Accurate estimation of surface roughness from texture features of the surface image using an adaptive neuro-fuzzy inference system

2005 ◽  
Vol 29 (1) ◽  
pp. 95-100 ◽  
Author(s):  
Kuang-Chyi Lee ◽  
Shinn-Jang Ho ◽  
Shinn-Ying Ho
Keyword(s):  
Surface Roughness ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Texture Features ◽  
Accurate Estimation ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Surface Image

scholarly journals Identifikasi Citra Digital Jenis Beras Menggunakan Metode Anfis dan Sobel

2021 ◽  
Vol 7 (2) ◽  
pp. 123-128
Author(s):  
Gansar Suwanto ◽  
Riza Ibnu Adam ◽  
Garno
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Texture Features ◽  
Image Method ◽  
Inference System ◽  
Neuro Fuzzy ◽  
System Method ◽  
Sobel Edge Detection ◽  
The Many ◽  
Shape Characteristics

Rice is one of the leading national food products and superior agricultural products in Indonesia. The many types of rice in Indonesia make it increasingly difficult to distinguish rice by just relying on the eye. Because each type of rice has relatively different shape and texture characteristics. Therefore, digital images can be used as a first step in identifying types of rice. This study aims to identify the types of rice using image processing. Taking the value of the shape characteristics using the morphology method and compared with the sobel method. While taking the value of the texture features using the grayscale image method. Then, the value of the shape and texture do the grouping according to the type of rice. The data used in this study were 140 images. 100 of the 140 images were conducted training using the ANFIS (Adaptive Neuro Fuzzy Inference System) method by utilizing the value of the shape and texture of the image. The test was carried out 5 times using 140 images. The test results using the ANFIS (Adaptive Neuro Fuzzy Inference System) method by 85.2%. Meanwhile, sobel edge detection can affect accuracy by 3%.

scholarly journals Estimation of Heating Temperature for Fire-Damaged Concrete Structures Using Adaptive Neuro-Fuzzy Inference System

Materials ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 3964 ◽  
Author(s):  
Hyun Kang ◽  
Hae-Chang Cho ◽  
Seung-Ho Choi ◽  
Inwook Heo ◽  
Heung-Youl Kim ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Heating Temperature ◽  
Concrete Structures ◽  
Estimation Methods ◽  
Accurate Estimation ◽  
Temperature Estimation ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Heating Test

The structural performance of concrete structures subjected to fire is greatly influenced by the heating temperature. Therefore, an accurate estimation of the heating temperature is of vital importance for deriving a reasonable diagnosis and assessment of fire-damaged concrete structures. In current practice, various heating temperature estimation methods are used, however, each of these estimation methods has limitations in accuracy and faces disadvantages that depend on evaluators’ empirical judgments in the process of deriving diagnostic results from measured data. Therefore, in this study, a concrete heating test and a non-destructive test were carried out to estimate the heating temperatures of fire-damaged concrete, and a heating temperature estimation method using an adaptive neuro-fuzzy inference system (ANFIS) algorithm was proposed based on the results. A total of 73 datasets were randomly extracted from a total of 87 concrete heating test results and we used them in the data training process of the ANFIS algorithm; the remaining 14 datasets were used for verification. The proposed ANFIS algorithm model provided an accurate estimation of heating temperature.

Adaptive Neuro-Fuzzy Inference System Modelling of Surface Roughness in High Speed Turning of AISI P 20 Tool Steel

2011 ◽  
Vol 314-316 ◽  
pp. 341-345
Author(s):  
Bo Di Cui
Keyword(s):  
Experimental Data ◽  
Surface Roughness ◽  
Tool Steel ◽  
Fuzzy Inference System ◽  
High Speed ◽  
Fuzzy Inference ◽  
System Modelling ◽  
Inference System ◽  
High Speed Turning ◽  
Neuro Fuzzy

Accurate predictive modelling is an essential prerequisite for optimization and control of production in modern manufacturing environments. In this paper, an adaptive neuro-fuzzy inference system (ANFIS) model was developed to predict the surface roughness in high speed turning of AISI P 20 tool steel. Experiments were designed and performed to collect the training and testing data for the proposed model based on orthogonal array. For decreasing the complexity of the ANFIS structure, principal component analysis (PCA) was used to deal with the experimental data. The comparison between predictions and experimental data showed that the proposed method was both effective and efficient for modelling surface roughness.

Surface Roughness Prediction in High Speed End Milling Using Adaptive Neuro-Fuzzy Inference System

2015 ◽  
Vol 1115 ◽  
pp. 122-125
Author(s):  
Muataz Hazza F. Al Hazza ◽  
Amin M.F. Seder ◽  
Erry Y.T. Adesta ◽  
Muhammad Taufik ◽  
Abdul Hadi bin Idris
Keyword(s):  
Surface Roughness ◽  
Fuzzy Inference System ◽  
High Speed ◽  
Fuzzy Inference ◽  
End Milling ◽  
Machining Process ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Roughness Prediction ◽  
Coated Carbide

One of the significant characteristics in machining process is final quality of surface. The best measurement for this quality is the surface roughness. Therefore, estimating the surface roughness before the machining is a serious matter. The aim of this research is to estimate and simulate the average surface roughness (Ra) in high speed end milling. An experimental work was conducted to measure the surface roughness. A set of experimental runs based on box behnken design was conducted to machine carbon steel using coated carbide inserts. Moreover, the Adaptive Neuro-Fuzzy Inference System (ANFIS) has been used as one of the unconventional methods to develop a model that can predict the surface roughness. The adaptive-network-based fuzzy inference system (ANFIS) was found to be capable of high accuracy predictions for surface roughness within the range of the research boundaries.

Computer vision measurement and optimization of surface roughness using soft computing approaches

2020 ◽  
Vol 42 (13) ◽  
pp. 2475-2481 ◽  
Author(s):  
Radha Krishnan Beemaraj ◽  
Mathalai Sundaram Chandra Sekar ◽  
Venkatraman Vijayan
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Soft Computing ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Optimization Technique ◽  
Inference System ◽  
Random Forest Classification ◽  
Vision Measurement ◽  
Neuro Fuzzy

This paper proposes an efficient methodology for predicting surface roughness using different soft computing approaches. The soft computing approaches are artificial neural network, adaptive neuro-fuzzy inference system and genetic algorithm. The proposed surface roughness prediction procedure has the following stages as feature extraction from the materials, classifications using random forests, adaptive neuro-fuzzy inference system (ANFIS). In this paper, the statistical features are extracted from material images as skewness, kurtosis, mean, variance, contrast, and energy.The surface roughness accuracy value varied between ANFIS and random forest classification in every measurement sequence. This limitation can be overcome by the genetic algorithm to optimize the best results. The optimization technique can produce more accurate surface roughness results for more than 98% and reduce the error rate up to 0.5%.

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