A novel hybrid ICA-ANFIS model for prediction of manufacturing processes performance

Abstract

In order to reduce the manufacturing process variability and improve the production yield, it is important to predict the performance of manufacturing process. The adaptive neuro-fuzzy inference system (ANFIS) is a powerful network which can predict the output parameters of manufacturing process. However, design of ANFIS needs trial and errors to select the best structure. In this study, an imperialistic competitive algorithm has been used to determine the ANFIS architecture to reach minimum values of the prediction error. In order to evaluate the performance of this combined method, two illustrative examples of manufacturing processes have been used. Results indicated that the combined method has superiority in prediction of output, rather than previous developed ANFIS models and so it can be applied for modeling of the other manufacturing processes.

Keywords

Combined algorithm imperialistic competitive algorithm adaptive neuro-fuzzy inference system optimization

Get full access to this article

View all access options for this article.

References

Rao

. Advanced modeling and optimization of manufacturing processes: international research and development, London: Springer-Verlag, 2010.

Tsourveloudis

. Predictive modeling of the Ti6Al4V alloy surface roughness. J Intell Robot Syst 2010; 60: 513–530.

Baseri

Rahmani

Bakhshi-Jooybari

. Selection of bending parameters for minimal spring-back using an ANFIS model and simulated annealing algorithm. J Manuf Sci Eng 2011; 133: 031010-1-7.

Baseri

. Design of adaptive neuro-fuzzy inference system for estimation of grinding performance. Mater Manuf Process 2011; 26: 757–763.

Pashaie

Jafari

Baseri

. Nusselt number estimation along a wavy wall in an inclined lid-driven cavity using adaptive neuro-fuzzy inference system (ANFIS). Int J Eng A 2013; 26: 383–392.

Rao

. Engineering optimization: Theory and practice, Hoboken, NJ: John Wiley & Sons, Inc, 2009.

Teimouri

Baseri

. Artificial evolutionary approaches to produce smoother surface in magnetic abrasive finishing of hardened AISI 52100 steel. J Mech Sci Technol 2013; 27: 533–539.

Baseri

. Simulated annealing based optimization of dressing conditions for increasing the grinding performance. Int J Adv Manuf Technol 2012; 59: 531–538.

Teimouri

Baseri

. Improvement of dry EDM process characteristics using artificial soft computing methodologies. Prod Eng Res Develop 2012; 6: 493–504.

10.

Baseri

. A study of grinding wheel sharpness using neural network and fuzzy logic approaches. Int J Abrasive Technol 2010; 3: 316–337.

11.

Suresh

PVS

Venkateswara Rao

Deshmukh

. A genetic algorithmic approach for optimization of surface roughness prediction model. Int J Mach Tools Manuf 2002; 42: 675–680.

12.

Raja

Baskar

. Application of Particle Swarm Optimization technique for achieving desired milled surface roughness in minimum machining time. Exp Syst Appl 2012; 39: 5982–5989.

13.

Baseri

Bakhshi-Jooybari

Rahmani

. Modeling of spring-back in V-die bending process by using fuzzy learning back-propagation algorithm. Exp Syst Appl 2011; 38: 8894–8900.

14.

Gisario

Barletta

Conti

. Spring back control in sheet metal bending by laser-assisted bending: Experiment analysis, empirical and neural network modeling. Optics Lasers Eng 2011; 49: 1372–1383.

15.

Yaghoobi

Baseri

Bakhshi-Jooybari

. Pressure path optimization of hydrodynamic deep drawing of cylindrical-conical parts. Int J Precis Eng Manuf 2013; 14: 2095–2100.

16.

Atashpaz-Gargari

Lucas

. Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. 2007 IEEE Congress on Evolutionary Computation (CEC 2007) 2007, pp. 4661–4667.

17.

Taghavifar

Mardani

. Energy loss optimization of run-off-road wheels applying imperialist competitive algorithm. Inform Process Agric 2014; 1: 57–65.

18.

Jiang

Kwong

. Modeling customer satisfaction for new product development using a PSO-based ANFIS approach. Appl Soft Comput 2012; 12: 726–734.

19.

Chen

Lee

. An ANFIS-based model for predicting adequacy of vancomycin regimen using improved genetic algorithm. Exp Syst Appl 2011; 38: 13050–13056.

20.

Shahlaei

Madadkar-Sobhani

Saghaie

. Application of an expert system based on Genetic Algorithm–Adaptive Neuro-Fuzzy Inference System (GA–ANFIS) in QSAR of cathepsin K inhibitors. Exp Syst Appl 2012; 39: 6182–6191.

21.

Jang

J-S

. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybernet 1993; 23: 665–685.