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.     

Optimization of cryogenic cooled EDM process parameters using grey relational analysis

This paper presents an experimental investigation on cryogenic cooling of liquid nitrogen (LN₂) copper electrode in the electrical discharge machining (EDM) process. The optimization of the EDM process parameters, such as the electrode environment (conventional electrode and cryogenically cooled electrode in EDM), discharge current, pulse on time, gap voltage on material removal rate, electrode wear, and surface roughness on machining of AlSiCp metal matrix composite using multiple performance characteristics on grey relational analysis was investigated. The L₁₈ orthogonal array was utilized to examine the process parameters, and the optimal levels of the process parameters were identified through grey relational analysis. Experimental data were analyzed through analysis of variance. Scanning electron microscopy analysis was conducted to study the characteristics of the machined surface.     

基于混合算法的薄壁件铣削加工工艺参数优化

结合神经网络法和遗传算法的优点,提出了一种以倒传递神经网络法为基础的加工工艺参数优化方法,对薄壁件铣削加工工艺参数进行优化。将田口实验所得数据经倒传递神经网络进行训练与测试,来建立薄壁件铣削加工的信噪比预测器,并通过最大化信噪比,将铣削过程变异降至最低,进而找出最佳加工工艺参数组合。通过数值模拟与加工实验,验证了所提方法在薄壁件铣削加工工艺参数优化中的有效性。     

A study on optimal design of process parameters in single point incremental forming of sheet metal by combining Box–Behnken design of experiments,response surface methods and genetic algorithms

Incremental forming is a sheet metal forming process characterized by high flexibility; for this reason, it is suggested for rapid prototyping and customized products. On the other hand, this process is slower than traditional ones and requires in-depth studies to know the influence and the optimization of certain process parameters. In this paper, a complete optimization procedure starting from modeling and leading to the search of robust optimal process parameters is proposed. A numerical model of single point incremental forming of aluminum truncated cone geometries is developed by means of Finite Element simulation code ABAQUS and validated experimentally. One of the problems to be solved in the metal forming processes of thin sheets is the taking into account of the effects of technological process parameters so that the part takes the desired mechanical and geometrical characteristics. The control parameters for the study included wall inclination angle (α), tool size (D), material thickness (Th_ini), and vertical step size (In). A total of 27 numerical tests were conducted based on a 4-factor, 3-level Box–Behnken Design of Experiments approach along with FEA. An analysis of variance (ANOVA) test was carried out to obtain the relative importance of each single factor in terms of their main effects on the response variable. The main and interaction effects of the process parameters on sheet thinning rate and the punch forces were studied in more detail and presented in graphical form that helps in selecting quickly the process parameters to achieve the desired results. The main objective of this work is to examine and minimize the sheet thinning rate and the punch loads generated in this forming process. A first optimization procedure is based on the use of graphical response surfaces methodology (RSM). Quadratic mathematical models of the process were formulated correlating for the important controllable process parameters with the considered responses. The adequacies of the models were checked using analysis of variance technique. These analytical formulations allow the identification of the influential parameters of an optimization problem and the reduction of the number of evaluations of the objective functions. Taking the models as objective functions further optimization has been carried out using a genetic algorithm (GA) developed in order to compute the optimum solutions defined by the minimum values of sheet thinning and the punch loads and their corresponding combinations of optimum process parameters. For validation of its accuracy and generalization, the genetic algorithm was tested by using two analytical test functions as benchmarks of which global and local minima are known. It was demonstrated that the developed method can solve high nonlinear problems successfully. Finally, it is observed that the numerical results showed the suitability of the proposed approaches, and some comparative studies of the optimum solutions obtained by these algorithms developed in this work are shown here.     

Optimization of process parameters for recycling of mill scale using Taguchi experimenal design

The present work submits an investigation about the optimum process parameters and quality improvement of mill scale recycling. With increasing concerns on environmental issues, the recycling of materials of all types has become an important issue. In this paper, an optimization method is developed to improve quality in mill scale recycling. The optimum configuration of process parameters to achieve high metallization efficiency was determined by experiments. The Taguchi method, the signal-to-noise (S/N) ratio, the analysis of variance (ANOVA) and rsponse surface optimization are employed to find the main effects and to determine their optimum process parameters. The significant process parameters were identified and their effects on mill scale recycling were studied. Finally, a confirmation experiment with the optimal levels of the process parameters was carried out to demonstrate the effectiveness of the Taguchi method.     

A study of the Taguchi optimization method for surface roughness in finish milling of mold surfaces

The objective of this paper is to develop a Taguchi optimization method for low surface roughness in terms of process parameters when milling the mold surfaces of 7075-T6 aluminum material. Considering the process parameters of feed, cutting speed, axial-radial depth of cut, and machining tolerance, a series of milling experiments were performed to measure the roughness data. A regression analysis was applied to determine the fitness of data used in the Taguchi optimization method using milling experiments based on a full factorial design. Taguchi orthogonal arrays, signal-to-noise (S/N) ratio, and analysis of variance (ANOVA) are used to find the optimal levels and the effect of the process parameters on surface roughness. A confirmation experiment with the optimal levels of process parameters was carried out in order to demonstrate the effectiveness of the Taguchi method. It can be concluded that Taguchi method is very suitable in solving the surface quality problem of mold surfaces.     

Grey-based taguchi method for optimization of bead geometry in submerged arc bead-on-plate welding

A multi-response optimization problem has been developed in search of an optimal parametric combination to yield favorable bead geometry of submerged arc bead-on-plate weldment. Taguchi’s L25 orthogonal array (OA) design and the concept of signal-to-noise ratio (S/N ratio) have been used to derive objective functions to be optimized within experimental domain. The objective functions have been selected in relation to parameters of bead geometry viz. bead width, bead reinforcement, depth of penetration and depth of HAZ. The Taguchi approach followed by Grey relational analysis has been applied to solve this multi-response optimization problem. The significance of the factors on overall output feature of the weldment has also been evaluated quantitatively by analysis of variance method (ANOVA). Optimal result has been verified through additional experiment. This indicates application feasibility of the Grey-based Taguchi technique for continuous improvement in product quality in manufacturing industry.     

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.     

An integrated parameter optimization system for MISO plastic injection molding

This paper presents the development of a parameter optimization system that integrates mold flow analysis, the Taguchi method, analysis of variance (ANOVA), back-propagation neural networks (BPNNs), genetic algorithms (GAs), and the Davidon–Fletcher–Powell (DFP) method to generate optimal process parameter settings for multiple-input single-output plastic injection molding. In the computer-aided engineering simulations, Moldex3D software was employed to determine the preliminary process parameter settings. For process parameter optimization, an L₂₅ orthogonal array experiment was conducted to arrange the number of experimental runs. The injection time, velocity pressure switch position, packing pressure, and injection velocity were employed as process control parameters, with product weight as the target quality. The significant process parameters influencing the product weight and the signal to noise (S/N) ratio were determined using experimental data based on the ANOVA method. Experimental data from the Taguchi method were used to train and test the BPNNs. Then, the BPNN was combined with the DFP method and the GAs to determine the final optimal parameter settings. Three confirmation experiments were performed to verify the effectiveness of the proposed system. Experimental results show that the proposed system not only avoids shortcomings inherent in the commonly used Taguchi method but also produced significant quality and cost advantages.     

Multi-response optimization using the Taguchi-based grey relational analysis: a case study for dissimilar friction stir butt welding of AA6082-T6/AA5754-H111

This paper presents a multi-response optimization process for dissimilar friction stir welding of AA6082/AA5754 aluminum alloys. An L₉ orthogonal array was constituted for the experiments. Three welding parameters—tool shoulder diameter-to-pin diameter (D/d) ratio, tool rotational speed (TRS), and welding speed (WS)—were associated with tensile strength and elongation. An optimization process was started to determine the signal-to-noise (S/N) ratio. Grey relational analyses were performed utilizing the S/N ratio. According to the results of a series of analyses, the optimal welding condition was determined as 4 for D/d, 1,000 rpm for TRS, and 100 mm/min for WS. The analysis of variance results showed that all the welding parameters are statistically significant at 95 % confidence level. Additionally, the joint efficiency of welding fabricated at the optimal condition was compared for both AA6082 and AA5754. This revealed that the joint efficiency is 66 % for AA6082 and 92 % for AA5754.     

Optimization of green sand casting process parameters of a foundry by using Taguchi��s method

An optimization technique for process parameters of green sand casting of a cast iron differential housing cover based on the Taguchi parameter design approach is proposed in this paper. The process parameters considered are green strength, moisture content, pouring temperature, and mould hardness vertical and horizontal. An attempt has been made to obtain optimal level of the process parameters in order to yield the optimum quality characteristics of the cast iron differential housing cover castings. An orthogonal array, the signal-to-noise (S/N) ratio, and analysis of variance are used to analyze the effect of selected process parameters and their levels on the casting defects. The results indicate that the selected process parameters significantly affect the casting defects of grey cast iron differential housing cover castings. A confirmation run is used to verify the results, which indicated that this method is more efficient in determining the best casting parameters for differential housing cover.     

Application of Taguchi methods in the optimization of cutting parameters for surface finish and hole diameter accuracy in dry drilling processes

The aim of the work reported here was to utilize Taguchi methods to optimize surface finish and hole diameter accuracy in the dry drilling of Al 2024 alloy. The parameters of hole quality are analyzed under varying cutting speeds (30, 45, and 60 m/min), feed rates (0.15, 0.20, and 0.25 mm/rev), depths of drilling (15 and 25 mm), and different drilling tools (uncoated and TiN- and TiAlN-coated) with a 118° point angle. This study included dry drilling with HSS twist drills. The settings of the drilling parameters were determined by using Taguchi’s experimental design method. Orthogonal arrays of Taguchi, the signal-to-noise (S/N) ratio, the analysis of variance (ANOVA), and regression analyses are employed to find the optimal levels and to analyze the effect of the drilling parameters on surface finish and hole diameter accuracy values. Confirmation tests with the optimal levels of machining parameters are carried out in order to illustrate the effectiveness of the Taguchi optimization method. The validity of Taguchi’s approach to process optimization is well established.     

A study of Taguchi optimization method for identifying optimum surface roughness in CNC face milling of cobalt-based alloy (stellite 6)

The aim of this work is to develop a study of Taguchi optimization method for low surface roughness value in terms of cutting parameters when face milling of the cobalt-based alloy (stellite 6) material. The milling parameters evaluated are feed rate, cutting speed and depth of cut, a series of milling experiments are performed to measure the surface roughness data. The settings of face milling parameters were determined by using Taguchi experimental design method. Orthogonal arrays of Taguchi, the signal-to-noise (S/N) ratio, the analysis of variance (ANOVA) are employed to find the optimal levels and to analyze the effect of the milling parameters on surface roughness. Confirmation tests with the optimal levels of cutting parameters are carried out in order to illustrate the effectiveness of Taguchi optimization method. It is thus shown that the Taguchi method is very suitable to solve the surface quality problem occurring the face milling of stellite 6 material.     

Investigation of the effect of machining parameters on the surface quality of machined brass (60/40) in CNC end milling—ANFIS modeling

Brass and brass alloys are widely employed industrial materials because of their excellent characteristics such as high corrosion resistance, non-magnetism, and good machinability. Surface quality plays a very important role in the performance of milled products, as good surface quality can significantly improve fatigue strength, corrosion resistance, or creep life. Surface roughness (Ra) is one of the most important factors for evaluating surface quality during the finishing process. The quality of surface affects the functional characteristics of the workpiece, including fatigue, corrosion, fracture resistance, and surface friction. Furthermore, surface roughness is among the most critical constraints in cutting parameter selection in manufacturing process planning. In this paper, the adaptive neuro-fuzzy inference system (ANFIS) was used to predict the surface roughness in computer numerical control (CNC) end milling. Spindle speed, feed rate, and depth of cut were the predictor variables. Experimental validation runs were conducted to validate the ANFIS model. The predicted surface roughness was compared with measured data, and the maximum prediction error for surface roughness was 6.25 %, while the average prediction error was 2.75 %.     

Unified synthesis of Watt-I six-link mechanisms using evolutionary optimization

A Watt-I mechanism can operate in eight different combinations of assembly modes and output link. In this paper, a novel approach is presented for carrying out unified optimum synthesis of various combination types of Watt-I mechanism, irrespective of whether identical or different ranges of variables are specified for different combination types. By carrying out unified synthesis the less suited combination types can be identified, leading to their elimination from the synthesis process. This results in a saving of the overall computational time. The presented approach can be implemented with most of the evolutionary optimization methods. In this paper, the Differential Evolution algorithm is chosen as the optimization method. Unified optimization results are presented for two problems. The proposed approach is general and can be used, with suitable modifications, to carry out unified optimum design of alternate mechanical systems which can perform a given task.     

Strength modeling and optimizing ultrasonic welded parts of ABS-PMMA using artificial intelligence methods

This study analyzes variations in metal removal rate (MRR) and quality performance of roughness average (R _a) and corner deviation (CD) depending on parameters of wire electrical discharge machining (WEDM) process in relation to the cutting of pure tungsten profiles. A hybrid method including response surface methodology (RSM) and back-propagation neural network (BPNN) integrated simulated annealing algorithm (SAA) were proposed to determine an optimal parameter setting. The results of 18 experimental runs via a Taguchi orthogonal table were utilized to train the BPNN to predict the MRR, R _a, and CD properties. Simultaneously, RSM and SAA approaches were individually applied to search for an optimal setting. In addition, analysis of variance was implemented to identify significant factors for the processing parameters. Furthermore, the field-emission scanning electron microscope images show that a lot of built-edge layers were presented on the finishing surface after the WEDM process. Finally, the optimized result of BPNN with integrated SAA was compared with that obtained by an RSM approach. Comparisons of the results of the algorithms and confirmation experiments show that both RSM and BPNN/SAA methods are effective tools for the optimization of parameters in WEDM process.     

An experimental study on grinding parameters for manufacturing PCD micro-milling tool

Micromachining has become a necessary manufacturing process. Micro-milling tool and its evolution play a vital role in the development of micromachining. This study optimizes the grinding process of polycrystalline diamond (PCD) compact for manufacturing PCD micro-tool. The optimization is conducted by using four parameters, i.e., grain size of PCD compact, grain size of abrasive wheel, grinding speed, and feed rate designed by the Taguchi orthogonal array. The study then evaluates two grinding characteristics, i.e., grinding forces and cutting edge radius of the PCD compact. The results of ANOVA show that the most influential parameter on grinding PCD compact is the grain size of the PCD compact, followed by the grain size of the abrasive wheel, feed rate, and grinding speed. As an example, a quadrilateral PCD micro-milling tool with a cutting edge diameter of 80 μm is fabricated by using the optimized parameters.     

Realistic variant of just-in-time flowshop scheduling: integration of L p -metric method in PSO-like algorithm

This study strives to schedule a just-in-time hybrid flowshop with sequence-dependent setup times by considering two performance measures, namely makespan and sum of the earliness and tardiness, simultaneously. The paper proposes a mixed integer programming model. However, since the simpler case with a single stage and with a single machine per stage is NP-hard, the utilization of the exact algorithms for the real-life problems is limited. Thus, this paper proposes a novel solving algorithm with a weighted L _p -metric-based framework. Since the particle swarm optimization is originally designed for continuous solution space, in this study, we modify the particle position based on our representation so that a particle position is decoded into a schedule using the largest processing time algorithm, Hadamard product, and swap operator. Furthermore, we apply a variable neighborhood search and a tabu search to improve the solution quality. This hybridization which combines the advantages of the individual components is the key innovative aspect of the approach. We investigate the performance of our algorithm in the comparison with several algorithms and show that it has a good performance.     

Design of experiment based statistical approaches to optimize submerged arc welding process parameters

Joining of metals is very useful concept which is being utilized since Bronze Age and then gradual advancement gave rise to development of modern welding. And now welding is increasingly used in the fields of fabrication, manufacturing and construction. But productivity is the main concern in many manufacturing and industrial welding applications. Therefore selection of a welding process and its variables/parameters without sacrificing weld quality with respect to productivity and its quality is very important because an optimum blend of parameters which inevitably develop minimum or no defect will result in high productivity. For this study Submerge Arc Welding (SAW) process is selected for optimization because this versatile welding process is the first choice whenever good productivity with high quality requires in fabrication and manufacturing of Marine & pressure vessels, pipelines and offshore structures. Here Signal to noise (S/N) ratio analyses are used to find significant effects of key parameters on selected responses and then for optimization design of experiment based both quality loss function (OFM) and desirability function along with variance analyses by ANOVA are utilized. Moreover codes and standards provide a range for weld process parameters but author experienced that still there is a window to further optimize these parameters to produce the quality weld. Therefore this study is also useful to contribute in welding related research work by enhancing the knowledge of welding process and its analysis by utilizing advance statistical optimization techniques to find optimum zone within the acceptable zone from Code & Standard based tolerance Zone.     

A study on fiber laser micro-spot welding of thin stainless steel using response surface methodology and simulated annealing approach

This study analyzed variations of shear strength that depend on the fiber laser process during micro-spot welding of AISI 304 stainless thin sheets. A preliminary study used ANSYS results to obtain initial process conditions. The experimental plan was based on a Taguchi orthogonal array table. A hybrid method that includes the response surface methodology (RSM)- and back propagation neural network (BPNN)- integrated simulated annealing algorithm (SAA) is proposed to search for an optimal parameter setting of the micro-spot welding process. In addition, an analysis of variance was implemented to identify significant factors influencing the micro-spot welding process parameters, which was also used to compare the results of BPNN-integrated SAA with the RSM approach. The results show that the RSM and BPNN/SAA methods are both effective tools for the optimization of micro-spot welding process parameters. A confirmation experiment was also conducted in order to validate the optimal welding process parameter values.