Predicting the grinding force of titanium matrix composites using the genetic algorithm optimizing back-propagation neural network model

A back-propagation neural network BP model and a genetic algorithm optimizing back-propagation neural network (GA-BP) model are proposed to predict the grinding forces produced during the creep-feed deep grinding of titanium matrix composites. These models consider quantitative and non-quantitative grinding parameters (e.g. up-grinding mode and down-grinding mode) as inputs. Comparative results show that the GA-BP model has better prediction accuracy (e.g. up to 95%) than the conventional regression model and the BP model. Specific grinding energy was calculated against the grinding parameters and grinding modes based on the grinding forces predicted by the GA-BP model.

[1]  Kuang-Hua Fuh,et al.  Force modeling and forecasting in creep feed grinding using improved bp neural network , 1997 .

[2]  R. K. Bhoi,et al.  Artificial Neural Network Prediction of Material Removal Rate in Electro Discharge Machining , 2005 .

[3]  Biao Zhao,et al.  Grinding behavior and surface appearance of (TiCp + TiBw)/Ti-6Al-4V titanium matrix composites , 2014 .

[4]  Tianyu Yu,et al.  Investigation on stress distribution and wear behavior of brazed polycrystalline cubic boron nitride superabrasive grains: Numerical simulation and experimental study , 2017 .

[5]  Abhijit Chandra,et al.  Performance and modeling of paired polishing process , 2016 .

[6]  Bin Lin,et al.  Experimental studies on grinding forces and force ratio of the unsteady-state grinding technique , 2002 .

[7]  J. G. Wager,et al.  Influence of Up-Grinding and Down-Grinding on the Contact Zone , 1991 .

[8]  Biao Zhao,et al.  Comparative study on cutting behavior of vitrified cubic boron nitride wheel and electroplated cubic boron nitride wheel in high-speed grinding of (TiCp + TiBw)/Ti-6Al-4V composites , 2016 .

[9]  Bin Li,et al.  Numerical Simulation and Prediction of Cutting Force in Turning of Titanium Alloy , 2012 .

[10]  Wenfeng Ding,et al.  Grindability evaluation and tool wear during grinding of Ti2AlNb intermetallics , 2018 .

[11]  S. F. Ghaderi,et al.  Integration of Artificial Neural Networks and Genetic Algorithm to Predict Electrical Energy consumption , 2006, IECON 2006 - 32nd Annual Conference on IEEE Industrial Electronics.

[12]  Amitava Ghosh,et al.  On the grindability of Titanium alloy by brazed type monolayered superabrasive grinding wheels , 2006 .

[13]  Sang Won Lee,et al.  A study on tool condition monitoring and diagnosis of micro-grinding process based on feature extraction from force data , 2015 .

[14]  David K. Aspinwall,et al.  Creep feed grinding of gamma titanium aluminide and burn resistant titanium alloys using SiC abrasive , 2007 .

[15]  Driss Ouazar,et al.  Evolving neural network using real coded genetic algorithm for daily rainfall-runoff forecasting , 2009, Expert Syst. Appl..

[16]  Yu Jingyuan,et al.  BP neural network prediction of the mechanical properties of porous NiTi shape memory alloy prepared by thermal explosion reaction , 2006 .

[17]  Wenfeng Ding,et al.  Grinding performance and surface integrity of particulate-reinforced titanium matrix composites in creep-feed grinding , 2018 .

[18]  Tianyu Yu,et al.  Influence of grain wear on material removal behavior during grinding nickel-based superalloy with a single diamond grain , 2017 .

[19]  Fei Huang,et al.  Brief Introduction of Back Propagation (BP) Neural Network Algorithm and Its Improvement , 2012 .

[20]  T. Radhakrishnan,et al.  Milling force prediction using regression and neural networks , 2005, J. Intell. Manuf..

[21]  Shifei Ding,et al.  An optimizing BP neural network algorithm based on genetic algorithm , 2011, Artificial Intelligence Review.

[22]  Tianyu Yu,et al.  Grinding performance of textured monolayer CBN wheels: Undeformed chip thickness nonuniformity modeling and ground surface topography prediction , 2017 .

[23]  A. Chandra,et al.  Experimental and modeling characterization of wear and life expectancy of electroplated CBN grinding wheels , 2017 .

[24]  David K. Aspinwall,et al.  Creep feed grinding of burn-resistant titanium (BuRTi) using superabrasive wheels , 2011 .

[25]  Z Yi,et al.  Investigation on stress distribution and pressure capacity of two-layer split high-pressure die based on finite element analysis , 2019, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science.

[26]  Bi Zhang,et al.  Specific energy in grinding of tungsten carbides of various grain sizes , 2009 .

[27]  Hao Wang,et al.  Introduction to Genetic Algorithms in Electromagnetics , 1995 .

[28]  Geng Liu,et al.  Investigation into grindability of a superalloy and effects of grinding parameters on its surface integrity , 2015 .

[29]  Gaurang Panchal,et al.  Behaviour Analysis of Multilayer Perceptrons with Multiple Hidden Neurons and Hidden Layers , 2011 .

[30]  John Lancaster,et al.  Optimization of the hydrotesting sequence in tank farm construction using an adaptive genetic algorithm with stochastic preferential logic , 2008 .

[31]  K. Fuh,et al.  The workpiece temperature, fluid cooling effectiveness and burning threshold of grinding energy in creep feed grinding , 1998 .

[32]  Onur BOYABATLI,et al.  Parameter Selection in Genetic Algorithms , 2004 .

[33]  Bing Chen,et al.  Modeling and prediction of surface roughness in belt polishing based on artificial neural network , 2018 .

[34]  Jason Teo,et al.  A comparative investigation of non-linear activation functions in neural controllers for search-based game AI engineering , 2011, Artificial Intelligence Review.

[35]  Krzysztof Nadolny,et al.  Experimental study into the grinding force in surface grinding of steel CrV12 utilizing a zonal centrifugal coolant provision system , 2018 .

[36]  Lei Wu,et al.  Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method , 2016 .

[37]  Jun Yang,et al.  Thermal error compensation based on genetic algorithm and artificial neural network of the shaft in the high-speed spindle system , 2017 .

[38]  Manoj Kumar Tiwari,et al.  A biased random key genetic algorithm approach for inventory-based multi-item lot-sizing problem , 2015 .

[39]  S. Malkin,et al.  Thermal Analysis of Grinding , 2007 .

[40]  Mohammad Reza Razfar,et al.  Optimum damage and surface roughness prediction in end milling glass fibre-reinforced plastics, using neural network and genetic algorithm , 2009 .