud-based aluminum metal matrix composites in CNC turning process.     

Modeling and optimization of a reluctance accelerator using DOE-based response surface methodology

This paper introduces experiment-based modeling and optimization of a reduced-scale Electromagnetic launcher (EML) using the Design of experiments (DOE) technique. Response surface models describing the velocity and kinetic energy of the launched projectile were developed using the Box–Behnken method with design variable transforms, and an Analysis of variance (ANOVA) was conducted to refine the models by removing statistically insignificant terms. A bi-objective optimization problem with the maximum velocity and maximum kinetic energy as objects was considered, and a Pareto front was obtained using the generated response surfaces as the solution of the problem. Verification tests on the optimal design points were conducted to demonstrate the validity of the developed models.