Study of plasma- and detonation gun-sprayed alumina coatings using taguchi experimental design

Atmospheric plasma spraying (APS) is a most versatile thermal spray method for depositing alumina (Al₂O₃) coatings, and detonation gun (D-gun) spraying is an alternative thermal spray technology for depositing such coatings with extremely good wear characteristics. The present study is aimed at comparing the characteristics of Al₂O₃ coatings deposited using the above techniques by using Taguchi experimental design. Alumina coating experiments were conducted using a Taguchi fractional-factorial (L₈) design parametric study to optimize the spray process parameters for both APS and D-gun. The Taguchi design evaluated the effect of four APS and D-gun spray variables on the measured coating attributes. The coating qualities evaluated were surface roughness, porosity, microhardness, abrasion, and sliding wear. The results show that the coating quality is directly related to the corresponding coating microstructure, which is significantly influenced by the spray parameters employed. Though it is evident that the D-gun-sprayed coatings consistently exhibit dense and uniform microstructure, higher hardness, and superior tribological performance, the attainment of suitable plasma-sprayed coatings can be improved by employing the Taguchi analysis.     

The application of the artificial neural network and taguchi method to process sequence design in metal forming processes

This study shows the correlation between the design methodology of the artificial neural network (ANN) and the statistical design of experiments (DOE) approach and is demonstrated for process design in various metal forming processes. After investigating the effect of each parameter upon the characteristics by the Taguchi method which is one of the DOE, orthogonal array (OA) table and characteristics are applied to ANN as experimental data and then opiimal design parameters are established. Using the rigid plastic FEM, the simulations are performed and the results of ANN are confirmed. This technique requires smaller runs than the conventional method to find the optimal condition of design parameters for the design’s aim. This new technique can be used in a wide range of metal forming process designs.     

A hybrid approach for processing parameters optimization of Ti-22Al-25Nb alloy during hot deformation using artificial neural network and genetic algorithm

In the present investigation, isothermal compression tests of Ti-22Al-25Nb alloy were carried out under various hot deformation conditions, including the deformation temperature range of 940–1060 °C and the strain rate range of 0.01–10 s^?1. The constitutive relationship of Ti-22Al-25Nb alloy was developed using artificial neural network (ANN). During training process, standard error back-propagation algorithm was employed in the network model using experimental data sets. Based on the fitness function obtained from established ANN model, the optimization model of hot processing parameters for Ti-22Al-25Nb alloy was successfully created using genetic algorithm (GA). The optimal results achieved from the integrated ANN and GA optimization model were tested by using processing map. Consequently, it can be suggested that the combined approach of ANN and GA provides a novel way with respect to the optimization of processing parameters in the field of materials science.     

基于BP神经网络的新零件材料消耗定额预测方法研究

为精确地预测熔模铸造中新零件材料消耗定额，采用了BP神经网络的方法进行建模。在分析影响各工序零件材料消耗主要因素的基础上，确定了BP神经网络模型的特征参数，并根据实际情况确定了输入层和隐含层的神经元个数，从而确定了模型的结构。用试验数据对模型结构进行训练，最终建立了一个用于新零件材料消耗定额预测的BP神经网络模型。     

Design of Nano-Micro-Composite Ceramic Tool and Die Material with Back Propagation Neural Network and Genetic Algorithm

An algorithm combined with back propagation neural network (BPNN) and genetic algorithm (GA) was used in the optimum design of the compositions of an advanced ZrO₂/TiB₂/Al₂O₃ nano-micro-composite ceramic tool and die materials. GA was used to fully optimize the network topology, thresholds, and initial connection weights of BPNN. The input parameters are the contents of each compositions of ceramic tool and die materials and the output parameters are mechanical properties including hardness, flexural strength, and fracture toughness. The compositions with optimum mechanical properties can be chosen for materials preparation with less error and the result can be used to guide the experimental process. As a result, the nano-micro-composite ceramic tool and die material with good mechanical properties was then fabricated. It indicated that the algorithm can offer a robust and efficient way for the compositional design of ceramic tool and die materials.     

A design of experiment study of plasma-sprayed alumina-titania coatings

This article presents an experimental study of the plasma spraying of alumina- titania powder. This powder system is being used to fabricate heater tubes that emulate nuclear fuel tubes for use in thermal-hydraulic testing. Coating experiments were conducted using a Taguchi fractional- factorial design parametric study. Operating parameters were varied around the typical spray parameters in a systematic design of experiments to display the range of plasma processing conditions and their effect on the resultant coating. The coatings were characterized by hardness and electrical tests, image analysis, and optical metallography. Coating qualities are discussed with respect to dielectric strength, hardness, porosity, surface roughness, deposition efficiency, and microstructure. The attributes of the coatings are correlated with the changes in operating parameters.     

基于实验设计的电火花微小孔加工参数优化研究

综合考虑电火花微小孔加工中伺服参数与电参数的影响，采用实验设计的方法对微小孔加工效率进行优化研究。首先以一次正交试验对小孔加工中的微进给速度进行优化，以获得稳定快速的伺服响应，在此基础上进行二次正交试验，采用田口方法确定电参数的最佳水平组合。经参数优化后，小孔加工时间由初始的137．2s缩短到67．9s，加工效率显著提高。     

A taguchi experimental design study of twin-wire electric arc sprayed aluminum coatings

An experimental study was conducted on the twin- wire electric arc spraying of aluminum coatings. This aluminum wire system is being used to fabricate heater tubes that emulate nuclear fuel tubes for use in thermal-hydraulic experiments. Experiments were conducted using a Taguchi fractional factorial design parametric study. Operating parameters were varied around the typical process parameters in a systematic design of experiments to display the range of processing conditions and their effect on the resultant coating. The coatings were characterized by hardness tests, optical metallography, and image analysis. This article discusses coating hardness, roughness, deposition efficiency, and microstructure. The study attempts to correlate the features of the coatings with the changes in operating parameters. A numerical model of the process is presented, including gas, droplet, and coating dynamics.     

基于正交试验的气辅成型工艺优化

以加强筋为研究对象,对气辅成型中的熔体温度、熔体预注射量、气体注射压力、气体注射延迟时间、气体注射时间等影响气辅成型质量的主要参数进行数值模拟,并对影响塑件成型质量的相关工艺参数进行正交试验。先通过初步的五因素四水平L16(45)正交试验获得工艺参数的大致范围,然后在该基础上再进行改进的四因素三水平L9(34)正交试验,最终获得较好的工艺条件。     

A Taguchi and experimental investigation into the optimal processing conditions for the abrasive jet polishing of SKD61 mold steel

This study introduces an abrasive jet polishing (AJP) technique in which the pneumatic air stream carries not only abrasive particles, but also an additive of either pure water or pure water with a specified quantity of machining oil. Taguchi design experiments are performed to identify the optimal AJP parameters when applied to the polishing of electrical discharge machined SKD61 mold steel specimens. A series of experimental trials are then conducted using the optimal AJP parameters to investigate the respective effects of the additive type and the abrasive particle material and diameter in achieving a mirror-like finish of the polished surface. The Taguchi trials indicate that when polishing is performed using pure water as an additive, the optimal processing parameters are as follows: an abrasive material to additive ratio of 1:2, an impact angle of 30°, a gas pressure of 4 kg/cm², a nozzle-to-workpiece height of 10 mm, a platform rotational velocity of 200 rpm, and a platform travel speed of 150 mm/s. Applying these processing parameters, it is found that the optimal polishing effect is attained using #8000SiC abrasive particles and a 1:1 mixture of water-solvent machining oil and pure water. The experimental results show that under these conditions, the average roughness of the electrical discharge machined SKD61 surface is reduced from an original value of R_a=1.03 μm (R_max: 7.74 μm) to a final value of R_a=0.13 μm (R_max: 0.90 μm), corresponding to a surface roughness improvement of approximately 87%.     

STUDY ON PROPERTY PREDICTION FOR SEALING ALLOYS

ＳＴＵＤＹＯＮＰＲＯＰＥＲＴＹＰＲＥＤＩＣＴＩＯＮＦＯＲＳＥＡＬＩＮＧＡＬＬＯＹＳ￥Ｚ．Ｎ．Ｘｉａ;Ｓ．Ｇ．Ｌａｉ;Ｙ．Ｚ．ＳｕｎａｎｄＹ．Ｗ．Ｌｕ（ＤｅｐａｒｔｍｅｎｔｏｆＭａｔｅｒｉａｌｓＳｃｉｅｎｃｅａｎｄＥｎｇｉｎｅｅｒｉｎｇ,Ｔｓｉｎｇｈｕ...     

Application of artificial neural network and Taguchi method to preform design in metal forming considering workability

This study describes a new method of perform design in muti-stage metal forming processes considering workability limited by ductile fracture. The finite element simulation combined with ductile fracture criterion has been performed in order to predict ductile fracture. The artificial neural network using the Taguchi method has been implemented for minimizing objective functions relevant to the forming process. The combinations of design parameters used in finite element simulation are selected by orthogonal array in statistical design of experiments. The orthogonal array and the result of simulation are used as train data for artificial neural networks. The cold heading process is taken as an example of designing preforms which do not form any fracture in the finished product. The results of analysis to validate the proposed design method are presented.     

基于遗传神经网络的等离子喷涂纳米ZrO2-7%Y2O3涂层工艺参数优化

将BP神经网络和遗传算法相结合用于等离子喷涂纳米ZrO2-7％ Y2O3涂层的工艺参数优化,根据正交试验结果对模型结构进行训练,建立了喷涂距离、喷涂电流、主气压力、辅气压力与涂层结合强度和显微硬度之间的BP神经网络模型,并基于遗传算法对涂层结合强度和显微硬度进行了单目标和多目标参数优化.结果表明,模型预测值与试验值十分接近,说明该网络模型是正确和可靠的.遗传算法优化的涂层最大结合强度和显微硬度(HV)分别为44.0 MPa和12.663 GPa;当涂层结合强度和显微硬度两个性能参数权重相同时,在喷涂距离90.66 mm、喷涂电流934.63 A、主气压力0.304MPa和辅气压力0.898 MPa时涂层综合性能最优.     

基于Taguchi方法的低压铸造铝合金车轮工艺优化

4／t用铸造模拟软件预测低压铸造铝合金车轮缩孔缩松缺陷，凝固时间、缩松缺陷等级作为质量指标，利用Taguchi方法优化低压铸造铝合金车轮工艺参数。Taguchi方法的目的是使用最少的试验次数获得铸件质量指标变化最小的最优化的工艺组合。研究了模具温度和浇注温度对缩松缺陷等级的影响。以某型车轮为例证明该方法的有效性，改变了长期以来铸造工艺完全靠经验获得的状况。     

基于人工智能算法的最优加工表面粗糙度预测研究

以切削速度、进给量、切削深度、刀尖圆弧半径为设计变量,采用正交试验法进行了立方氮化硼(CBN)刀具干式车削冷作模具钢Cr12MoV的试验研究。利用神经网络的非线性拟合能力和遗传算法的全局寻优能力,建立了加工表面粗糙度预测模型并获得了使表面粗糙度达到最优的切削用量与刀尖圆弧半径组合。利用遗传算法获得的最优表面粗糙度值比田口方法和切削试验所获得的最佳表面粗糙度值分别降低了7.1%和17.2%。文中所采用的方法也为切削加工中刀具磨损、切削力和残余应力等问题的建模与参数优化提供理论参考。     

Design of a fuzzy logic approach based on genetic algorithms for robust plasma-sprayed zirconia depositions

This paper proposes a design for an adaptive system by modeling the relationship between coating surface roughnesses and the controling factors in plasma spray coating processes. A statistical design was used to obtain sufficient experimental information with the least number of experiments. Analysis of variance was then used to select significant control factors for reinforced coatings, and these identified factors used to construct an adaptive fuzzy logic control model. In order to model the process, a fuzzy logic controller (FLC) was utilized. A genetic algorithm (GA) was applied as a tool to optimize rule bases from traditional FLCs. Therefore, with the use of a GA-optimized FLC, robust reinforced deposition for coatings in the plasma spraying process can be obtained. The experimental results show that the obtained optimal rule base for FLC is capable of achieving the desired results. That is to say, the proposed design, which combines a statistical method and a GA-optimized FLC, is efficient and robust for the investigation of reinforced coatings in a plasma spraying process.     

The investigation on the prediction of tool wear and the determination of optimum cutting conditions in machining 17-4PH stainless steel

The purpose of this paper is to develop a predictive model for the prediction of tool flank wear and an optimization model for the determination of optimum cutting conditions in machining 17-4PH stainless steel. The back-propagation neural network (BPN) was used to construct the predictive model. The genetic algorithm (GA) was used in the optimization model. The Taguchi method (TM) was used to find the optimum parameters for both models, respectively. Two steps of experiments have been carried out by machining 6 mm length and 90 mm length of the workpiece, respectively. The experimental scheme was arranged by using an orthogonal array of TM. It has been shown that the predictive model is capable of predicting the tool flank wear in an agreement behavior. The optimization model has also been proved that it is a convenient and efficient method to find the optimum cutting conditions associated with the maximum metal removal rate (MMRR) under different constraints. The constraint is the tool flank wear that can be determined from the predictive model. Furthermore, the systematic procedure to develop the models in this paper can be applied to the usage of the predictive or optimized problems in metal cutting.     

基于神经网络的焊机参数预测方法

为获得高质量的7075-T6/AZ31B异种合金Zn中间层-超声辅助FSLW接头,通过RBF-遗传算法对转速、焊接速度、Zn中间层厚度及超声功率四种工艺参数进行了优化. 结果表明,经过训练的RBF神经网络满足预测精度要求;将其与遗传算法相结合,在经多次迭代后可获得最优工艺参数组合. 取可执行最优解转速1 037 r/min、焊接速度82 mm/min、Zn层厚度0.04 mm和超声功率1 440 W进行试验验证,焊接接头拉剪载荷达到8 860 N,与已报道最优接头相比提高11.4%. RBF神经网络与遗传算法相结合的人工智能优化方法可准确预测并优化接头质量,且具有较大的时间及经济优势.     

电火花线切割5083Al合金的实验研究和参数优化(英文)

通过实验分析,优化电火花线切割5083 Al合金的工艺参数。基于Taguchi优化法,将脉宽、脉间、峰电流和线张力作为输入参数进行了系列实验。将表面粗糙度和切割速度作为响应,基于信噪比,测定了输入参数对响应的影响。通过Taguchi优化方法,得到了最大切割速度和最小表面粗糙度的最佳加工参数。再采用附加的模型预测可能的加工组合。最后,通过使用Pareto优化法,得到一个简明的工艺参数表。     

基于RBF-GA的铝/镁异材FSLW工艺参数优化

为获得高质量的7075-T6/AZ31B异种合金Zn中间层-超声辅助FSLW接头,通过RBF-遗传算法对转速、焊接速度、Zn中间层厚度及超声功率四种工艺参数进行了优化. 结果表明,经过训练的RBF神经网络满足预测精度要求;将其与遗传算法相结合,在经多次迭代后可获得最优工艺参数组合. 取可执行最优解转速1 037 r/min、焊接速度82 mm/min、Zn层厚度0.04 mm和超声功率1 440 W进行试验验证,焊接接头拉剪载荷达到8 860 N,与已报道最优接头相比提高11.4%. RBF神经网络与遗传算法相结合的人工智能优化方法可准确预测并优化接头质量,且具有较大的时间及经济优势.