Cutting performance of glass-vinyl ester composite by abrasive water jet

Polymer matrix composite materials have been increasingly used in aerospace, defense, automotive and marine industries. In these fields, nontraditional machining method of abrasive water jet (AWJ) has been used significantly in order to form polymer matrix components. In this study, glass fiber reinforced vinyl ester composite plates have been investigated under various AWJ cutting parameters by using the Taguchi experimental design in detail. For Taguchi experimental design, experimental parameters of standoff distance, abrasive mass flow rate, traverse speed, pressure and material thickness were determined at three levels. Top kerf width and the surface roughness were investigated in order to understand the cutting performance. Finally, linear regression models were conducted and all performance parameters were examined using analysis of variance (ANOVA) and main effects plots. According to the overall test results, standoff distance was determined as the most effective one. The optimal parameter levels were obtained by the ‘main effects plots’, and finally, the predictive modeling was validated by performing the optimal combination of parameter levels.     

Prediction and control of surface roughness for the milling of Al/SiC metal matrix composites based on neural networks

In recent years, there has been a significant increase in the utilization of Al/SiC particulate composite materials in engineering fields, and the demand for accurate machining of such composite materials has grown accordingly. In this paper, a feed-forward multi-layered artificial neural network (ANN) roughness prediction model, using the Levenberg-Marquardt backpropagation training algorithm, is proposed to investigate the mathematical relationship between cutting parameters and average surface roughness during milling Al/SiC particulate composite materials. Milling experiments were conducted on a computer numerical control (CNC) milling machine with polycrystalline diamond (PCD) tools to acquire data for training the ANN roughness prediction model. Four cutting parameters were considered in these experiments: cutting speed, depth of cut, feed rate, and volume fraction of SiC. These parameters were also used as inputs for the ANN roughness prediction model. The output of the model was the average surface roughness of the machined workpiece. A successfully trained ANN roughness prediction model could predict the corresponding average surface roughness based on given cutting parameters, with a 2.08% mean relative error. Moreover, a roughness control model that could accurately determine the corresponding cutting parameters for a specific desired roughness with a 2.91% mean relative error was developed based on the ANN roughness prediction model. Finally, a more reliable and readable analysis of the influence of each parameter on roughness or the interaction between different parameters was conducted with the help of the ANN prediction model.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-020-00326-x     

Optimization of microstructural and mechanical properties of friction stir welding using the cellular automaton and Taguchi method

In this study, microstructural and mechanical properties of friction stir welding (FSW) of AA1100 is optimized using Taguchi L9 orthogonal design of experiments. First, in order to study the microstructural properties of the weld, microstructure evolution of the weld zone is simulated with the cellular automaton (CA) method coupling the modified Laasraoui–Jonas (LJ) model. Then, the microstructural simulation results were validated by obtained experimental results. Good agreements between the simulation and the experimental results were observed. Then, tensile and hardness test were done to investigate the mechanical properties of the weld. The design parameters considered in the experiment were rotational speed, traverse speed, and shoulder diameter. The optimum process parameters were determined with reference to grain size (GS), ultimate tensile strength (UTS) and hardness. The predicted optimal value of grain size, ultimate tensile strength and hardness was validated by conducting the confirmation test using optimum parameters. Analysis of variance was done in order to determine the most dominant factors in friction stir welding.     

Surface roughness modelling in hard turning operation of AISI 4140 using CBN cutting tool

In this study, the influence of hardness (H) and spindle speed (N) on surface roughness (Ra) in hard turning operation of AISI 4140 using CBN cutting tool has been studied. A multiple regression analysis using analysis of variance is conducted to determine the performance of experimental values and to show the effect of hardness and spindle speed on the surface roughness. Artificial neural network (ANN) and regression methods have been used for modelling of surface roughness in hard turning operation of AISI 4140 using CBN cutting tool. The input parameters are selected to be as hardness and spindle speed and the output is the surface roughness. Regression and artificial neural network optimum models have been presented for predicting surface roughness. The predicted surface roughness by the employed models has been compared with the experimental data which shows the preference of ANN in prediction of surface roughness during hard turning operation. Finally, a reverse ANN model is constructed to estimate the hardness and spindle speed from surface roughness values. The results indicate that the reverse ANN model can predict hardness for the train data and spindle speed for the test data with a good accuracy but the predicted spindle speed for the train data and the predicted hardness for the test data don’t have acceptable accuracy.     

An investigation on the optimum conditions of synthesizing a magnetite based ferrofluid as MRI contrast agent using Taguchi method

In this study, some stabilized magnetite based ferrofluids were synthesized using Dextran as a stabilizing agent. In order to achieve optimum experimental conditions for synthesizing ferrofluids as MRI contrast agents, the Taguchi method was used. This approach was employed to design and minimize the number of required experiments. By using the Taguchi orthogonal (L16) array, four parameters including solution temperature and alkalinity, reaction temperature and stirring rate were selected at four predetermined levels for 16 experiments. Synthesizing processes established based on this set of experimental conditions were carried out and the obtained ferrofluids were characterized using PCS, VSM, TEM and FT-IR techniques. The obtained results were used and analyzed through the Qualitek-4 software and the proposed optimum experimental conditions were used for synthesizing the desired sample. Finally, this sample was used as a potential MRI contrast agent for imaging lymph nodes.     

Giant Magnetoimpedance Effect: Concept and Prediction in Amorphous Materials

Giant magneto impedance (GMI) effect was experimentally measured on as-cast, post-production and coated with chemical technique amorphous wire and ribbon materials consisted of varied chemical composition over a frequency range from 0.1 to 8 MHz under a static magnetic field between ?8 and +8 kA/m. The results show that each amorphous sample has a certain operational frequency for which the GMI effect has maximum magnitude and the other parameters such as annealing and coating have a significant influence on the GMI effect. It is believed that the domain structure and wall mechanism in the material are responsible for this behaviour. A 3-node input layer, 1-node output layer artificial neural network (ANN) model with three hidden layers including 30 neurons and full connectivity between the nodes was developed. A total of 1600 input vectors obtained from varied treated samples was available in the training data set. After the network was trained, better results were obtained from the network formed by the hyperbolic tangent transfer function in the hidden layers, there was a sigmoid transfer function in the output layer and we predicted the GMI. Comparing the predicted values obtained from the ANN model with the experimental data indicates that a well-trained neural network model provides very accurate results.     

Warpage and Shrinkage Optimization of Injection-Molded Plastic Spoon Parts for Biodegradable Polymers Using Taguchi,ANOVA and Artificial Neural Network Methods

In this study, it is attempted to give an insight into the injection processability of three self-prepared polymers from A to Z. This work presents material analysis, injection molding simulation, design of experiments alongside considering all interaction effects of controlling parameters carefully for green biodegradable polymeric systems, including polylactic acid(PLA), polylactic acid-thermoplastic polyurethane(PLA-TPU) and polylactic acid-thermoplastic starch(PLA-TPS). The experiments were carried out using injection molding simulation software Autodesk Moldflow?in order to minimize warpage and volumetric shrinkage for each of the mentioned systems. The analysis was conducted by changing five significant processing parameters, including coolant temperature, packing time, packing pressure, mold temperature and melt temperature. Taguchi's L27(35) orthogonal array was selected as an efficient method for design of simulations in order to consider the interaction effects of the parameters and reduce spurious simulations. Meanwhile, artificial neural network(ANN) was also used for pattern recognition and optimization through modifying the processing conditions. The Taguchi coupled analysis of variance(ANOVA) and ANN analysis resulted in definition of optimum levels for each factor by two completely different methods. According to the results, melting temperature, coolant temperature and packing time had significant influence on the shrinkage and warpage. The ANN optimal level selection for minimization of shrinkage and/or warpage is in good agreement with ANOVA optimal level selection results. This investigation indicates that PLA-TPU compound exhibits the highest resistance to warpage and shrinkage defects compared to the other studied compounds.     

Confirmation testing of the Taguchi methods by artificial neural-networks simulation

In Taguchi's methods of parameter design, a confirmation test is usually necessary to remove concerns about the choice of control parameters, experimental design, or assumptions about responses. This paper investigated the use of artificial neural-networks simulation to validate the set of control parameters identified as significant through Taguchi's methods, and to verify that the recommended settings for the control parameters are indeed optimal or near-optimal. Using the experimental layout and measured responses from a Taguchi parameter-design experiment, we applied cross-validate training to ascertain that the trained neural-network can reproduce acceptable results on unseen experimental layouts. We then used the trained neural-network to simulate and search for the global optimal settings for the control parameters, and the results compared with the recommended settings from the Taguchi parameter-design experiment.     

Optimization of chemical composition for TC11 titanium alloy based on artificial neural network and genetic algorithm

It is quite difficult for materials to develop the quantitative model of chemical elements and mechanical properties, because the relationship between them presents the multivariable and non-linear. In this work, the combined approach of artificial neural network (ANN) and genetic algorithm (GA) was employed to synthesize the optimum chemical composition for satisfying mechanical properties for TC11 titanium alloy based on the large amount of experimental data. The chemical elements (Al, Mo, Zr, Si, Fe, C, O, N and H) were chosen as input parameters of the ANN model, and the output parameters are mechanical properties, including ultimate tensile strength, yield strength, elongation and reduction of area. The fitness function for GA was obtained from trained ANN model. It is found that the percentage errors between experimental and predicted are all within 5%, which suggested that the ANN model has excellent generalization capability. The results strongly indicated that the proposed optimization model offers an optimal chemical composition for TC11 titanium alloy, which implies it is a novel and effective approach for optimizing materials chemical composition.     

Systematic development of pH-independent controlled release tablets of carvedilol using central composite design and artificial neural networks

The purpose of this study was to apply the optimization method incorporating artificial neural network (ANN) using pH-independent release of weakly basic drug, carvedilol from HPMC-based matrix formulation. Because of weakly basic nature of carvedilol, drug shows pH-dependent solubility. The enteric polymer EUDRAGIT L100 was added formulations to overcome pH-dependent solubility of carvedilol. Effects of the Hydroxypropylmethyl cellulose (HPMC) K4M and EUDRAGIT L100 amount on drug release were investigated. For this purpose 13 kinds of formulations were prepared at three different levels of each variables. The optimization of the formulation was evaluated by using ANN method. Two formulation parameters, the amounts of HPMC K4M and Eudragit L100 at three levels (?1, 0, 1) were selected as independent/input variables. In-vitro dissolution sampling times at twelve different time points were selected as dependent/output variables. By using experimental dissolution results and amount of HPMC K4M and EUDRAGIT L100, percentage of dissolved carvedilol was predicted by ANN. Similarity factor (f₂) between predicted and experimentally observed profile was calculated and f₂ value was found 76.33. This value showed that there was no difference between predicted and experimentally observed drug release profile. As a result of these experiments, it was found that ANNs can be successfully used to optimize controlled release drug delivery systems.     

Turning of Stainless Steel with Chamfered Main Cutting Edge Tools

A force model for a single-point tool with the chamfered main cutting edge was proposed in this present study which could consequently result in tools being manufactured which produce a built-up edge (BUE). Special tool holders and their geometrical configuration were designed and are discussed in the initial stages of this paper. The tool holders were milled using medium-carbon steel bars and these holders with the mounting tips were ground to fit various specifications. The experimental results correlated closely with the theoretical values predicted by the model. Cutting experiments were conducted on stainless steel and carbon steel for studying the mechanism of the secondary chip; in addition, the ideal tool geometrical configurations of the relationship between the shapes of the secondary chip and tool geometries were observed.     

Prediction of permeability coefficients of compounds through caco-2 cell monolayer using artificial neural network analysis

Artificial neural network (ANN) analysis was used to predict the permeability of selected compounds through Caco-2 cell monolayers. Previously reported models, which were shown to be useful in the prediction of permeability values, use many structural parameters. More complex equations have also been proposed using both linear and non-linear relationships, including ANN analysis and various structural parameters. But proposed models still need to be developed using different neuron patterns for more precise predictions and a better understanding of which factors affect the permeation. To develop a simple and useful model or method for easy prediction is also a general need. Permeability coefficients (log k_p) were obtained from various literature sources. Some structural parameters were calculated using computer programs. Multiple linear regression analysis (MLRA) was used to predict Caco-2 cell permeability for the set of 50 compounds (r² = 0.403). A successful ANN model was developed, and the ANN produced log kp values that correlated well with the experimental ones (r² = 0.952). The permeability of a compound, famotidine, which has not previously been studied, through the Caco-2 cell monolayer was investigated, and its permeability coefficient determined. It was then possible to compare the experimental data with that predicted using the trained ANN with previously determined Caco-2 cell permeability values and structural parameters of compounds. The model was also tested using literature values. The developed and described ANN model in this publication does not require any experimental parameters; it could potentially provide useful and precise prediction of permeability for new drugs or other penetrants.     

Influence of process parameters on cutting force and torque during drilling of glass–fiber polyester reinforced composites

This research outlines the Taguchi optimization methodology, which is applied to optimize cutting parameters in drilling of glass fiber reinforced composite (GFRC) material. Analysis of variance (ANOVA) is used to study the effect of process parameters on machining process. This procedure eliminates the need for repeated experiments, time and conserves the material by the conventional procedure. The drilling parameters and specimen parameters evaluated are speed, feed rate, drill size and specimen thickness. A series of experiments are conducted using TRIAC VMC CNC machining center to relate the cutting parameters and material parameters on the cutting thrust and torque. The measured results were collected and analyzed with the help of the commercial software package MINITAB14. An orthogonal array, signal-to-noise ratio are employed to analyze the influence of these parameters on cutting force and torque during drilling. The method could be useful in predicting thrust and torque parameters as a function of cutting parameters and specimen parameters. The main objective is to find the important factors and combination of factors influence the machining process to achieve low cutting low cutting thrust and torque. From the analysis of the Taguchi method indicates that among the all-significant parameters, speed and drill size are more significant influence on cutting thrust than the specimen thickness and the feed rate. Study of response table indicates that the specimen thickness, and drill size are the significant parameters of torque. From the interaction among process parameters, thickness and drill size together is more dominant factor than any other combination for the torque characteristic.     

Developing a model for hardness prediction in water-quenched and tempered AISI 1045 steel through an artificial neural network

The aim of the current study was to develop an artificial neural network (ANN) model to predict the hardness drop of the water-quenched and tempered AISI 1045 steel specimens, as a function of tempering temperature and time parameters. In the first stage, the effects of selected tempering parameters on the hardness drop value were investigated. In the second stage, a group of data, which have been obtained from experiments, was used for training of the ANN model. Likewise, another group of experimental data was utilized for the ANN model validation. Ultimately, maximum error of the ANN prediction was determined. The agreement between the predicted values of the ANN model with the experimental data was found to be reasonably good.     

切削参数的两阶段优化系统

以汽车轴承支架轴承孔的精镗切削为例,综合运用田口方法、灰色关联度分析、倒传递神经网络和粒子群算法等相关技术,对切削参数进行两阶段优化,迅速有效地找出最佳切削参数组合,不仅保证加工精度符合多目标要求,而且提升产品的稳定性。通过分析和验证表明,该方法实用有效,为提高机械加工企业的市场竞争力开辟了一种新的思路和途径。     

A comparative study of multiple regression analysis and back propagation neural network approaches on plain carbon steel in submerged-arc welding

Weld bead plays an important role in determining the quality of welding particularly in high heat input processes. This research paper presents the development of multiple regression analysis (MRA) and artificial neural network (ANN) models to predict weld bead geometry and HAZ width in submerged arc welding process. Design of experiments is based on Taguchi’s L16 orthogonal array by varying wire feed rate, transverse speed and stick out to develop a multiple regression model, which has been checked for adequacy and significance. Also, ANN model was accomplished with the back propagation approach in MATLAB program to predict bead geometry and HAZ width. Finally, the results of two prediction models were compared and analyzed. It is found that the error related to the prediction of bead geometry and HAZ width is smaller in ANN than MRA.     

Artificial neural network modelling to predict hot deformation behaviour of as HIPed FGH4169 superalloy

Abstract

The hot deformation behaviour of as HIPed FGH4169 superalloy was studied by single stroke compression test on MMS-200 test machine at the temperatures of 950–1050°C and the strain rates of 0·004–10 s^?1. Based on the experimental results, a back-propagation artificial neural network model and constitutive equation method were established to predict the flow stress of FGH4169 superalloy. The predictability of two different models was compared. The correlation coefficients of experimental and predicted flow stress with the trained BP ANN model and constitutive equation were 0·9995 and 0·9808 respectively. The average root mean square error (RMSE) values of the trained ANN model and constitutive equation are 0·39 and 2·21 MPa respectively. And the average absolute relative error (AARE) values of the trained ANN model and constitutive equation are 1·79 and 7·47% respectively. The results showed that the ANN model is an effective tool to predict the flow stress in comparison with constitutive equation.     

Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations

This work was aimed at determining the feasibility of artificial neural networks (ANN) by implementing backpropagation algorithms with default settings to generate better predictive models than multiple linear regression (MLR) analysis. The study was hypothesized on timolol-loaded liposomes. As tutorial data for ANN, causal factors were used, which were fed into the computer program. The number of training cycles has been identified in order to optimize the performance of the ANN. The optimization was performed by minimizing the error between the predicted and real response values in the training step. The results showed that training was stopped at 10?000 training cycles with 80% of the pattern values, because at this point the ANN generalizes better. Minimum validation error was achieved at 12 hidden neurons in a single layer. MLR has great prediction ability, with errors between predicted and real values lower than 1% in some of the parameters evaluated. Thus, the performance of this model was compared to that of the MLR using a factorial design. Optimal formulations were identified by minimizing the distance among measured and theoretical parameters, by estimating the prediction errors. Results indicate that the ANN shows much better predictive ability than the MLR model. These findings demonstrate the increased efficiency of the combination of ANN and design of experiments, compared to the conventional MLR modeling techniques.     

人工神经网络在玻璃配方设计中的应用研究

应用人工神经网络技术,采用Neuralworks Predict软件建立BP网络模型,通过对R2O-MO-Al2O3-SiO2系统玻璃组成与热膨胀系数关系实验数据的训练,以期能预测该系统指定组成的玻璃的热膨胀系数?研究结果表明,所建立的神经网络模型能较正确地反映玻璃氧化物组成与其热膨胀系数之间的规律性。模型对给定组成玻璃热膨胀系数的预测值与实际测试值的相对误差在6．4％以内,表明由神经网络技术建立的这一模型能正确反映R2O-MO-Al2O3-SiO2系统玻璃组成与热膨胀系数间的内在规律性。     

Machining error compensation using radial basis function network based on CAD/CAM/CAI integration concept

This paper presents a machining error compensation methodology using an Artificial Neural Network (ANN) model trained by an inspection database of the On-Machine-Measurement (OMM) system. This is an application of the CAD/CAM/CAI integration concept. First, to improve machining and inspection accuracies, the geometric errors of a three-axis CNC machining centre and the probing errors are compensated using a closed-loop configuration. Then, a workpiece is machined using the machining centre, and the error distributions of the machined surface are inspected using OMM. In order to analyse efficiently the machining errors, two characteristic error parameters, W err and D err , are defined. Subsequently, these parameters are modelled using a Radial Basis Function (RBF) network approach as an ANN model. Based on the RBF network model, the tool path is corrected to effectively reduce the errors using an iterative algorithm. In the iterative algorithm, the changes of the cutting conditions can be identified according to the corrected tool path. In order to validate the approaches proposed in this paper, an experimental machining process is performed, and the results are evaluated. As a result, about 90% of machining error reduction can be achieved through the proposed approaches.