scholarly journals Adaptive Network Based Fuzzy Inference System with Meta-Heuristic Optimizations for International Roughness Index Prediction

2019 ◽  
Vol 9 (21) ◽  
pp. 4715 ◽  
Author(s):  
Hoang-Long Nguyen ◽  
Binh Thai Pham ◽  
Le Hoang Son ◽  
Nguyen Trung Thang ◽  
Hai-Bang Ly ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Hybrid Models ◽  
Road Surface ◽  
International Roughness Index ◽  
Adaptive Network ◽  
Inference System ◽  
Roughness Index ◽  
Road Surface Roughness

The International Roughness Index (IRI) is the one of the most important roughness indexes to quantify road surface roughness. In this paper, we propose a new hybrid approach between adaptive network based fuzzy inference system (ANFIS) and various meta-heuristic optimizations such as the genetic algorithm (GA), particle swarm optimization (PSO), and the firefly algorithm (FA) to develop several hybrid models namely GA based ANGIS (GANFIS), PSO based ANFIS (PSOANFIS), FA based ANFIS (FAANFIS), respectively, for the prediction of the IRI. A benchmark model named artificial neural networks (ANN) was also used to compare with those hybrid models. To do this, a total of 2811 samples in the case study of the north of Vietnam (Northwest region, Northeast region, and the Red River Delta Area) within the scope of management of the DRM-I Department were used to validate the models in terms of various criteria like coefficient of determination (R) and the root mean square error (RMSE). Experimental results affirmed the potentiality and effectiveness of the proposed prediction models whereas the PSOANFIS (RMSE = 0.145 and R = 0.888) is better than the other models named GANFIS (RMSE = 0.155 and R = 0.872), FAANFIS (RMSE = 0.170 and R = 0.849), and ANN (RMSE = 0.186 and R = 0.804). The results of this study are helpful for accurate prediction of the IRI for evaluation of quality of road surface roughness.

Single Input Multi Output Adaptive Network Based Fuzzy Inference System for Machinability Data Selection in Turning Operations

2011 ◽  
Vol 383-390 ◽  
pp. 1062-1070
Author(s):  
Adeel H. Suhail ◽  
N. Ismail ◽  
S.V. Wong ◽  
N.A. Abdul Jalil
Keyword(s):  
Surface Roughness ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Cutting Parameters ◽  
Machining Process ◽  
Data Selection ◽  
Adaptive Network ◽  
Inference System ◽  
Single Input ◽  
Machinability Data

The selection of machining parameters needs to be automated, according to its important role in machining process. This paper proposes a method for cutting parameters selection by fuzzy inference system generated using fuzzy subtractive clustering method (FSCM) and trained using an adaptive network based fuzzy inference system (ANFIS). The desired surface roughness (Ra) was entered into the first step as a reference value for three fuzzy inference system (FIS). Each system determine the corresponding cutting parameters such as (cutting speed, feed rate, and depth of cut). The interaction between these cutting parameters were examined using new sets of FIS models generated and trained for verification purpose. A new surface roughness value was determined using the cutting parameters resulted from the first steps and fed back to the comparison unit and was compared with the desired surface roughness and the optimal cutting parameters ( which give the minimum difference between the actual and predicted surface roughness were find out). In this way, single input multi output ANFIS architecture presented which can identify the cutting parameters accurately once the desired surface roughness is entered to the system. The test results showed that the proposed model can be used successfully for machinability data selection and surface roughness prediction as well.

Modelling of laser processing cut quality by an adaptive network-based fuzzy inference system

2009 ◽  
Vol 223 (10) ◽  
pp. 2369-2381 ◽  
Author(s):  
Sivarao Subramonian ◽  
P Brevern ◽  
N S M El-Tayeb ◽  
V C Vengkatesh
Keyword(s):  
Surface Roughness ◽  
Pressure Vessel ◽  
Fuzzy Inference System ◽  
Laser Processing ◽  
Intelligent Systems ◽  
Fuzzy Inference ◽  
Design Parameters ◽  
Adaptive Network ◽  
Inference System ◽  
Cut Quality

Real-world problems in precision machining now require intelligent systems that integrate knowledge, techniques, and methodologies. Intelligent systems possess human-like expertise within a specific domain to adapt themselves and to learn to do better in making decisions for an intelligent manufacturing system. An intelligent tool called adaptive network-based fuzzy inference system (ANFIS) was used to model and predict the laser cut quality of a 2.5 mm manganese—molybdenum (Mn—Mo) alloy pressure vessel plate in this article. A 3 kW CO2 laser machine with seven selected design parameters was used to carry out 128 experiments based on 2 k factorial design with single replication. Because surface roughness (Ra) was the response parameter, it was targeted to be <15 μm to meet the requirement and benchmark of the pressure vessel manufacturer who sponsored this project. The DIN 2310-5 German laser cutting of metallic materials standard and procedure was referred to for evaluating surface roughness, where experimentally obtained results were used for Ra predictive modelling. Predictions of non-linear laser processing by ANFIS were found to be extremely promising in supplying the desired output, where Ra was predicted to an excellent degree of accuracy, reaching almost 70 per cent with the experimental pure error below 30 per cent.

Forward and reverse mapping of multiresponses turning process using adaptive network-based fuzzy inference system and simulated annealing algorithm

2017 ◽  
Vol 232 (10) ◽  
pp. 1746-1758 ◽  
Author(s):  
Reza Teimouri ◽  
Saeid Amini
Keyword(s):  
Surface Roughness ◽  
Cutting Force ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Turning Process ◽  
Adaptive Network ◽  
Inference System ◽  
Grey Relational Grade ◽  
Grey Relational ◽  
Reverse Mapping

Input–output relationships of a turning process have been established both in forward as well as reverse directions using adaptive network-based fuzzy inference system. Four input parameters, namely insert type, lubrication strategy, feed rate, and cutting velocity and two outputs, namely cutting force and surface roughness have been considered for the aforementioned mappings. Training and testing of the network in the forward direction were adopted by use of experimental data, which derived from high-speed turning of Monel K500 super alloy. For multiattributes reverse mapping problem, grey relational grade was firstly used to convert the cutting force and surface roughness in a single attribute problem. Then the reverse mapping was performed by the use of simulated annealing algorithm by minimizing the absolute difference between grey relational grade of specified cutting force and surface roughness and adaptive network-based fuzzy inference system model of grey relational grade, which was derived from the forward mapping. The confirmation was performed in eight benchmark tests. Results indicated that the proposed methodology can predict the input–output relationship of high-speed turning process in both forward and backward directions with error goal below 8%. The developed model was further used to find the parametric influence of the process factors on the cutting force and surface roughness.

Virtual fit evaluation of pants using the Adaptive Network Fuzzy Inference System

2021 ◽  
pp. 004051752110205
Author(s):  
Xueqing Zhao ◽  
Ke Fan ◽  
Xin Shi ◽  
Kaixuan Liu
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Test Accuracy ◽  
Adaptive Network ◽  
Data Set ◽  
Inference System ◽  
Second Stage ◽  
Simulated Environment ◽  
Two Stages ◽  
Fold Cross Validation

Virtual reality is a technology that allows users to completely interact with a computer-simulated environment, and put on new clothes to check the effect without taking off their clothes. In this paper, a virtual fit evaluation of pants using the Adaptive Network Fuzzy Inference System (ANFIS), VFE-ANFIS for short, is proposed. There are two stages of the VFE-ANFIS: training and evaluation. In the first stage, we trained some key pressure parameters by using the VFE-ANFIS; these key pressure parameters were collected from real try-on and virtual try-on of pants by users. In the second stage, we evaluated the fit by using the trained VFE-ANFIS, in which some key pressure parameters of pants from a new user were determined and we output the evaluation results, fit or unfit. In addition, considering the small number of input samples, we used the 10-fold cross-validation method to divide the data set into a training set and a testing set; the test accuracy of the VFE-ANFIS was 94.69% ± 2.4%, and the experimental results show that our proposed VFE-ANFIS could be applied to the virtual fit evaluation of pants.

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