Surface roughness prediction modelling for commercial dies using ANFIS, ANN and RSM

Hossain, Md. Shahriar Jahan; Ahmad, Nafis

doi:10.1504/ijise.2014.058834

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…However, from the tabulated results, the ANN and ANFIS models seem to be more appropriate compared to RSM models. Such results also agreed with the prior study results [ 48 ]. As the error results are within 10%, it can be claimed that the selected models are capable to accurately predict the results.…”

Section: Resultssupporting

confidence: 93%

Comparative evaluation and sensitivity analysis of multi-modelling and optimization of milling Ti–6Al–4V alloy with high-pressure coolant jets

Sultana¹,

Dhar²

2023

Heliyon

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Section: Resultssupporting

confidence: 93%

Comparative evaluation and sensitivity analysis of multi-modelling and optimization of milling Ti–6Al–4V alloy with high-pressure coolant jets

Sultana¹,

Dhar²

2023

Heliyon

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“…For developing these ANFIS models, 74 experimental data are adopted from the experiments conducted by Hossain and Ahmad [4]. At first the most influential parameters are chosen based on exhaustive search method and some statistical analysis.…”

Section: Methodsmentioning

confidence: 99%

“…This research involves the experiments [see Figure 1] conducted by Hossain and Ahmad [4] on a die material Hot die steel (H13) for ball end milling operation. There were six machining parameters concerned with that experiments-cutter axis inclination angle (φ degree), tool diameter (d mm), spindle speed (S rpm), radial depth of cut (f x mm), feed rate (f y mm/min), and axial depth of cut (t mm).…”

Section: Problem Descriptionmentioning

confidence: 99%

Cutting Parameter Optimization for End Milling OpeRation Using Advanced Metaheuristic Algorithms

Hossain¹

2017

IJARA

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In die manufacturing industries surface roughness is considered as a vital quality characteristic in order to retain the consumers' satisfaction. On the other hand, manufacturers want to minimize the machining time which eventually reduces their cost. This research deals with an optimization problem to minimize the machining time (T) for end milling operation on hot die steel (H13), subject to specified surface roughness (R a ) limits. Six machining parameters and corresponding T and R a were recorded from 74 independent experiments. After exhaustive search, three machining parameters (tool inclination angle, tool diameter and radial depth of cut) for R a and two machining parameters (feed rate and radial depth of cut) for T are found to be highly influential. In terms of these corresponding parameters, two ANFIS models are developed for the prediction of R a and T, respectively. These models are utilized to find the optimum values of machining parameters. Five advanced metaheuristic algorithms, Artificial Bee Colony (ABC), quick artificial bee colony, modified differential evolution, ant colony optimization for real numbers and simulated annealing, with or without local search, are applied for solving this optimization problem. Each of the algorithms is run for 30 times, allowing 100,000 number of function evaluations in each run. Statistical analysis (F-test, t-test) are done to evaluate the performance of the algorithms. Hybrid ABC with local search is proposed as the best algorithm for solving this problem based on average of minimum machining time obtained. The proposed optimization approach can be used for parameter selection in real time machining with artificially intelligent Computer Numerical Control (CNC) machine tools.

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“…The most frequently used models for prediction of machining performance are mathematical modelling, the regression technique and artificial intelligence (AI) technique (Cica et al, 2013). Recently, (AI)-based models, such as ANN approaches, have become the preferred trend as they are applied by most researchers to develop optimal machining conditions to predict performance measure (Hossain and Ahmad, 2014).…”

Section: Introductionmentioning

confidence: 99%

Modelling, prediction and analysis of surface roughness in turning process with carbide tool when cutting steel C38 using artificial neural network

Benamar

Noureddine

Boukezzi

et al. 2017

IJISE

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Surface roughness is a very important measurement in machining process and a determining factor describing the quality of machined surface. This research aims to analyse the effect of cutting parameters [cutting speed (v), feed rate (f) and depth of cut (d)] on the surface roughness in turning process. For that purpose, an artificial neural network (ANN) model was built to predict and simulate the surface roughness. The ANN model shows a good correlation between the predicted and the experimental surface roughness values, which indicates its validity and accuracy. A set of 27 experimental data on steel C38 using carbide P20 tool have been conducted in this study.

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Surface roughness prediction modelling for commercial dies using ANFIS, ANN and RSM

Cited by 6 publications

References 19 publications

Comparative evaluation and sensitivity analysis of multi-modelling and optimization of milling Ti–6Al–4V alloy with high-pressure coolant jets

Comparative evaluation and sensitivity analysis of multi-modelling and optimization of milling Ti–6Al–4V alloy with high-pressure coolant jets

Cutting Parameter Optimization for End Milling OpeRation Using Advanced Metaheuristic Algorithms

Modelling, prediction and analysis of surface roughness in turning process with carbide tool when cutting steel C38 using artificial neural network

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