scholarly journals Prediction of Surface Roughness in CNC Turning Process using Adaptive Neural Fuzzy Inference System

2021 ◽  
Author(s):  
Ramakrishnan A ◽  
◽  
B.Radha Krishnan ◽  
Keyword(s):  
Surface Roughness ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Mean Value ◽  
Turning Process ◽  
Inference System ◽  
Cnc Turning ◽  
Vision Measurement ◽  
Cnc Turning Process ◽  
Neural Fuzzy

This paper presents the methodology of surface roughness inspection in the CNC Turning process. Adaptive Neural Fuzzy Inference System classifier can predict the high accuracy roughness value by insisting on surface roughness image. The vision system captures the image and determines the mean value by using the ANFIS algorithm. Training sets variables speed, depth of cut, feed rate, and mean value are feed as the input, and manual stylus probe surface roughness value is feed as the output. After the simulation process, the testing input was performed, and finally getting the vision measurement value. This higher accuracy (above 95%) and low error rate (below 4%) can be achieved by using the ANFIS classifier, which is predominantly helpful for the industry to measure surface roughness. Assign the quality of the product by evaluating the manual stylus probe and vision measurement value.

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%.

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.

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.

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