FEM/AI models for the simulation of precision grinding

Simulation of grinding is a topic of great interest due to the wide application of the process in contemporary industry. Up to date, several modelling methods have been utilized in order to accurately describe the complex phenomena taking place during grinding, the most common being the finite element method and artificial intelligence techniques, e.g. soft computing methods. The present paper proposes a new hybrid model for precision grinding, more specifically the combination of finite elements with neural networks. The model possesses the advantages of both the aforementioned methods, for the prediction of several grinding features that define the outcome of the process and the quality of the final product.

[1]  Wear Simulation Modeling by Using the Finite Element Method , 2015 .

[2]  Saeed Setayeshi,et al.  Prediction of influence parameters on the hot rolling process using finite element method and neural network , 2009 .

[3]  Uday S. Dixit,et al.  Hybrid Modeling and Optimization of Manufacturing: Combining Artificial Intelligence and Finite Element Method , 2012, Int. J. Manuf. Mater. Mech. Eng..

[4]  Akbar A. Javadi,et al.  Neural network for constitutive modelling in finite element analysis , 2003 .

[5]  Angelos P. Markopoulos,et al.  Molecular dynamics modeling of a single diamond abrasive grain in grinding , 2015 .

[6]  Wei Chen,et al.  Using genetic algorithm-back propagation neural network prediction and finite-element model simulation to optimize the process of multiple-step incremental air-bending forming of sheet metal , 2010 .

[7]  Yunlian Qi,et al.  Development of constitutive relationship model of Ti600 alloy using artificial neural network , 2010 .

[8]  Martin Novák,et al.  Surface quality of hardened steels after grinding , 2011 .

[9]  B. J. Mac Donald,et al.  Determination of the optimal load path for tube hydroforming processes using a fuzzy load control algorithm and finite element analysis , 2004 .

[10]  Srinivasan Chandrasekar,et al.  Simulation of thermal stresses due to grinding , 2001 .

[11]  Ekkard Brinksmeier,et al.  Advances in Modeling and Simulation of Grinding Processes , 2006 .

[12]  Wit Grzesik,et al.  Meshing strategies in FEM simulation of the machining process , 2015 .

[13]  A. G. Mamalis,et al.  Effect of the workpiece material on the heat affected zones during grinding: a numerical simulation , 2003 .

[14]  Rémy Glardon,et al.  Finite element and neural network models for process optimization in selective laser sintering , 2004 .

[15]  Martin Novak,et al.  Surfaces with high precision of roughness after grinding , 2012 .

[16]  Erry Yulian Triblas Adesta,et al.  MODELLING AND ANALYSING THE CUTTING FORCES IN HIGH SPEED HARD END MILLING USING NEURAL NETWORK , 2015 .

[17]  Giuseppina Ambrogio,et al.  A hybrid finite element method–artificial neural network approach for predicting residual stresses and the optimal cutting conditions during hard turning of AISI 52100 bearing steel , 2008 .

[18]  H. Ohmori,et al.  Grinding of the Alloy INCONEL 718 and Final Roughness of the Surface , 2016 .

[19]  S. Malkin,et al.  Temperatures and Energy Partition for Grinding with Vitrified CBN Wheels , 1999 .

[20]  I. C. Howard,et al.  A combined neuro fuzzy-cellular automata based material model for finite element simulation of plane strain compression , 2007 .

[21]  Robert Bauer,et al.  Finite element modeling approaches in grinding , 2009 .