A novel approach for ANFIS modelling based on Grey system theory for thermal error compensation

The fast and accurate modelling of thermal errors in machining is an important aspect for the implementation of thermal error compensation. This paper presents a novel modelling approach for thermal error compensation on CNC machine tools. The method combines the Adaptive Neuro Fuzzy Inference System (ANFIS) and Grey system theory to predict thermal errors in machining. Instead of following a traditional approach, which utilises original data patterns to construct the ANFIS model, this paper proposes to exploit Accumulation Generation Operation (AGO) to simplify the modelling procedures. AGO, a basis of the Grey system theory, is used to uncover a development tendency so that the features and laws of integration hidden in the chaotic raw data can be sufficiently revealed. AGO properties make it easier for the proposed model to design and predict. According to the simulation results, the proposed model demonstrates stronger prediction power than standard ANFIS model only with minimum number of training samples

Application of ANFIS in Predicting of TiAlN Coatings Hardness

In this paper, a new approach in predicting the hardness of Titanium Aluminum Nitrite (TiAlN) coatings using Adaptive Neuro-Fuzzy Inference System (ANFIS) is implemented. TiAlN coated cutting tool is widely used in machining due to its excellent properties. The TiAlN coatings were formed using Physical Vapor Deposition (PVD) magnetron sputtering process. The substrate sputtering power, bias voltage and temperature were selected as the input parameters and the hardness as an output of the process. A statistical design of experiment called Response Surface Methodology (RSM) was used in collecting optimized data. The ANFIS model was trained using the limited experimental data. The triangular, trapezoidal, bell and Gaussian shapes of membership functions were used for inputs as well as output. The results of ANFIS model were validated with the testing data and compared with fuzzy and nonlinear RSM hardness models in terms of the root mean square error (RMSE) and model prediction accuracy. The result indicated that the ANFIS model using 3-3-3 triangular shapes membership function obtained better result compared to the fuzzy and nonlinear RSM hardness models. The result also indicated that the ANFIS model could predict the output response in high prediction accuracy even using limited training data

Application of ANFIS in predicting TiAlN coatings flank wear

In this paper, a new approach in predicting the flank wear of Titanium Aluminum Nitrite (TiAlN) coatings using Adaptive Network Based Fuzzy Inference System (ANFIS) is implemented. TiAlN coated cutting tool is widely used in machining due to its excellent resistance to wear. The TiAlN coatings were formed using Physical Vapor Deposition (PVD) magnetron sputtering process. The substrate sputtering power, bias voltage and temperature were selected as the input parameters and the flank wear as an output of the process. A statistical design of experiment called Response Surface Methodology (RSM) was used in collecting optimized data. The ANFIS model was trained using the limited experimental data. The triangular, trapezoidal, bell and Gaussian shapes of membership functions were used for inputs as well as output. The results of ANFIS model were validated with the testing data and compared with fuzzy rule-based and RSM flank wear models in terms of the root mean square error (RMSE), coefficient determination (R2) and model accuracy (A). The result indicated that the ANFIS model using three bell shapes membership function obtained better result compared to the fuzzy and RSM flank wear models. The result also indicated that the ANFIS model could predict the output response in high prediction accuracy even using limited training data

Application of ANFIS in Predicting of TiAlN Coatings Hardness

In this paper, a new approach in predicting the hardness of Titanium Aluminum Nitrite (TiAlN) coatings using Adaptive Neuro-Fuzzy Inference System (ANFIS) is implemented. TiAlN coated cutting tool is widely used in machining due to its excellent properties. The TiAlN coatings were formed using Physical Vapor Deposition (PVD) magnetron sputtering process. The substrate sputtering power, bias voltage and temperature were selected as the input parameters and the hardness as an output of the process. A statistical design of experiment called Response Surface Methodology (RSM) was used in collecting optimized data. The ANFIS model was trained using the limited experimental data. The triangular, trapezoidal, bell and Gaussian shapes of membership functions were used for inputs as well as output. The results of ANFIS model were validated with the testing data and compared with fuzzy and nonlinear RSM hardness models in terms of the root mean square error (RMSE) and model prediction accuracy. The result indicated that the ANFIS model using 3-3-3 triangular shapes membership function obtained better result compared to the fuzzy and nonlinear RSM hardness models. The result also indicated that the ANFIS model could predict the output response in high prediction accuracy even using limited training data

Thermal error modelling of a gantry-type 5-axis machine tool using a Grey Neural Network Model

This paper presents a new modelling methodology for compensation of the thermal errors on a gantry-type 5-axis CNC machine tool. The method uses a “Grey Neural Network Model with Convolution Integral” (GNNMCI(1, N)), which makes full use of the similarities and complementarity between Grey system models and artificial neural networks (ANNs) to overcome the disadvantage of applying either model in isolation. A Particle Swarm Optimisation (PSO) algorithm is also employed to optimise the proposed Grey neural network. The size of the data pairs is crucial when the generation of data is a costly affair, since the machine downtime necessary to acquire the data is often considered prohibitive. Under such circumstances, optimisation of the number of data pairs used for training is of prime concern for calibrating a physical model or training a black-box model. A Grey Accumulated Generating Operation (AGO), which is a basis of the Grey system theory, is used to transform the original data to a monotonic series of data, which has less randomness than the original series of data. The choice of inputs to the thermal model is a non-trivial decision which is ultimately a compromise between the ability to obtain data that sufficiently correlates with the thermal distortion and the cost of implementation of the necessary feedback sensors. In this study, temperature measurement at key locations was supplemented by direct distortion measurement at accessible locations. This form of data fusion simplifies the modelling process, enhances the accuracy of the system and reduces the overall number of inputs to the model, since otherwise a much larger number of thermal sensors would be required to cover the entire structure. The Z-axis heating test, C-axis heating test, and the combined (helical) movement are considered in this work. The compensation values, calculated by the GNNMCI(1, N) model were sent to the controller for live error compensation. Test results show that a 85% reduction in thermal errors was achieved after compensation

Solar Tracking System based on Adaptive Neuro-Fuzzy Inference System (ANFIS)

Fotovoltaik panellerin güç toplama verimliliğini artırmak için genellikle güneş takip sistemleri (GTS) ile entegre edilmelidir. Bu çalışmada, uyarlamalı sinirsel bulanık çıkarım uygulaması ile GTS sunulmuştur. GTS, zenit ve azimut açılarını kontrol eden iki motora sahip çift eksenli olarak tasarlanmıştır. Bu motorların hızının kontrol edilmesi için ANFIS’in tasarlanmasından sonra bulanık mantık kontrolörünün giriş-çıkış ilişkisini öğrenmek için yapay sinir ağı eğitilmiştir. Pozisyon hatası ve hatanın değişimi modellerin girişi olarak alınmıştır. Motora uygulanan gerilim modellerin çıkışı olarak alınmıştır. ANFIS modelde, deneysel verilerden doğrudan üretilen kurallar kümesine sahip yapay sinir ağının öğrenme yeteneği ile bulanık çıkarım modeli birleştirilir. Sonuç olarak, elde edilen sonuçlar GTS için amaçlanan kontrol yaklaşımının doğru cevap ve takip etme etkinliğini doğrular.Solar tracking systems (STS) should usually be integrated with photovoltaic (PV) panel so that the photovoltaic panels can increase power collection efficiency. In this paper, STS with implementation of adaptive neuro-fuzzy inference system (ANFIS) is presented. STS designed as dual axis has two motors that control azimuth angle and zenith angle. After designing an ANFIS for controlling these motors' speed, a Neural Network is trained to learn the input–output relationship of fuzzy logic controller. Position error and error variation were taken as model’s inputs. Applied voltage to the motor was taken as model's output. The ANFIS model is combined modeling function of fuzzy inference with the learning ability of artificial neural network that has set of rules generated directly from the experimental data. Finally, the obtained results confirm the tracking efficiency and correct response of the proposed control approach for STS

QUANTIFYING THE EFFECT OF CONSTRUCTION SITE FACTORS ON CONCRETE QUALITY, COSTS AND PRODUCTION RATES

Factors affecting concrete can be categorized as structured factors or unstructured factors. The first group of factors consists of those related to the production process of concrete including water-cement ratio, properties of raw materials and mix proportions. Unstructured factors or construction site factors are related to labor skills and local conditions during the construction process of a project. Concrete compressive strength as a quality metric, costs and production rates may be affected significantly by such factors while performing concrete operations at the jobsite. Several prior studies have investigated the effect of structured factors on concrete. However literature is limited regarding the effects of unstructured factors during the construction phase of a facility. This study proposes a systematic methodology to identify and quantify the effects of construction site factors including crew experience, compaction method, mixing time, curing humidity and curing temperature on concrete quality, costs and production rates using fuzzy inference systems. First, the perceived importance of construction-related factors is identified and evaluated through literature review and a survey deployed to construction experts. Then, the theory of design of experiments (DOE) is used to conduct a full 25 factorial experiment consisting of 32 runs and 192 compressive strength tests to identify statistically significant unstructured factors. Fuzzy inference systems (FISs) are proposed for predicting concrete compressive strength, costs and production rate effects through the use of adapted network-based fuzzy inference system (ANFIS). Finally, an optimization model is formulated and tested for managing concrete during the construction process of a facility. Literature review and survey results showed that curing humidity, crew experience, and compaction method are the top three factors perceived by construction experts to affect concrete compressive strength, whereas crew experience, mixing time and compaction method are the top three factors affecting concrete costs and production rates. Additionally, crew experience, compaction and mixing time were found to dominate global ranking of perceived affecting factors through the application of the relative importance index (RII). When conducting designed experiments and analysis of variance (ANOVA), compaction method, mixing time, curing humidity and curing temperature were identified to be statistically significant construction site factors for concrete compressive strength whereas crew experience, compaction method and mixing time were statistically significant factors for cost and production rates. A Sugeno type fuzzy inference system (FIS) for quantifying compressive strength, cost and production rate effects was created by using ANFIS, having correlation coefficients (R-squared values) greater than 93%, indicating that resulting models predict new observations well. Curing temperature (i.e., on-site curing temperature) was identified to be the most affecting condition for concrete compressive strength while mixing time had the biggest impact on concrete cost and production rates. The developed FISs can be used as a decision–support tool that allows for determining desired operating conditions, that ensures specified compressive strength, saves resources and maximizes profits when fabricating, placing and curing concrete

Modeling Under MATLAB by ANFIS of Three-Phase Tetrahedral Transformer Using in Microwave Generator for Three Magnetrons Per Phase

This work deals with the modeling of a new three-phase tetrahedral transformer of HV power supply, which feeds three magnetrons per phase. The design of this new power supply is composed of three single-phase with magnetic shunt transformers coupling in star; each one is size to feed voltage-doubling cells, thereby feeds a magnetron. In order to validate the functionality of this power supply, we simulate it under Matlab-Simulink environment. Thus, we modeled nonlinear inductance using a new approach of neuro-fuzzy (ANFIS); this method based on the interpolation of the curve B(H) of ferromagnetic material, the results obtained gives forms of both voltages and currents, which shows that they are in accordance with those of experimental tests, respecting the conditions recommended by the magnetron manufacture

Development and evaluation of an adaptive neuro fuzzy interface models to predict performance of a solar dryer

 This research is carried out to predict energy efficiency of a solar dryer by adaptive neuro-fuzzy inference system (ANFIS) model. In this model, temperatures in the collector inlet, collector outlet and in the dry chamber exit and also absorbed heat energy by collector and necessary energy for evaporation of product moisture were considered as an ANFIS network inputs. To investigate the capability of ANFIS models in prediction of dryer efficiency, empirical model and regression analysis were used and their results were compared by ANFIS models. To evaluate an accuracy ANFIS models, statistical parameters such as mean absolute error, mean squared error, sum squared error, correlation coefficient (R) and probability (P) were calculated. Results indicated that coefficient of determination for ANFIS model was higher than empirical model and regression analysis whereas amounts of SSE and MSE were lower. From the results of this research, it is concluded that ANFIS model represent energy efficiency better than empirical model and regression analysis. Finally, it can be stated that the ANFIS model could be efficient in to determining the energy efficiency in a forced-convection solar dryer