Machine assisted manual torch operation: system design, response modeling, and speed control

Manual GTAW is commonly used in industry especially for applications which require high weld quality. Unfortunately, skills needed for critical welding operations typically require a long time to develop. In this paper a control system was developed to assist the welder in regulating his/her arm movement (i.e., the welding speed) in real-time. Training experiments are conducted by a human welder, whose movement speed (system output) is accurately tracked by a Leap motion sensor. Visual commands (arrows with both direction and amplitude) are displayed on a monitor, which is used as system input. Modeling trials suggest that Moving Average (MA) models are not sufficient in capturing the human movement dynamics. Auto-Regressive Moving Average (ARMA) models are then identified using Least Squares (LS), and F-test is used to select the model order. Pole placement controller is proposed to control the human welder arm movement. Controller simulation shows that the proposed controller is able to drive the system to desired speed with no static error and acceptable regulating speed. To further demonstrate the effectiveness of the proposed controller, tracking experiments are conducted by a human welder to track various set-points. It is observed that with the proposed pole placement controller, the welder can track the desired speed with acceptable accuracy. The proposed welding speed assistance system may be beneficial to develop the next generation intelligent welding machines.

In this paper, we compare and analyze the prediction performance of existing ANFIS (Adaptive Neuro Fuzzy Inference System) models. The ANFIS model is a fusion of neural networks with learning ability, adaptability, and computational ability, and a fuzzy system with human-like thinking ability and reasoning ability. In this paper, we analyze the prediction performance of ANFIS models using different input space partitioning methods. ANFIS 1 uses SC (Subtractive Clustering), a clustering method based on the density of data, to calculate the center of the cluster by calculating potential values inversely proportional to the distances of the different inputs. ANFIS 2 uses Fuzzy C-Means (FCM) clustering, which is a clustering method based on degree of belonging to data, and classifies each data belonging to each cluster according to degree of affiliation. ANFIS 3 is a method of using Context based Fuzzy C-Means (CFCM) clustering, which is a clustering method considering characteristics of input and output space based on FCM clustering. It creates clusters for each context generated according to characteristics of output space, to classify the data. To evaluate these ANFIS models, we use short term electricity price forecasting data and Boston housing data. The prediction performance is based on the Root Mean Squared Error (RMSE) method. Experimental results show that the prediction performance of the ANFIS model is different depending on the characteristics of each data. Furthermore, we confirmed that the prediction performance of ANFIS model using CFCM clustering method is constant.

In this paper, a hybrid meta-heuristic artificial intelligence (AI) method, named “ANFIS–PSOGA,” is developed for modeling the discharge coefficient of circular side orifices for the first time. For this purpose, the adaptive neuro fuzzy inference system (ANFIS) network is optimized by means of two robust optimization algorithms: the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm. Furthermore, the Monte Carlo simulations are utilized to enhance the performance of the AI hybrid model. Besides, the k-fold cross-validation method is employed in order to verify the numerical results. Initially, the performance of three fuzzy inference system (FIS) generations of the ANFIS model including grid partitioning, subtractive clustering and fuzzy C-means clustering are evaluated. After that, using the parameters affecting the discharge coefficient, eleven ANFIS models are defined and then the best ANFIS model and the most influencing input parameters are identified through the conduction of a sensitivity analysis. For the superior ANFIS model, mean absolute percentage error, root-mean-square error and the correlation coefficient (R) are obtained 0.021, 0.020 and 0.871, respectively. Also, the Froude number (Fr) is detected as the most effective input parameter. Moreover, the discharge coefficient of the circular side orifices and the flow field within main channels along the side orifices are simulated through the adoption of a computational fluid dynamics (CFD) model. Additionally, the flow field at three different cross sections (beginning, middle and end of orifice) along the side weir is evaluated. For instance, by advancing the flow toward the side orifice, the Froude number is reduced. Besides, at each cross section, the maximum shear stress is estimated to occur in the vicinity of the crest of the circular side orifice. According to the CFD results, the maximum lateral velocity is estimated near the circular side orifice. In addition, the results yielded by the ANFIS, ANFIS–PSO, ANFIS–PSOGA and CFD models are compared and an uncertainty analysis is carried out for the models. Based on the comparison, ANFIS–PSOGA has the best performance in simulating the discharge coefficient. Ultimately, a simple and practical computer code for the best model (ANFIS–PSOGA) is proposed which can be easily used by scholars and engineers in practical usages.

In complex industrial processes (CIPs), due to technical and economic limitations, key performance indicators (KPIs), especially the chemical content-related KPIs, are often difficult to measure in real time, which hinders the propagation of advanced process control technologies. This paper presents a soft sensor-based online KPI inference scheme by a state transition algorithm (STA)-optimized adaptive pre-sparse neuro-fuzzy inference system model, called STA-APSNFIS. It introduces a pre-sparse neural network to the traditional adaptive neuro-fuzzy inference system (ANFIS) model to establish an adaptive pre-sparse neuro-fuzzy inference system (APSNFIS) model to alleviate the adverse effects of data redundancy and noise interference in the detectable process monitoring data, which can effectively reduce the complexity of neuro-fuzzy inference system (NFIS) and speed up its convergence. Successively, to avoid being trapped at a local optimum, the STA-based optimization algorithm is adopted to replace the traditional gradient-based optimization approach to achieve an optimal APSNFIS model. Extensive validation and comparative experiments on nonlinear numeric simulation systems, benchmark Tenessee Eastman (TE) process and a real industrial bauxite flotation process demonstrated that the proposed STA-APSNFIS performed favorably against traditional ANFIS model as well as its variants, e.g., PSO-ANFIS, GA-ANFIS, and some other soft sensor-based KPI inference models.

The energy and draft requirements of a disk plow have been recognized as essential factors when attempting to correctly match it with tractor power. This study examines the possible of using an adaptive neuro-fuzzy inference system (ANFIS) approach and its performance compared to a multiple linear regression (MLR) model to determine the energy and draft requirements of a disk plow. A total of 133 data patterns were obtained by conducting experiments in the field and from the literature. Of these 133 data points, 121 were arbitrarily selected and used for training, and the remaining 12 were used for testing the models. The input variables were plowing depth, plowing speed, soil texture index, initial soil moisture content, initial soil bulk density, disk diameter, disk angle, and disk tilt angle, and output variable was draft of the disk plow. Four membership functions were used with ANFIS: a triangular membership function, generalized bell-shaped membership function, trapezoidal membership function, and Gaussian curve membership function. An evaluation of the outcomes of the ANFIS and MLR modeling shows that the triangular membership function performed better than the other functions. When the ANFIS model draft predictions were compared to the measured values, the average relative error was –1.97%. A comparison of the ANFIS model with other approaches showed that the energy and draft requirements of the disk plow could be estimated with satisfactory accuracy. Keywords: ANFIS, MLR, disk plow, draft, tillage DOI: 10.25165/j.ijabe.20201302.4077 Citation: Al-Dosary N M N, Al-Hamed S A, Aboukarima A M. Application of adaptive neuro-fuzzy inference system to predict draft and energy requirements of a disk plow. Int J Agric & Biol Eng, 2020; 13(2): 198–207.

Electrochemical-kinetics, MD-simulation and multi-input single-output (MISO) modeling using adaptive neuro-fuzzy inference system (ANFIS) prediction for dexamethasone drug as eco-friendly corrosion inhibitor for mild steel in 2 M HCl electrolyte

Currently, manufacturing industries focus on intelligent manufacturing. Prediction and monitoring of tool wear are essential in any material removal process, and implementation of a tool condition monitoring system (TCMS) is necessary. This work presents the flank wear prediction during the hard turning of EN8 steel using the deep learning (DL) algorithm. The turning operation is conducted with three levels of selected parameters. CNMG 120408 grade, TiN‐coated cemented carbide tool is used for turning. Cutting force and flank wear are assessed under dry‐cutting conditions. DL algorithms such as adaptive neuro‐fuzzy inference system (ANFIS) and convolutional auto encoder (CAE) are used to predict the flank wear of the single‐point cutting tool. The DL model is developed with turning parameters and cutting force to predict flank wear. The different ANFIS and CAE models are employed to develop the prediction model. Grid‐based ANFIS structure with Gauss membership function performed better than ANFIS models. The ANFIS model’s average testing error of 0.0074011 mm and prediction accuracy of 99.81% are achieved.

Comparison of ANFIS and NN models—With a study in critical buckling load estimation

River flow forecasting is an essential procedure that is necessary for proper reservoir operation. Accurate forecasting results in good control of water availability, refined operation of reservoirs and improved hydropower generation. Therefore, it becomes crucial to develop forecasting models for river inflow. Several approaches have been proposed over the past few years based on stochastic modeling or artificial intelligence (AI) techniques. In this article, an adaptive neuro-fuzzy inference system (ANFIS) model is proposed to forecast the inflow for the Nile River at Aswan High Dam (AHD) on monthly basis. A major advantage of the fuzzy system is its ability to deal with imprecision and vagueness in inflow database. The ANFIS model divides the input space into fuzzy sub-spaces and maps the output using a set of linear functions. A historical database of monthly inflows at AHD recorded over the past 130 years is used to train the ANFIS model and test its performance. The performance of the ANFIS model is compared to a recently developed artificial neural networks (ANN) model. The results show that the ANFIS model was capable of providing higher inflow forecasting accuracy specially at extreme inflow events compared with that of the ANN model. It is concluded that the ANFIS model can be quite beneficial in water management of Lake Nasser reservoir at AHD.

The shear strength (Su) of soils is one of the most widely used parameters for designing structures safely, where Su is found with the unconfined compression test (UCS). Although UCS can be acquired by performing uniaxial compression test it would be extremely helpful to predict the UCS without performing any compression test, namely, using computational methods considering different parameters of soils such as consistency limits, fine grain ratio, liquidity index, and void ratio. The goal of present work is predicting UCS taking into account these soil parameters with the aid of developed Adaptive Neuro-Fuzzy Inference System (ANFIS) model and the Multiple Linear Regression (MLR) analyses. On the other hand, the effect of the size of the training set of designed models on the results is examined, also. For this aim, four different models composed of different training and test set ratios have been constructed and analyzed using ANFIS and MLR. It is concluded that UCS can be predicted using MLR analysis and ANFIS model with best 0.76 and 0.91 values of determination coefficient (R2) around the x = y line respectively, and the effect of the size of the training set of models on ANFIS is more pronounced than MLR models.

Precise control of electro-hydraulic actuator (EHA) system has been an interesting subject due to its nonlinearities and uncertainties characteristics. Good control can be designed when precise model of the system is available. ANFIS (Adaptive Neuro-Fuzzy Inference System) modeling technique has proven to be able to model various nonlinear systems. The objective of this paper is to obtain an ANFIS model from EHA system stimulus-response data. The prediction ability of ANFIS is also tested using limited data set for model training. Results show that ANFIS model is able to estimate nonlinear EHA system with high precision, even when training data provided is limited.

ANFIS for analysis friction factor and Nusselt number of pulsating nanofluids flow in the fluted tube under magnetic field

ABSTRACTAsphaltene precipitation is introduced as a seriously problematic issue in the petroleum reservoirs that causes filling porosity of rocks and reduction of oil production. Hence, estimating the amount of precipitated asphaltene has huge importance in preventing the deposition of asphaltene. The present study was done to estimate the precipitated asphaltene as a function of temperature, dilution ratio, and molecular weight of different n-alkanes using adaptive neuro-fuzzy inference system (ANFIS). Moreover, another new scaling model was also developed to compare with the ANFIS model. In addition, these two developed models have been compared with previously developed correlations. The obtained values of R2 for the ANFIS and scaling models were 0.9912 and 0.9862, respectively. These tools are simple to use and can be used as an accurate approximation of the precipitated asphaltene as a function of temperature, dilution ratio, and molecular weight of different n-alkanes.

Objective: To evaluate the ability of prediction performance of ANFIS (Adaptive Neuro Fuzzy Inference System) model on groundwater level fluctuation in Lower Bhavani River Basin (LBRB). Method: To improve the accuracy of prediction of ANFIS model, WT (Wavelet Transform) and CWTFT (Continuous Wavelet Fast Fourier Transform) are performed as preprocessing techniques. Model development is carried out through three different input parameters (duration, groundwater recharge and groundwater discharge) and one output parameter (groundwater fluctuation) for the period of 2009-2015 on monthly stress basis. Findings: Based on the comparative prediction by the models CWTFT-ANFIS and WTANFIS over conventional ANFIS model through calibration and validation process on different reaches of the study area, optimum model is identified. Statistical indices are performed to measure the best fitting and trend identification for the each prediction. Keywords: ANFIS, CWTFT, Fluctuation, Groundwater, Lower Bhavani River Basin, Wavelet

This paper presents the possibility of using Adaptive Neuro Fuzzy Inference System for Power Transformer Paper Condition Assessment. The dielectric characteristics, dissolved gasses, and furan of 108 running transformers is collected. The 2-furaldehyde (2FAL) data is transformed to Degree of Polymerization (DP), and then statistically analysed to get independent variables as the predictor for the transformer paper condition assessment. CO and CO2 are well known as one of the product of cellulose degradation, while interfacial tension, acidity, and color from the oil are statistically correlated with furan. ANFIS (Adaptive Neuro-Fuzzy Inference System) and Multiple Regression (MR) model is built based on the previous statistical analysis, and then the result is evaluated and compared, resulting in better accuracy of ANFIS model. Three different evaluation criteria MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Square Error) calculated from ANFIS prediction are lower than those from MR model, with the MAPE of ANFIS model is 15.38%.