Artificial intelligent modeling to predict tensile strength of inertia friction-welded pipe joints

Adaptive neural network-based fuzzy inference system (ANFIS) is an artificial intelligent neuro-fuzzy technique used for modeling and control of ill-defined and uncertain systems. The present paper proposes this novel technique of ANFIS to predict the tensile strength of inertia friction-welded tubular pipe joints with the aid of artificial neural network approach combined with the principle of fuzzy logic. The proposed model is multiple input–single output type of model which uses rotational speed and forge load as input signals. The set of rules has been generated directly from the experimental data using ANFIS. The performance of the proposed model is validated by comparing the predicted results with the actual practical results obtained by conducting the confirmation experiments. The application of χ ² test confirms that the values of tensile strength predicted by proposed ANFIS model are well in agreement with the experimental values at 0.1 % level of significance. The proposed model can also be used as intelligent online adaptive control model for pipeline welding.     

Using adaptive neuro-fuzzy inference system (ANFIS) for proton exchange membrane fuel cell (PEMFC) performance modeling

In this paper, an adaptive neuro-fuzzy inference system (ANFIS) is used for modeling proton exchange membrane fuel cell (PEMFC) performance using some numerically investigated and compared with those to experimental results for training and test data. In this way, current density I (A/cm²) is modeled to the variation of pressure at the cathode side P_C (atm), voltage V (V), membrane thickness (mm), Anode transfer coefficient α_an, relative humidity of inlet fuel RH_a and relative humidity of inlet air RH_c which are defined as input (design) variables. Then, we divided these data into train and test sections to do modeling. We instructed ANFIS network by 80% of numerical-validated data. 20% of primary data which had been considered for testing the appropriateness of the models was entered ANFIS network models and results were compared by three statistical criterions. Considering the results, it is obvious that our proposed modeling by ANFIS is efficient and valid and it can be expanded for more general states.     

Predication of concrete mix design using adaptive neural fuzzy inference systems and fuzzy inference systems

The aim of this study is to design adaptive neural-fuzzy inference system (ANFIS) model and fuzzy expert system for determination of concrete mix designs and finally compare their results. Idea of these systems based on two surveys: first, ACI structures and principles, second a concrete mix designs dataset that collected via Prof. I-Cheng Yeh. Datasets that loaded in to ANFIS has 552 mix designs and based on ACI mix designs. Moreover, in this study, we have designed fuzzy expert system. Input fields of fuzzy expert system are Slump, Maximum Size of Aggregate (D _max), Concrete Compressive Strength (CCS), and Fineness Modulus. Output fields are quantities of water, cement, fine aggregate (F.A.) and coarse aggregate (C.A.). In the ANFIS model, we have four layers (four ANFIS models): the first layer takes values of D _max and Slump and then determines the quantity of Water, the second layer takes values of Water (computed in the past layer) and CCS then measures the value of Cement, the third layer takes values of D _max and Slump to compute C.A. and the fourth layer takes values of Water, Cement, and C.A. (determined in past layers) and then measures the value of F.A. When these systems were designed and tested, comparison between two systems (FIS and ANFIS) results showed that results of ANFIS model are better than fuzzy expert system’s results. In the ANFIS model, for Water output field, training and average testing errors are 0.86 and 0.8. For cement field, training error and average testing error are in the orders of 0.21 and 0.22. Training and average testing error of C.A. are in the orders of 0.0001 and 0.0004 and finally, training and average testing errors of F.A. are in the orders of 0.0049 and 0.0063. Results of fuzzy expert system in comparison to ACI results follow average errors: average error of Water, Cement, C.A., and F.A. are in the orders of 9.5%, 27.6%, 96.5%, and 49%.     

Prediction of surface roughness and cutting zone temperature in dry turning processes of AISI304 stainless steel using ANFIS with PSO learning

This paper presents an approach for modeling and prediction of both surface roughness and cutting zone temperature in turning of AISI304 austenitic stainless steel using multi-layer coated (TiCN?+?TiC?+?TiCN?+?TiN) tungsten carbide tools. The proposed approach is based on an adaptive neuro-fuzzy inference system (ANFIS) with particle swarm optimization (PSO) learning. AISI304 stainless steel bars are machined at different cutting speeds and feedrates without cutting fluid while depth of cut is kept constant. ANFIS for prediction of surface roughness and cutting zone temperature has been trained using cutting speed, feedrate, and cutting force data obtained during experiments. ANFIS architecture consisting of 12 fuzzy rules has three inputs and two outputs. Gaussian membership function is used during the training process of the ANFIS. The surface roughness and cutting zone temperature values predicted by the PSO-based ANFIS model are compared with the measured values derived from testing data set. Testing results indicate that the predicted surface roughness and cutting zone temperature are in good agreement with measured roughness and temperature.     

基于改进自适应模糊推理系统的YG3硬质合金精密外圆磨削表面质量预测

为提高硬质合金材料精密外圆磨削的表面完整性和加工质量,研究其表面质量的预测技术,建立了基于自适应模糊推理系统(ANFIS)的YG3硬质合金精密外圆磨削表面粗糙度预测模型,并引入混合田口遗传算法(HTGA)对预测模型进行了改进。采用工艺试验中所用的磨削参数及相应条件下测得的表面粗糙度数据作为训练样本和测试样本,通过对BP神经网络模型、传统ANFIS预测模型及改进ANFIS预测模型的预测结果进行对比分析,对三种模型的有效性和预测精度进行了验证。结果表明,所提出的改进ANFIS预测模型从预测值相对误差Er的分布及均方根相对误差EMSRE的大小来看,均优于其他两种预测模型,预测精度较高,是一种有效的表面质量预测方法。      

Concentration measurement of three-phase flow based on multi-sensor data fusion using adaptive fuzzy inference system

This paper proposes a new method for the volumetric-concentration measurement of coal/biomass/air three-phase flow using multi-sensor data fusion techniques. The method integrates capacitive and electrostatic sensors and incorporates the data fusion model of an adaptive network based fuzzy inference system (ANFIS), which simulates the human׳s understanding of things. The features of the two sensor signals are extracted as the input of the ANFIS under various experimental conditions. The fusion model of the ANFIS establishes the relationship between the volumetric-concentration of the solid phase and the signal features by training with two different learning rules: the gradient descent method only and the hybrid method combining the Kalman filter algorithm with the gradient descent algorithm. Experimental results show that the ANFIS based on the hybrid learning rule outperforms the system based on the gradient descent learning rule and that the fiducial error for biomass and pulverized coal flows are 1.2% and 0.7%, respectively.     

Navigation of autonomous mobile robot using adaptive network based fuzzy inference system

Intelligent soft computing techniques such as fuzzy inference system (FIS), artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are proven to be efficient and suitable when applied to a variety of engineering systems. The hallmark of this paper investigates the application of an adaptive neuro-fuzzy inference system (ANFIS) to path generation and obstacle avoidance for an autonomous mobile robot in a real world environment. ANFIS has also taken the advantages of both learning capability of artificial neural network and reasoning ability of fuzzy inference system. In this present design model different sensor based information such as front obstacle distance (FOD), right obstacle distance (ROD), left obstacle distance (LOD) and target angle (TA) are given input to the adaptive fuzzy controller and output from the controller is steering angle (SA) for mobile robot. Using ANFIS tool box, the obtained mean of squared error (MSE) for training data set in the current paper is 0.031. The real time experimental results also verified with simulation results, showing that ANFIS consistently perform better results to navigate the mobile robot safely in a terrain populated by variety obstacles.     

Automated visual inspection for surface appearance defects of varistors using an adaptive neuro-fuzzy inference system

The purpose of this study is to develop an automated visual inspection system for analysis of the surface appearance of ring varistors based on an adaptive neuro-fuzzy inference system (ANFIS). Known image patterns of the six types of ring varistors are used in a training process to establish Sugeno FIS rules, and the input-output data are then set to train the ANFIS to tune the membership function. Feature extraction reduces image complexity using two-dimensional edge detection, calculated within divided rectangular region. The ANFIS combines the neural network adaptive capabilities and fuzzy logic qualitative to train a classification system for six different types of components. The performance of the ANFIS is evaluated in terms of training performance and classification accuracy. The results confirm that the proposed ANFIS is capable of classifying the six types of ring varistors with an accuracy of 98.67%. This paper has not been published elsewhere nor has it been submitted for publication elsewhere.     

交通环境振动测试数据中暗振动去除的ANFIS法

轨道交通引起的环境振动测试数据中混杂着暗振动的成分。提出了一种去除暗振动的自适应神经模糊推理系统(adaptive neuro-fuzzy inference system,简称ANFIS)法,阐述了其基本原理,给出了该法的具体实现步骤。通过一条列车引起的地面振动加速度时程与一条暗振动加速度时程叠加得到现场实测振动加速度时程,采用提出的ANFIS法及其他几种已有方法对该算例进行了去除暗振动的计算,并进行了对比分析。几种方法计算的时程均方根误差分别为:谱幅值修正法0.414mm/s~2,自功率谱法0.363mm/s~2,自互功率谱法0.261mm/s~2,ANFIS法0.074mm/s~2,可见,ANFIS法均方根误差最小;几种方法计算的加权振级VLz分别为:振动级修正法63.842dB,谱幅值修正法62.894dB,自功率谱法63.859dB,自互功率谱法63.802dB,ANFIS法63.805dB,ANFIS法计算结果与真实交通振动值63.815dB最接近。结果表明,在时程、傅里叶谱、功率谱密度及振动级的计算上,ANFIS法计算结果都与真实交通振动值非常接近,产生的误差比其他已有方法更小。     

Towards selective laser sintering of objects with customized mechanical properties based on ANFIS predictions

Recently, the adaptive network-based fuzzy inference system (ANFIS) has been used extensively in modeling of manufacturing processes to save both optimization time and manufacturing costs. ANFIS is a powerful iterative tool for optimizing non-linear and multivariable manufacturing operations. In the present study, ANFIS is used to predict the optimum manufacturing parameters in selective laser sintering (SLS) of cement-filled polyamide 12 (PA12) composite. For this purpose, a set of cement-filled PA12 test specimens is manufactured by SLS technique with 8 different values of laser power (4.5–8 Watt) and 8 different weight fractions of white cement (5 %–40 %). Mechanical characterization of cement-filled PA12 is carried out to evaluate the ultimate tensile strength (UTS), compressive strength, and flexural properties. The experimental data are then divided into two groups; one group for training the ANFIS model and the other group for checking the validity of the identified model. The built ANFIS model was validated experimentally and comparison with experimental results revealed mean relative errors of 2.92 %, 3.84 %, 4.75 %, and 3.31 % in the predictions of UTS, compressive strength, flexural modulus, and flexural yield strength, respectively.

     

In-situ diagnosis of vapor-compressed chiller performance for energy saving

In-situ diagnosis of chiller performance is an essential step for energy saving business. The main purpose of the in-situ diagnosis is to predict the performance of a target chiller. Many models based on thermodynamics have been proposed for the purpose. However, they have to be modified from chiller to chiller and require profound knowledge of thermodynamics and heat transfer. This study focuses on developing an easy-to-use diagnostic technique that is based on adaptive neuro-fuzzy inference system (ANFIS). The effect of sample data distribution on training the ANFIS is investigated. It is found that the data sampling over 10 days during summer results in a reliable ANFIS whose performance prediction error is within measurement errors. The reliable ANFIS makes it possible to prepare an energy audit and suggest an energy saving plan based on the diagnosed chilled water supply system.     

Management of the wind speed data using adaptive neuro-fuzzy methodology

In this paper, the accuracy of the Weibull model of wind speed is evaluated using an adaptive neuro-fuzzy inference system (ANFIS) based on wind data. The wind data comprises of wind speed measurements in the city of Nis in Serbia at different heights of 10 m, 30 m and 40 m for duration of one year. The ANFIS results are compared with the experimental results and Weibull model using root-mean-square error (RMSE), coefficient of determination, and Pearson coefficient. The effectiveness of the proposed unified strategy is verified based on the simulation results.     

Adaptive-Network-Based Fuzzy Inference System Application to Estimate the Flashover Voltage on Insulator

Abstract

This paper presents application of adaptive network based fuzzy inference system (ANFIS) to estimate critical flashover voltage on polluted insulators. Diameter, height, creepage distance, form factor and equivalent salt deposit density were used as input variables for ANFIS, and critical flashover voltage was estimated. In order to train the network and to test its performance, the data sets are derived from experimental results obtained from the literature and a mathematical model. Obtained results were given in both tabulated and graphical form for various cases studies, separately. Satisfactory and more accurate results obtained by using ANFIS to estimate the critical flashover voltage for the considered conditions compared with the previous works. Both test and validation stages were explained in detail and it is observed that estimated results rather close to experimental results.     

Prediction of flow discharge in compound open channels using adaptive neuro fuzzy inference system method

Discharge estimation in rivers is the most important parameter in flood management. Predicting discharge in the compound open channel by analytical approach leads to solving a system of complex nonlinear equations. In many complex mathematical problems that lead to solving complex problems, an artificial intelligence model could be used. In this study, the adaptive neuro fuzzy inference system (ANFIS) is used for modeling and predicting of flow discharge in the compound open channel. Comparison of results showed that the divided channel method with horizontal division lines with the Coefficient of determination (0.76) and root mean square error (0.162) is accurate among the analytical approaches. The ANFIS model with the coefficient of determination (0.98) and root mean square error (0.029) for the testing stage has suitable performance for predicting the discharge of flow in the compound open channel. During the development of the ANFIS model, found that the relative depth, ratio of hydraulics radius and ratio of the area are the most influencing parameters in discharge prediction by the ANFIS model.     

Artificial evolutionary approaches to produce smoother surface in magnetic abrasive finishing of hardened AISI 52100 steel

In this work, two models of feed forward back-propagation neural network (FFBP-NN) and adaptive neuro-fuzzy inference system (ANFIS) have been developed to predict the performance of magnetic abrasive finishing process, based on experimental data of literature [7]. Input parameters of process are electromagnet’s voltage, mesh number of abrasive particles, poles rotational speed and weight percent of abrasive particles, and also the output is percentage of surface roughness variation. In order to select the best model, a comparison between developed models has been done based on their mean absolute error (MAE) and root mean square error (RMSE). Moreover, optimization methods based on simulated annealing (SA) and particle swarm optimization (PSO) algorithms were used to maximize the percent of surface roughness variation and select the optimal process parameters. Results indicated that the models based on artificial intelligence predict much more precise values with respect to predictive regression model developed in main literature [7]. Also, the ANFIS model had a lowest value of MAE and RMSE with respect to others. So it was used as an objective function to maximize the surface roughness variation by using SA and PSO. Comparison between the obtained optimal solutions and analysis of results in main literature indicated that SA and PSO could find the optimal answers logically and precisely.     

Diagnosing planetary gear faults using the fuzzy entropy of LMD and ANFIS

The small size, low weight, and large transmission ratio of planetary gear have resulted in large-scale use, low speed, and heavy-duty mechanical systems. Poor working conditions of planetary gear lead to frequent occurrence of faults. A method is proposed for diagnosing faults in planetary gear based on fuzzy entropy of Local mean decomposition (LMD) and Adaptive neuro-fuzzy inference system (ANFIS). The original vibration signal is decomposed into six Product function (PF) components and a residual using LMD. Given that decomposed PF components contain the main fault feature information, fuzzy entropy is used to reflect the complexity and irregularity of each PF component. The fuzzy entropies of each PF component are defined as the input of the ANFIS model, and its parameters and membership functions are adaptively adjusted based on training samples. Finally, fuzzy inference rules are determined, and the optimal ANFIS model is obtained. Testing samples are used to verity the trained ANFIS model. The overall fault recognition rate reaches 88.8%, and the fault recognition rate for gear with wear reaches 96%. Therefore, the proposed method is effective at diagnosing planetary gear faults.     

A neural-network-based methodology for the prediction of surface roughness in a turning process

A neural-network-based methodology is proposed for predicting the surface roughness in a turning process by taking the acceleration of the radial vibration of the tool holder as feedback. Upper, most likely and lower estimates of the surface roughness are predicted by this method using very few experimental data for training and testing the network. The network model is trained using the back-propagation algorithm. The learning rate, the number of neurons in the hidden layer, the error goal, as well as the training and the testing dataset size, are found automatically in an adaptive manner. Since the training and testing data are collected from experiments, a data filtration scheme is employed to remove faulty data. The validation of the methodology is carried out for dry and wet turning of steel using high speed steel and carbide tools. It is observed that the present methodology is able to make accurate prediction of surface roughness by utilising small sized training and testing datasets.     

ANN-based prediction of surface and hole quality in drilling of AISI D2 cold work tool steel

This paper focuses on artificial neural network (ANN)-based modeling of surface and hole quality in drilling of AISI D2 cold work tool steel with uncoated titanium nitride (TiN) and titanium aluminum nitride (TiAlN) monolayer- and TiAlN/TiN multilayer-coated-cemented carbide drills. A number of drilling experiments were conducted at all combinations of different cutting speeds (50, 55, 60, and 65 m/min) and feed rates (0.063 and 0.08 mm/rev) to obtain training and testing data. The experimental results showed that the surface roughness (Ra) and roundness error (Re) values were obtained with the TiN monolayer- and TiAlN/TiN multilayer-coated drills, respectively. Using some of the experimental data in training stage, an ANN model was developed. To evaluate the performance of the developed ANN model, ANN predictions were compared with the experimental results. It was found that the determination coefficient values are more than 0.99 for both training and test data. Root mean square error and mean error percentage values were very low. ANN results showed that ANN can be used as an effective modeling technique in accurate prediction of the Ra and Re.     

Heat transfer correlations for air-water two-phase flow of different flow patterns in a horizontal pipe

Heat transfer coefficients were measured and new correlations were developed for two-phase heat transfer in a horizontal pipe for different flow patterns. Flow patterns were observed in a transparent circular pipe (2.54 cm I. D. and L/D=96) using an air/water mixture. Visual identification of the flow patterns was supplemented with photographic data, and the results were plotted on the How regime map proposed by Taitel and Dukler and agreed quite well with each other. A two-phase heat transfer experimental setup was built for this study and a total of 150 two-phase heat transfer data with different flow patterns were obtained under a uniform wail heat 11 ux boundary condition. For these data, the superficial Reynolds number ranged from 640 to 35,500 for the liquid and from 540 to 21,200 for the gas. Our previously developed robust two-phase heat transfer correlation for a vertical pipe with modified constants predicted the horizontal pipe air-water heat transfer experimental data with good accuracy. Overall the proposed correlations predicted the data with a mean deviation of 1.0% and an rms deviation of 12%.     

电火花加工8418钢的工艺预测模型

在电火花加工中,加工工艺指标的结果与工艺参数的设置密切相关。一般情况下,操作者在进行实际执行之前,只能根据以往的加工规律以及经验手段对其结果进行预判,达到预先评估加工结果的目的。针对这一情况,提出一种适用于电火花加工工艺指标结果预测的模型,该模型的建立是基于支持向量回归理论的数学方法,并利用遗传算法优化该方法中的各参数。以电火花加工8418模具钢为例,结合正交试验方法和经验加工方法选取加工工艺参数,并记录工艺指标结果。为保证EDM工艺指标预测模型的准确性,将试验数据随机分成训练集和测试集,利用训练集训练EDM工艺指标预测模型,可得加工时间模型均方误差T_(MSE)=0.95′10~(-4),平方相关系数T_(R2)=0.99 1;工件去除率模型均方误差MRR_(MSE)=1.02′10~(-4),平方相关系数MRR_(R2)=0.999 3;电极损耗率模型均方误差EWR_(MSE)=1.11′10~(-4),平方相关系数EWR_(R2)=0.998 9。再利用测试集验证该模型,可见预测结果与试验结果之间的误差在5%以内,从而证明电火花加工8418钢工艺预测模型的准确性和有效性。