A simulator based on artificial neural networks and NSGA-II for prediction and optimization of the grinding process of superalloys with high performance grinding wheels

Abstract Computational intelligence (CI) has been applied to grinding processes in order to perform more and more efficient operations. Among the techniques that compose the area of CI, it is possible to highlight the artificial neural networks (ANN) and the multiobjective optimization algorithms, such as the NSGA-II. These tools are useful to the computational modeling of several processes. However, until now, the hybrid use of these techniques has not been explored for external cylindrical grinding of superalloys. Filling this gap, this work had the objective of developing a methodology that uses a multilayer perceptron (MLP) ANN associated to the NSGA-II, in the form of an objective function. The method optimizes the grinding process of superalloys with the use of two grinding wheels, one conventional and another cBN superabrasive. The methodology consisted of three phases: the first one was of data collection through experiments for the construction of a dataset, containing input configurations of a grinding process related to output parameters that determine the quality of the machining. These parameters, in addition, are also objectives to be minimized when this problem is mathematically modeled. The second one dealt with the training and validation of an MLP ANN as the simulator of this grinding process, and finally, the third, dealt with the generation of optimized solutions by means of the NSGA-II associated with the MLP ANN already trained. Regarding the results, it could be demonstrated that the simulation does not present evidence of statistical differences when compared to real data. As far as optimization is concerned, the non-dominated solutions presented values, in several scenarios, consistent with the literature of the area. In this sense, this work brings as its main contribution a hybrid, scalable computational method that can be used as a tool for decision making. It is also concluded that it can be useful in the planning of economically efficient operations, since the simulation does not involve logistic costs.

[1]  José Manuel Benítez,et al.  Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS , 2012 .

[2]  Josh Patterson,et al.  Deep Learning: A Practitioner's Approach , 2017 .

[3]  Kalyanmoy Deb,et al.  Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms , 1994, Evolutionary Computation.

[4]  Michael F. Zäh,et al.  Experimental and numerical analysis of transient behavior during grind-hardening of AISI 52100 , 2012, Production Engineering.

[5]  Vinod Yadava,et al.  Multi-objective optimization of process parameters in Electro-Discharge Diamond Face Grinding based on ANN-NSGA-II hybrid technique , 2013, Frontiers of Mechanical Engineering.

[6]  W. B. Rowe,et al.  Process Requirements for Cost-Effective Precision Grinding , 2004 .

[7]  Siyu Zhang,et al.  Development of a hybrid particle swarm optimization algorithm for multi-pass roller grinding process optimization , 2018, The International Journal of Advanced Manufacturing Technology.

[8]  Ravindra Nath Yadav,et al.  Multiobjective optimization of slotted electrical discharge abrasive grinding of metal matrix composite using artificial neural network and nondominated sorting genetic algorithm , 2013 .

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

[10]  Sanchit Kumar Khare,et al.  Predictive Modeling of Surface Roughness in Grinding , 2015 .

[11]  E. Ezugwu.  Key improvements in the machining of difficult-to-cut aerospace superalloys , 2005 .

[12]  Fritz Klocke,et al.  Residual stress prediction in quick point grinding , 2012, Prod. Eng..

[13]  Tegoeh Tjahjowidodo,et al.  In-process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learning , 2019, Robotics and Computer-Integrated Manufacturing.

[14]  R. Venkata Rao,et al.  Surface Grinding Process Optimization Using Jaya Algorithm , 2016 .

[15]  Surjya K. Pal,et al.  Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II , 2007 .

[16]  S. Malkin,et al.  Optimization of Continuous Dress Creep-Feed Form Grinding Process , 2003 .

[17]  C. Herrmann,et al.  Determining optimal process parameters to increase the eco-efficiency of grinding processes , 2014 .

[18]  Rasit Ata,et al.  Artificial neural networks applications in wind energy systems: a review , 2015 .

[19]  Wei Zhou,et al.  Modeling and predicting of surface roughness for generating grinding gear , 2013 .

[20]  Xuekun Li,et al.  The development of a hybrid firefly algorithm for multi-pass grinding process optimization , 2019, J. Intell. Manuf..

[21]  Abdalslam Darafon,et al.  3D metal removal simulation to determine uncut chip thickness, contact length, and surface finish in grinding , 2013 .

[22]  Indrajit Mukherjee,et al.  Comparing the performance of neural networks developed by using Levenberg-Marquardt and Quasi-Newton with the gradient descent algorithm for modelling a multiple response grinding process , 2012, Expert Syst. Appl..

[23]  Fukuo Hashimoto,et al.  Industrial challenges in grinding , 2009 .

[24]  M. J. Jackson,et al.  High-speed grinding with CBN grinding wheels — applications and future technology , 2001 .

[25]  Soheyl Khalilpourazari,et al.  Optimization of time, cost and surface roughness in grinding process using a robust multi-objective dragonfly algorithm , 2018, Neural Computing and Applications.

[26]  Yiming Rong,et al.  Framework of grinding process modeling and simulation based on microscopic interaction analysis , 2011 .

[27]  S. G. Deshmukh,et al.  A genetic algorithmic approach for optimization of surface roughness prediction model , 2002 .

[28]  W. Cleveland.  LOWESS: A Program for Smoothing Scatterplots by Robust Locally Weighted Regression , 1981 .

[29]  TkáčMichal,et al.  Artificial neural networks in business , 2016 .

[30]  W. König,et al.  Influence of the Composition and Structure of Steels on Grinding Process , 1981 .

[31]  Doriana M. D’Addona,et al.  Grinding Wheel Management through Neuro-Fuzzy Forecasting of Dressing Cycle Time , 2003 .

[32]  C. K. Nirala,et al.  ANN-NSGA-II dual approach for modeling and optimization in abrasive mixed electro discharge diamond grinding of Monel K-500 , 2018, Engineering Science and Technology, an International Journal.

[33]  M. Janardhan.  An Integrated Evaluation Approach for Modelling and Optimization of Surface Grinding Process Parameters , 2015 .

[34]  Soheyl Khalilpourazari,et al.  A Robust Stochastic Fractal Search approach for optimization of the surface grinding process , 2018, Swarm Evol. Comput..

[35]  Parthiv B. Rana,et al.  Parameters optimization of surface grinding process using Modified ε constrained Differential Evolution , 2017 .

[36]  T. Warren Liao,et al.  A neural network approach for grinding processes: Modelling and optimization , 1994 .

[37]  Avanish Kumar Dubey,et al.  Modelling and multi-objective optimisation of EDDG process using hybrid ANN-GA approach , 2016 .

[38]  Sangjae Lee,et al.  Predicting the helpfulness of online reviews using multilayer perceptron neural networks , 2014, Expert Syst. Appl..

[39]  Stephen Malkin,et al.  Grinding Technology: Theory and Applications of Machining with Abrasives , 1989 .

[40]  S. Liang,et al.  Predictive modeling of surface roughness in grinding , 2003 .

[42]  Li Yan,et al.  Applications of artificial intelligence in grinding , 1994 .

[43]  E J da Silva,et al.  Evaluation Technique for Determining Wheel Performance in the Grinding of Aerospace Materials , 2011 .

[44]  Yan Li,et al.  An Intelligent Multiagent Approach for Selection of Grinding Conditions , 1997 .

[45]  Vijayender Singh,et al.  A New Model for Grinding Force Prediction and Analysis , 2010 .

[46]  Ł. Rypina,et al.  Modelling of surface roughness and grinding forces using artificial neural networks with assessment of the ability to data generalisation , 2017, The International Journal of Advanced Manufacturing Technology.

[47]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[48]  Alluru Gopala Krishna,et al.  Multi-objective optimisation of surface grinding operations using scatter search approach , 2006 .

[49]  Leszek Rutkowski,et al.  Computational intelligence - methods and techniques , 2008 .

[50]  Michal Tkác,et al.  Artificial neural networks in business: Two decades of research , 2016, Appl. Soft Comput..

[51]  S. Agarwal.  Optimizing machining parameters to combine high productivity with high surface integrity in grinding silicon carbide ceramics , 2016 .

[52]  K. Wegener,et al.  Conditioning and monitoring of grinding wheels , 2011 .

[53]  Ming-Kuen Chen,et al.  Neural network modelling and multiobjective optimization of creep feed grinding of superalloys , 1992 .

[54]  Davood Afshari,et al.  Creep feed grinding optimization by an integrated GA-NN system , 2010, J. Intell. Manuf..

[55]  Pedro Paulo Balestrassi,et al.  Artificial neural networks for machining processes surface roughness modeling , 2010 .

[56]  M. Tricard,et al.  Innovations in Abrasive Products for Precision Grinding , 2004 .

[57]  Roberto Teti,et al.  Prediction of Dressing in Grinding Operation via Neural Networks , 2017 .

[58]  P. V. Rao,et al.  Development of specific grinding energy model , 2012 .

[59]  K. Wins,et al.  Integrated ANN-GA Approach For Predictive Modeling And Optimization Of Grinding Parameters With Surface Roughness As The Response , 2018 .

[60]  Ichiro Inasaki,et al.  Modelling and Simulation of Grinding Processes , 1992 .

[61]  Ichiro Inasaki,et al.  A Neural Network Approach to the Decision-Making Process for Grinding Operations , 1992 .

[62]  Yansheng Deng,et al.  Modeling and predicting ground surface topography on grinding chatter , 2018 .

[63]  Zhijian Liu,et al.  Application of Artificial Neural Networks for Catalysis: A Review , 2017 .

[64]  R. Saravanan,et al.  Ants colony algorithm approach for multi-objective optimisation of surface grinding operations , 2004 .

[65]  Wei Zhang,et al.  Calculation model for surface roughness of face gears by disc wheel grinding , 2017 .

[66]  Abbas Vafaeesefat,et al.  Optimum creep feed grinding process conditions for Rene 80 supper alloy using neural network , 2009 .