Multi-objective optimization of process parameters for the helical gear precision forging by using Taguchi method

Precision forging of the helical gear is a complex metal forming process under coupled effects with multi-factors. The various process parameters such as deformation temperature, punch velocity and friction conditions affect the forming process differently, thus the optimization design of process parameters is necessary to obtain a good product. In this paper, an optimization method for the helical gear precision forging is proposed based on the finite element method (FEM) and Taguchi method with multi-objective design. The maximum forging force and the die-fill quality are considered as the optimal objectives. The optimal parameters combination is obtained through S/N analysis and the analysis of variance (ANOVA). It is shown that, for helical gears precision forging, the most significant parameters affecting the maximum forging force and the die-fill quality are deformation temperature and friction coefficient. The verified experimental result agrees with the predictive value well, which demonstrates the effectiveness of the proposed optimization method.     

Geometric optimization and performance analysis of radial MR valve using Taguchi orthogonal experiment method

Aiming at the problems of increasing external dimensions and deteriorating key performance indicators in the design process of magnetorheological (MR) valve by using structural optimization method, a geometric optimization design methodology for the optimal design of a MR valve structure under specific volume constraints is proposed in this article. The optimization methodology couples the finite element model (FEM) constructed in COMSOL software with the Taguchi orthogonal experiment and response surface technology to build an approximate response surface function for the identified independent variables. Suitable optimization algorithms are then utilized to determine the optimal geometry of the MR valve, thereby maximizing the valve performance. Firstly, a radial MR valve with a single excitation coil was presented, and its structure and working principle were also elaborated. A mathematical model of the pressure drop was derived on the basis of the Bingham-Papanastasiou non-Newtonian constitutive model of MR fluid and the magnetic circuit had been analyzed with the FE analysis methodology. Then, a second-order response surface model (RSM) had been fitted for the magnetic flux density in the radial flow channel and spool region of the radial MR valve based on the Taguchi orthogonal experimental design. The fitted model was a function of the four independent variables of the radial MR valve, and the accuracy of the developed response surface function over the entire design space had also been estimated. Meanwhile, predictions made by the RSM and FE models were evaluated by analysis of variance and it was exhibited that the RSM model’s results agree with FE result fairly. Subsequently, the geometric optimization problem had been formulated for the constructed RSM exploiting the genetic algorithm to find the global optimum geometrical parameters of the radial MR valve. Furthermore, the experimental test rig was setup to explore the pressure drop and the response time characteristics of the initial and optimal radial MR valve as well as the dynamic performance of the MR valve controlled cylinder system under different excitation conditions. The experimental results show that under the applied current of 2 A, the pressure drop and adjustable coefficient of the optimal radial MR valve observably increased with values of 3.15 MPa and 5.40, respectively, when compared to 2.11 MPa and 4.22 of their respective initial values. Also, at the applied current of 1.25 A, the damping force of the MR valve controlled cylinder system enlarged by 46 %, with its optimal value being 3.65 kN and initial value as 2.50 kN, which was an excellent verification of the correctness of the RSM and the effectiveness of the optimal design.

     

Roughness optimization of flow-formed tubes using the Taguchi method

The present study reports the effect of various flow-forming process parameters and roller geometry on the roughness of flow-formed tubes of commercial pure copper UNS C11000. Thickness reduction ratio, feed rate, angular speed of mandrel, attack angle of roller, roller tip radius, and smooth angle of roller were considered as variable parameters. The effects of these input parameters on the roughness have been critically analyzed using the Taguchi method. Through ANOVA analysis, it has been found that the roller tip radius is the most important parameter affecting roughness followed by thickness reduction ratio. Selection of an optimum combination of variable parameters was performed based on “average of results.” The minimum roughness of 1.37 μm was achieved when the process parameters were set at their optimum values.     

Surface roughness optimization of cold-sprayed coatings using Taguchi method

In this paper, Taguchi L 18 orthogonal array have been employed for depositing the electro-conductive coatings by varying various process parameters, i.e., substrate material, type of powder feeding arrangement, stagnation gas temperature, stagnation gas pressure, and stand-off distance. The response parameter of the coatings so produced is measured in terms of surface roughness. The optimum process parameters are predicted on the basis of analyses (ANOVA) of the raw data and signal to noise ratio. The significant process parameters in order of their decreasing percentage contribution are: stagnation pressure, stand-off distance, substrate material, stagnation temperature of the carrier gas, and feed arrangement of the powder particles, respectively.     

Multi-objective optimization of forming parameters for tube hydroforming process based on the Taguchi method

Tube hydroforming is an attractive manufacturing technology which is now widely used in many industries, especially the automobile industry. The purpose of this study is to develop a method to analyze the effects of the forming parameters on the quality of part formability and determine the optimal combination of the forming parameters for the process. The effects of the forming parameters on the tube hydroforming process are studied by finite element analysis and the Taguchi method. The Taguchi method is applied to design an orthogonal experimental array, and the virtual experiments are analyzed by the use of the finite element method (FEM). The predicted results are then analyzed by the use of the Taguchi method from which the effect of each parameter on the hydroformed tube is given. In this work, a free bulging tube hydroforming process is employed to find the optimal forming parameters combination for the highest bulge ratio and the lowest thinning ratio. A multi-objective optimization approach is proposed by simultaneously maximizing the bulge ratio and minimizing the thinning ratio. The optimization problem is solved by using a goal attainment method. An example is given to illustrate the practicality of this approach and ease of use by the designers and process engineers.     

Parametric optimization for tumour identification: bioheat equation using ANOVA and the Taguchi method

Breast cancer is the number one killer disease among women. It is known that early detection of a tumour ensures better prognosis and higher survival rate. In this paper an intelligent, inexpensive and non-invasive diagnostic tool is developed for aiding breast cancer detection objectively. This tool is based on thermographic scanning of the breast surface in conjunction with numerical simulation of the breast using the bioheat equation. The medical applications of thermographic scanning make use of the skin temperature as an indication of an underlying pathological process. The thermal pattern over a breast tumour reflects the vascular reaction to the abnormality. Hence an abnormal temperature pattern may be an indicator of an underlying tumour. Seven important parameters are identified and analysis of variance (ANOVA) is performed using a 2n design (n = number of parameters, 7). The effect and importance of the various parameters are analysed. Based on the above 2(7) design, the Taguchi method is used to optimize the parameters in order to ensure the signal from the tumour maximized compared with the noise from the other factors. The model predicts that the ideal setting for capturing the signal from the tumour is when the patient is at basal metabolic activity with a correspondingly lower subcutaneous perfusion in a low temperature environment.     

A parametric study on residual stresses and forging load in cold radial forging process

In this work, a comprehensive study of radial forging process is presented through 2-D axisymmetric and 3-D finite element simulations while considering internal tube profile. The tube used in this investigation has four internal helical grooves along its length. The material is modeled with the elastic-plastic behavior, and sliding-sticking friction model is utilized to model the die-workpiece and mandrel-workpiece contacts. The numerical results in the 2-D case are compared with available experimental data. Residual stresses in the forged product, stress concentration around the grooves, pressure distribution on the hammers and mandrel and maximum forging load are studied. The effects of process parameters such as workpiece and die geometries, percentage of deformation, and workpiece motions on residual stresses and applied pressures on the hammers and mandrel are investigated. The results provide a valuable insight into the parameters affecting radially forged products and provide a useful tool for better design of this process.     

Multi-objective optimization of injection molding process parameters in two stages for multiple quality characteristics and energy efficiency using Taguchi method and NSGA-II

In this paper, the parameters optimization of plastic injection molding (PIM) process was obtained in systematic optimization methodologies by two stages. In the first stage, the parameters, such as melt temperature, injection velocity, packing pressure, packing time, and cooling time, were selected by simulation method in widely range. The simulation experiment was performed under Taguchi method, and the quality characteristics (product length and warpage) of PIM process were obtained by the computer aided engineering (CAE) method. Then, the Taguchi method was utilized for the simulation experiments and data analysis, followed by the S/N ratio method and ANOVA, which were used to identify the most significant process parameters for the initial optimal combinations. Therefore, the range of these parameters can be narrowed for the second stage by this analysis. The Taguchi orthogonal array table was also arranged in the second stage. And, the Taguchi method was utilized for the experiments and data analysis. The experimental data formed the basis for the RSM analysis via the multi regression models and combined with NSGS-II to determine the optimal process parameter combinations in compliance with multi-objective product quality characteristics and energy efficiency. The confirmation results show that the proposed model not only enhances the stability in the injection molding process, including the quality in product length deviation, but also reduces the product weight and energy consuming in the PIM process. It is an emerging trend that the multi-objective optimization of product length deviation and warpage, product weight, and energy efficiency should be emphasized for green manufacturing.     

Simultaneous optimization of multi-response problems in the Taguchi method using genetic algorithm

The optimization of multiple responses (or performance characteristics) has received increasing attention over the last few years in many manufacturing organizations. Most previous applications of the Taguchi method only emphasize the single-response problems, while the multi-response problems have received relatively little attention. Many Taguchi practitioners have employed past experience and engineering knowledge or judgement when dealing with multiple responses. The approach presented in this paper takes advantage of both the Taguchi method and genetic algorithm, which forms a robust and practical methodology in tackling multiple response optimization problems. The paper also presents a case study to illustrate the potential of this powerful integrated approach for tackling multiple response optimization problems. The variance analysis is also an integral part of the study, which identifies the most critical and statistically significant parameters.     

Optimization of EDM process for multiple performance characteristics using Taguchi method and Grey relational analysis

Electrical discharge machining (EDM) is one of the most extensively used non-conventional material removal processes. The Taguchi method has been utilized to determine the optimal EDM conditions in several industrial fields. The method, however, was designed to optimize only a single performance characteristic. To remove that limitation, the Grey relational analysis theory has been used to resolve the complicated interrelationships among the multiple performance characteristics. In the present study, we attempted to find the optimal machining conditions under which the micro-hole can be formed to a minimum diameter and a maximum aspect ratio. The Taguchi method was used to determine the relations between machining parameters and process characteristics. It was found that electrode wear and the entrance and exit clearances had a significant effect on the diameter of the micro-hole when the diameter of the electrode was identical. Grey relational analysis was used to determine the optimal machining parameters, among which the input voltage and the capacitance were found to be the most significant. The obtained optimal machining conditions were an input voltage of 60V, a capacitance of 680pF, a resistance of 500Ω, the feed rate of 1.5μm/s and a spindle speed of 1500rpm. Under these conditions, a micro-hole of 40μm average diameter and 10 aspect ratio could be machined.     

A new upper bound solution for analysis of the radial forging process

Radial forging is an open die forging process used for reducing diameter of shafts, tubes, stepped shafts and axels, and creating the internal profiles for tubes such as rifling the gun barrels. In the present research, a new model based on calculating the deformation work was developed to find an upper bound limit for the deformation load in the case of radial forging of rods and tubes. Also, the model was used to assess the effects of the process parameters. The accuracy of the model was tested by comparing the predicted results with those achieved from the experiment at work of Uhlig [Investigation of the motions and the forces in radial swaging. Doctoral Dissertation, Technical University Hannover, 1964]. A good agreement was found between the two sets of results.     

Study of void closure in hot radial forging process using 3D nonlinear finite element analysis

Hot radial forging is used to reduce porosity and increase strength for large-diameter billets. The goal of this research is to study void closure behavior in the hot radial forging process. A nonlinear coupled finite element model is developed to investigate the deformation mechanism of internal void defects during the hot radial forging process. The model is formulated in a three-dimensional frame and a viscoplastic material model has been used to describe the material behavior subjected to large deformation and high temperature. A global–local technique is employed to obtain accurate solutions around the void region. The effects of void location, mandrel, die shape, and the reduction of the tube thickness on the final void reduction are systematically investigated. The predicted reductions for central longitudinal voids in hot upsetting and hot rolling processes are in good agreement with experimental findings. The simulation results provide a valuable procedure for the design of porosity reduction during the hot radial forging process.     

Optimization of machining parameters in rotary EDM process by using the Taguchi method

Electrical discharge machining (EDM) is one of the advanced methods of machining. Most publications on the EDM process are directed towards non-rotational tools. But rotation of the tool provides a good flushing in the machining zone. In this study, the optimal setting of the process parameters on rotary EDM was determined. A total of three variables of peak current, pulse on time, and rotational speed of the tool with three types of electrode were considered as machining parameters. Then some experiments have been performed by using Taguchi's method to evaluate the effects of input parameters on material removal rate, electrode wear rate, surface roughness, and overcut. Moreover, the optimal setting of the parameters was determined through experiments planned, conducted, and analyzed using the Taguchi method. Results indicate that the model has an acceptable performance to optimize the rotary EDM process.     

Modeling and optimization of Nd:YAG laser micro-weld process using Taguchi Method and a neural network

The use of a pulsed Nd:YAG laser in the 0.1 mm- thick aluminum alloy lap micro-weld process was optimized. The welding parameters that influence the quality of the pulsed Nd:YAG laser lap micro-weld were evaluated by measuring of the tensile-shear strength. In this work, the Taguchi method was adopted to perform the initial optimization of the pulsed Nd:YAG laser micro-weld process parameters. A neural network with a Levenberg-Marquardt back-propagation (LMBP) algorithm was then adopted to develop the relationships between the welding process parameters and the tensile-shear strength of each weldment. The optimal parameters of the pulsed Nd:YAG laser micro-weld process were determined by simulating parameters using a well-trained back-propagation neural network model. Experimental results illustrate the proposed approach.     

Optimization of green sand casting process parameters by using Taguchi’s method

This paper analyses various significant process parameters of the green sand casting process. An attempt has been made to obtain optimal settings of the green sand casting process in order to yield the optimum quality characteristics of the spheroidal graphite (SG) cast iron rigid coupling castings. The process parameters considered are: green strength, moisture content, permeability and mould hardness. The effect of selected process parameters and its levels on the casting defects and the subsequent optimal settings of the parameters have been accomplished using Taguchi’s parameter design approach. The result indicates that the selected process parameters significantly affect the casting defects of SG cast iron rigid coupling castings. The estimation of the optimum performance characteristics of green sand casting at the optimum levels of parameters is done in this paper and the results are verified by confirming with practical experiments.