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.     

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.     

响应面法与遗传算法相结合的注塑工艺优化

应用田口方法进行试验设计,应用计算机辅助工程技术对注塑成形过程进行了分析,建立了注塑成形工艺参数与翘曲度关系的代理模型——响应面模型,对模型进行了验证研究,将响应面法与遗传算法相结合进行了注塑工艺参数优化。结果表明,响应面模型是准确可靠的,将响应面法和遗传算法相结合,可有效提高运算速度和优化效率。     

Efficient warpage optimization of thin shell plastic parts using response surface methodology and genetic algorithm

During the production of thin shell plastic parts by injection molding, warpage depending on the process conditions is often encountered. In this study, efficient minimization of warpage on thin shell plastic parts by integrating finite element (FE) analysis, statistical design of experiment method, response surface methodology (RSM), and genetic algorithm (GA) is investigated. A bus ceiling lamp base is considered as a thin shell plastic part example. To achieve the minimum warpage, optimum process condition parameters are determined. Mold temperature, melt temperature, packing pressure, packing time, and cooling time are considered as process condition parameters. FE analyses are conducted for a combination of process parameters organized using statistical three-level full factorial experimental design. The most important process parameters influencing warpage are determined using FE analysis results based on analysis of variance (ANOVA) method. A predictive response surface model for warpage data is created using RSM. The response surface (RS) model is interfaced with an effective GA to find the optimum process parameter values.     

Experimental-based optimization of polymer injection molding process parameters using anfis-ga method

Compared with ordinary injection-molded parts, the slender, cantilevered, and thin-walled plastic parts are harsh on the injection molding process conditions. For complexity and particularity, it is difficult to form such parts. It is also more likely to cause excessive warpage deformation, affecting the molding quality and performance. The automobile audio shell is a typical slender, cantilevered, thin-walled plastic part. When the mold structure and material are determined, optimizing its injection molding process is the most economical and effective method to manufacture the products with the optimum properties. In order to minimize the warpage deformation, the adaptive network based fuzzy inference system (ANFIS) and genetic algorithm (GA) were adopted to optimize the injection molding process parameters. In particular, considering the high-dimensional nonlinear relationship between the process parameters and the warpage, the ANFIS is constructed as the prediction model of the warpage. Then, the GA is used to globally optimize the prediction model to determine the optimal process parameters. The results show that the optimization method based on ANFIS-GA has a good performance. The warpage is reduced to 0.0925 mm while reduced by 88.25 %. The optimal injection molding process parameters are used for simulation and manufacture, verifying the effectiveness and reliability of the optimization method.

     

An integrated parameter optimization system for MIMO plastic injection molding using soft computing

This study proposes an integrated optimization system to find out the optimal parameter settings of multi-input multi-output (MIMO) plastic injection molding (PIM) process. The system is divided into two stages. In the first stage, the Taguchi method and analysis of variance (ANOVA) are employed to perform the experimental work, calculate the signal-to-noise (S/N) ratio, and determine the initial process parameters. The back-propagation neural network (BPNN) is employed to construct an S/N ratio predictor and a quality predictor. The S/N ratio predictor and genetic algorithms (GA) are integrated to search for the first optimal parameter combination. The purpose of this stage is to reduce the process variance. In the second stage, the quality predictor is combined with particle swarm optimization (PSO) to find the final optimal parameters. The quality characteristics, product length and warpage, are dedicated to finding the optimal process parameters. After the numerical analysis, the optimal parameters can meet the lowest variance and the product quality requirements simultaneously. Experimental results show that the proposed optimization system can not only satisfy the quality specification but also improve stability of the PIM process.     

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-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.     

基于类神经网络注塑成型翘曲和收缩值之预测

以熔融温度、模具温度、射出时间、保压压力、保压时间等5个制程参数作为控制因子。利用Moldflow来模拟塑料薄壳挡板不同的成型制程参数下的翘曲与收缩值。基于仿真所得翘曲及收缩值数据,使用田口方法结合倒传递神经网络5-14-14-2建立预测模型。再利用测试样本来验证的倒传递神经网络模型的准确性。运用所建立的倒传递神经网络模型预测其他成型制程参数的翘曲及收缩值。结果证明,田口法结合倒传递神经网络,不仅可以有效的优化倒传递神经网络,而能成功的预测翘曲及收缩值,与Moldflow仿真值相比平均误差都在±1%内。     

基于Moldflow的薄壁件翘曲变形研究

翘曲变形是注塑件的主要缺陷,利用电器后盖对薄壁成型工艺进行研究。采用Moldflow软件对塑件成型过程进行数值模拟,研究了保压压力、塑件材料对注塑件翘曲变形的影响。对薄壁注塑件的数值仿真模拟结果进行统计分析,并且对影响注塑翘曲变形量的工艺参数进行综合分析,得到最优的工艺参数组合。研究结果表明:最佳的工艺参数组合可以使得塑件翘曲量变得最小。     

Analysis of shrinkage and warpage in an injection-molded part with a thin shell feature using the response surface methodology

This paper presents a systematic methodology to analyze the shrinkage and warpage in an injection-molded part with a thin shell feature during the injection molding process. The systematic experimental design based on the response surface methodology (RSM) is applied to identify the effects of machining parameters on the performance of shrinkage and warpage. The experiment plan adopts the centered central composite design (CCD). The quadratic model of RSM associated sequential approximation optimization (SAO) method is used to find the optimum value of machining parameters. One real case study in the injection molding process of polycarbonate/acrylonitrile butadiene styrene (PC/ABS) cell phone shell has been performed to verify the proposed optimum procedure. The mold temperature (M _T), packing time (P _t), packing pressure (P _P) and cooling time (C _t) in the packing stage are considered as machining parameters. The results of analysis of variance (ANOVA) and conducting confirmation experiments demonstrate that the quadratic models of the shrinkage and warpage are fairly well fitted with the experimental values. The individual influences of all machining parameters on the shrinkage and warpage have been analyzed and predicted by the obtained mathematical models. For the manufacture of PC/ABS cell phone shell, the values of shrinkage and warpage present the reduction of 37.8 and 53.9%, respectively, using this optimal procedure.     

Optimization of process variables for improvement of seat-backboard peel strength using response surface design method

With an increased demand for comfortable and aesthetically pleasing automobile interiors, fabric seat covers are being used more widely. Previously, covers were manufactured using adhesives attached to a molding and covered with a skin layer. However, this process releases Volatile organic compounds (VOC), pollutes the air inside the automobile, and leads to peel-strength-related problems. This study examines a multi-component injection molding process that uses residual heat during injection molding to glue the skin layer to the molding, employed in seat-backboard manufacturing. Hence, the VOC emission problem is overcome as adhesives are not employed. To obtain enhanced peel strength the optimal skin material is selected using surface-adhesion length and material peel-strength measurements. The response surface design method is utilized with a design-of-experiments method to determine the process variables that maximize the peel strength for the selected materials. The process variable selection is then confirmed via additional experiments. It is expected that the problems related to VOC emissions and peel strength, which limit current seat-backboard manufacturing techniques, can be resolved through application of the optimal conditions identified in this study to a multi-component injection molding process.

     

Parametric optimization of ultrasonic metal welding using response surface methodology and genetic algorithm

This paper focuses on the development of an effective methodology to determine the optimum welding conditions that maximize the strength of joints produced by ultrasonic welding using response surface methodology (RSM) coupled with genetic algorithm (GA). RSM is utilized to create an efficient analytical model for welding strength in terms of welding parameters namely pressure, weld time, and amplitude. Experiments were conducted as per central composite design of experiments for spot and seam welding of 0.3- and 0.4-mm-thick Al specimens. An effective second-order response surface model is developed utilizing experimental measurements. Response surface model is further interfaced with GA to optimize the welding conditions for desired weld strength. Optimum welding conditions produced from GA are verified with experimental results and are found to be in good agreement.     

Multiobjective optimization of injection molding parameters based on soft computing and variable complexity method

The objective of this study is to propose an intelligent methodology for efficiently optimizing the injection molding parameters when multiple constraints and multiple objectives are involved. Multiple objective functions reflecting the product quality, manufacturing cost and molding efficiency were constructed for the optimization model of injection molding parameters while multiple constraint functions reflecting the requirements of clients and the restrictions in the capacity of injection molding machines were established as well. A novel methodology integrating variable complexity methods (VCMs), constrained non-dominated sorted genetic algorithm (CNSGA), back propagation neural networks (BPNNs) and Moldflow analyses was put forward to locate the Pareto optimal solutions to the constrained multiobjective optimization problem. The VCMs enabled both the knowledge-based simplification of the optimization model and the variable-precision flow analyses of different injection molding parameter schemes. The Moldflow analyses were applied to collect the precise sample data for developing BPNNs and to fine-tune the Pareto-optimal solutions after the CNSGA-based optimization while the approximate BPNNs were utilized to efficiently compute the fitness of every individual during the evolution of CNSGA. The case study of optimizing the mold and process parameters for manufacturing mice with a compound-cavity mold demonstrated the feasibility and intelligence of proposed methodology.     

Application of non-dominated sorting genetic algorithm for multi-objective optimization of electrical discharge diamond face grinding process

Hybrid machining processes (HMPs), having potential for machining of difficult to machine materials but the complexity and high manufacturing cost, always need to optimize the process parameters. Our objective was to optimize the process parameters of electrical discharge diamond face grinding (EDDFG), considering the simultaneous effect of wheel speed, pulse current, pulse on-time and duty factor on material removal rate (MRR) and average surface roughness (Ra). The experiments were performed on a high speed steel (HSS) workpiece at a self developed face grinding setup on an EDM machine. All the experimental results were used to develop the mathematical model using response surface methodology (RSM). The developed model was used to generate the initial population for a genetic algorithm (GA) during optimization, non-dominated sorting genetic algorithm (NSGA-II) was used to optimize the process parameters of EDDFG process. Finally, optimal solutions obtained from pareto front are presented and compared with experimental data.     

Optimization of injection molding process parameters using integrated artificial neural network model and expected improvement function method

In this study, an adaptive optimization method based on artificial neural network model is proposed to optimize the injection molding process. The optimization process aims at minimizing the warpage of the injection molding parts in which process parameters are design variables. Moldflow Plastic Insight software is used to analyze the warpage of the injection molding parts. The mold temperature, melt temperature, injection time, packing pressure, packing time, and cooling time are regarded as process parameters. A combination of artificial neural network and design of experiment (DOE) method is used to build an approximate function relationship between warpage and the process parameters, replacing the expensive simulation analysis in the optimization iterations. The adaptive process is implemented by expected improvement which is an infilling sampling criterion. Although the DOE size is small, this criterion can balance local and global search and tend to the global optimal solution. As examples, a cellular phone cover and a scanner are investigated. The results show that the proposed adaptive optimization method can effectively reduce the warpage of the injection molding parts.     

Optimal injection molding conditions considering the core shift for a plastic battery case with thin and deep walls

The objective of this paper is to examine the influence of injection molding parameters on the core shift to obtain the optimal injection molding conditions of a plastic battery case with thin and deep walls using numerical analyses and experiments. Unlike conventional injection molding analysis, the flexible parts of the mold were represented by 3-D tetrahedron meshes to consider the core shift in the numerical analysis. The design of experiments (DOE) was used to estimate the proper molding conditions that minimize the core shift and a dominant parameter. The results of the DOE showed that the dominant parameter is the injection pressure, and the core shift decreases when the injection pressure decreases. In addition, it was shown that the initial mold temperature and the injection time hardly affect the core shift. The results of the experiments showed that products without warpage are manufactured when the injection pressure is nearly 32 MPa. Comparing the results of the analyses with those of the experiments, optimal injection molding conditions were determined. In addition, it was shown that the core shift should be considered to simulate the injection molding process of a plastic battery case with thin and deep walls.     

A combined analytical hierarchical process (AHP) and Taguchi experimental design (TED) for plastic injection molding process settings

Injection molding process is without doubt a multi-objective process if processing time, productivity, effectiveness, and the multi-criteria quality of the product are taken into consideration. Process settings affect the degree by which these objectives are realized. This work suggests a new proposal for evaluating optimal process settings through the handling of the plastic injection molding process in the same approach as a traditional multi-objective multi-criteria process. In a sense, there are numerous objective functions including cooling time, volumetric shrinkage, warpage, sink marks, residual stresses, and various process settings including temperature, pressure, etc. Within the suggested proposal, the Taguchi experimental design is used to generate a balanced set of experiments to explore the process; then, the finite element software SIMPOE is used to evaluate the behavior of the injection molding at each experimental setting. Analytical hierarchical process is then employed for multiple comparisons of the objectives and experiments as such to give the overall objective weight for each process setting (experiment). Analysis of variance is then used to evaluate the significant factors and the optimal setting of the process. This technique proved effective to obtain compliance between process design and several common manufacturer preferences, although the considered part was not changed.     

Minimization of variation in volumetric shrinkage and deflection on injection molding of Bi-aspheric lens using numerical simulation

The profile of a bi-aspheric lens is such a way that the thickness narrows down from center to periphery (convex). Injection molding of these profiles has high shrinkage in localized areas, which results in internal voids or sink marks when the part gets cool down to room temperature. This paper deals with the influence of injection molding process parameters such as mold surface temperature, melt temperature, injection time, V/P Switch over by percentage volume filled, packing pressure, and packing duration on the volumetric shrinkage and deflection. The optimal molding parameters for minimum variation in volumetric shrinkage and deflection of bi-aspheric lens have been determined with the application of computer numerical simulation integrated with optimization. The real experimental work carried out with optimal molding parameters and found to have a shallow and steep surface profile accuracy of 0.14 and 1.57 mm, 21.38-45.66 and 12.28-26.90 μm, 41.56-157.33 and 41.56-157.33 nm towards Radii of curvatures (RoC), surface roughness (Ra) and waviness of the surface profiles (profile error Pt), respectively.

     

水介质单体液压支柱Y型密封圈的CAE优化分析

介绍了水介质单体液压支柱采用的密封元件———Y型密封圈的结构与成型;针对注射成型过程中Y型密封圈出现的主要成型缺陷———缩痕,分析了其产生的原因,提出了解决措施;运用Moldflow对Y型密封圈的注射成型过程进行CAE分析;从改进注射成型工艺参数和模具结构2个方面着手,提出3种优化方案进行CAE优化分析,并将所得之最优方案付诸于实际成型过程。依据最优方案成型的Y型密封圈其缺陷得以消除,符合水介质单体液压支柱对密封元件的要求。