Application of multiple regression and adaptive neuro fuzzy inference system for the prediction of surface roughness

Kumanan, S.; Jesuthanam, C. P.; Kumar, Rishi

doi:10.1007/s00170-006-0755-4

Cited by 67 publications

(22 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where o k is the desired output and z k is the output computed by the hybrid neural net [5]. Depth of cut appears to have significant effect on tool wear.…”

Section: Bottom Neuron W 2 = a 4 (X) × B 4 (Y)×c 4 (U) × D 4 (V)mentioning

confidence: 99%

“…Five network layers were used by ANFIS to perform the following fuzzy inference steps (Fig. 4): Layer 1 -input fuzzification; Layer 2 -fuzzy set database construction; Layer 3 -fuzzy rule base construction; Layer 4 -decision making; and Layer 5 -output de-fuzzification [3][4][5]. To explain this model, two rules and two linguistic values for each input variable are suggested.…”

Section: Anfis Modellingmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive neuro-fuzzy approach to predict tool wear accurately in turning operations for maximum cutting tool utilization

Sarhan

2015

IFAC-PapersOnLine

View full text Add to dashboard Cite

In industry, the capability to predict tool wear is an essential matter as the quality and performance of a cutting tool directly affect the product quality. Developing a model to predict tool wear can help control tool wear and maximize tool utilization. Therefore, this study presents a method of predicting tool flank wear of coated carbide inserts while machining AISI 1050 low carbon steel with a turning operation. The adaptive neuro-fuzzy approach (ANFIS) was implemented in this research. Experiments were conducted based on the Design of Experiments (DOE) technique by developing experiments with four factors at four levels corresponding to the L 16 (4 4 ) experimental array to measure tool flank wear. At the end, a verification test was conducted to illustrate the effectiveness of this approach. Using ANFIS, average prediction accuracy of 92.42% was obtained and the ANFIS tool wear model developed indicated how the interaction between factors influenced tool wear.

show abstract

“…where o k is the desired output and z k is the output computed by the hybrid neural net [5]. Depth of cut appears to have significant effect on tool wear.…”

Section: Bottom Neuron W 2 = a 4 (X) × B 4 (Y)×c 4 (U) × D 4 (V)mentioning

confidence: 99%

Section: Anfis Modellingmentioning

confidence: 99%

Adaptive neuro-fuzzy approach to predict tool wear accurately in turning operations for maximum cutting tool utilization

Sarhan

2015

IFAC-PapersOnLine

View full text Add to dashboard Cite

show abstract

“…The motivation for hybridization is the technique enhancement factor, multiplicity of application tasks and realizing multi-functionality [11]. ANFIS, CANFIS and TWNFIS are of these hybrid techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Several ANFIS models for predicting in machining operations have been developed [11][12][13][14][15][16][17][18][19]. For example, Kumanan et al [11] were using adaptive ANFIS and radial basis function neural networkfuzzy logic (RBFNN-FL) for the prediction of surface roughness in end milling. Dweiri et al [12] presented a model for down milling operation of Alumic-79 using the ANFIS to predict the effect of machining variables on the surface roughness.…”

Section: Introductionmentioning

confidence: 99%

Tool flank wear prediction in CNC turning of 7075 AL alloy SiC composite

Chavoshi

2010

Prod. Eng. Res. Devel.

View full text Add to dashboard Cite

Flank wear occurs on the relief face of the tool and the life of a tool used in a machining process depends upon the amount of flank wear; so predicting of flank wear is an important requirement for higher productivity and product quality. In the present work, the effects of feed, depth of cut and cutting speed on flank wear of tungsten carbide and polycrystalline diamond (PCD) inserts in CNC turning of 7075 AL alloy with 10 wt% SiC composite are studied; also artificial neural network (ANN) and co-active neuro fuzzy inference system (CANFIS) are used to predict the flank wear of tungsten carbide and PCD inserts. The feed, depth of cut and cutting speed are selected as the input variables and artificial neural network and co-active neuro fuzzy inference system model are designed with two output variables. The comparison between the results of the presented models shows that the artificial neural network with the average relative prediction error of 1.03% for flank wear values of tungsten carbide inserts and 1.7% for flank wear values of PCD inserts is more accurate and can be utilized effectively for the prediction of flank wear in CNC turning of 7075 AL alloy SiC composite. It is also found that the tungsten carbide insert flank wear can be predicted with less error than PCD flank wear insert using ANN. With Regard to the effect of the cutting parameters on the flank wear, it is found that the increase of the feed, depth of cut and cutting speed increases the flank wear. Also the feed and depth of cut are the most effective parameters on the flank wear and the cutting speed has lesser effect.

show abstract

“…Feed exerts the strongest influence on roughness. The authors in paper [18] propose the application of two different hybrid intelligent techniques, ANFIS and radial basis function neural network-fuzzy logic (RBFNN-FL) for the prediction of surface roughness in end milling. In addition to speed, feed, depth of cut, which are frequently used as the model input parameters, the vibrations occurring during machining are also considered.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Simulation of Surface Roughness in Face Milling

Šimunović¹,

Šimunović²,

Šarić³

2013

Int. j. simul. model.

View full text Add to dashboard Cite

The paper presents a research on machined surface roughness in face milling of aluminium alloy on a low power cutting machine. Based on the results of the central-composite plan of experiment with varying machining parameters (number of revolutions -spindle speed n, feed rate f and depth of cut a) and the roughness observed as output variable, two models have been developed: a regression model and a model based on the application of neural networks (NN model). The regression model (coefficient of determination of 0.965 or 0.952 adjusted) with insignificant lack of fit, provides a very good fit and can be used to predict roughness throughout the region of experimentation. Likewise, the model based on the application of neural networks approximates well the experimental results with the level of RMS (Root Mean Square) error in the phase of validation of 4.01 %.

show abstract

Application of multiple regression and adaptive neuro fuzzy inference system for the prediction of surface roughness

Cited by 67 publications

References 18 publications

Adaptive neuro-fuzzy approach to predict tool wear accurately in turning operations for maximum cutting tool utilization

Adaptive neuro-fuzzy approach to predict tool wear accurately in turning operations for maximum cutting tool utilization

Tool flank wear prediction in CNC turning of 7075 AL alloy SiC composite

Modelling and Simulation of Surface Roughness in Face Milling

Contact Info

Product

Resources

About