Power consumption and tool life models for the production process

For achieving the multi-objective optimization of product quality and power consumption of any production process, the formulation of generalized models is essential. Extensive research has been done on applying the traditional statistical methods (analysis of variance, response surface methodology, grey relational analysis, Taguchi method) in formulation of these models for the processes. In the present work, a detailed survey on the applications of these methods in modelling of power consumption for the production operations specifically machining is conducted. Critical issues arising from the survey are highlighted and hence form the motivation of this study. Further, three advanced soft computing methods, namely evolutionary-based genetic programming (GP), support vector regression, and multi-adaptive regression splines are proposed in predictive modelling of tool life and power consumption of a turning phenomenon in machining. Statistical comparison based on the five error metrics and hypothesis tests for the goodness of the fit reveals that the GP model outperforms the other two models. The hidden relationships between the process parameters are unveiled from the formulated models. It is found that the cutting speed parameter is the most influential input for power consumption and tool life in the turning phenomenon. The future scope comprising of the challenges in predictive modelling of production processes is highlighted in the end.

[1]  Paul Mativenga,et al.  Modelling of direct energy requirements in mechanical machining processes , 2013 .

[2]  Keng Siau,et al.  A review of data mining techniques , 2001, Ind. Manag. Data Syst..

[3]  Uday S. Dixit,et al.  Modeling of Metal Forming and Machining Processes: by Finite Element and Soft Computing Methods (Engineering Materials and Processes) (Engineering Materials and Processes) , 2008 .

[4]  Lin Jiang,et al.  Short-term natural gas demand prediction based on support vector regression with false neighbours filtered , 2015 .

[5]  I. Hanafi,et al.  Optimization of cutting conditions for sustainable machining of PEEK-CF30 using TiN tools , 2012 .

[6]  L. B. Abhang,et al.  Power Prediction Model for Turning EN-31 Steel Using Response Surface Methodology , 2010 .

[7]  Akhil Garg,et al.  A molecular simulation based computational intelligence study of a nano-machining process with implications on its environmental performance , 2015, Swarm Evol. Comput..

[8]  Xiangjun Xie,et al.  Improved delay-dependent stability analysis for neural networks with time-varying delays. , 2014, ISA transactions.

[9]  Kang Tai,et al.  Genetic Programming for Modeling Vibratory Finishing Process: Role of Experimental Designs and Fitness Functions , 2013, SEMCCO.

[10]  Bibhuti Bhusan Biswal,et al.  A general regression neural network approach for the evaluation of compressive strength of FDM prototypes , 2014, Neural Computing and Applications.

[11]  Yan He,et al.  An on-line approach for energy efficiency monitoring of machine tools , 2012 .

[12]  Jun Yang,et al.  RBFNDOB-based neural network inverse control for non-minimum phase MIMO system with disturbances. , 2014, ISA transactions.

[13]  A. J. Morris,et al.  Confidence bounds for neural network representations , 1997 .

[14]  Uday S. Dixit,et al.  Application of soft computing techniques in machining performance prediction and optimization: a literature review , 2010 .

[15]  Akhil Garg,et al.  A new computational intelligence approach in formulation of functional relationship of open porosity of the additive manufacturing process , 2015 .

[16]  J. Lam,et al.  Environmental sustainability of logistics service provider: an ANP-QFD approach , 2015 .

[17]  Vojislav Kecman,et al.  Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models , 2001 .

[18]  Akhil Garg,et al.  Selection of a robust experimental design for the effective modeling of nonlinear systems using Genetic Programming , 2013, 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

[19]  Marti A. Hearst.  Trends & Controversies: Support Vector Machines , 1998, IEEE Intell. Syst..

[20]  Cristian Caizar,et al.  Application of Taguchi method to selection of optimal lubrication and cutting conditions in face milling of AlMg3 , 2011 .

[21]  Paul Mativenga,et al.  Sustainable machining: selection of optimum turning conditions based on minimum energy considerations , 2010 .

[22]  M. Gheorghe,et al.  Models of machine tool efficiency and specific consumed energy , 2003 .

[23]  A. Garg,et al.  Review of genetic programming in modeling of machining processes , 2012, 2012 Proceedings of International Conference on Modelling, Identification and Control.

[24]  P.L.B. Oxley,et al.  Predicting cutting forces in face milling , 1994 .

[25]  Rajesh Kumar Bhushan,et al.  Optimization of cutting parameters for minimizing power consumption and maximizing tool life during machining of Al alloy SiC particle composites , 2013 .

[26]  Hare Krishna Mohanta,et al.  Online monitoring and control of particle size in the grinding process using least square support vector regression and resilient back propagation neural network. , 2015, ISA transactions.

[27]  Amir Hossein Gandomi,et al.  A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems , 2011, Neural Computing and Applications.

[28]  H. Taghavifar,et al.  Diesel engine spray characteristics prediction with hybridized artificial neural network optimized by genetic algorithm , 2014 .

[29]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[30]  Leonardo Vanneschi,et al.  Measuring bloat, overfitting and functional complexity in genetic programming , 2010, GECCO '10.

[31]  Shouming Zhong,et al.  Exponential convergence analysis of uncertain genetic regulatory networks with time-varying delays. , 2014, ISA transactions.

[32]  Y. Wong,et al.  An Approach to Theoretical Modeling and Simulation of Face Milling Forces , 2000 .

[33]  Amir Hossein Gandomi,et al.  Assessment of artificial neural network and genetic programming as predictive tools , 2015, Adv. Eng. Softw..

[34]  S. Chatterjee,et al.  Regression Analysis by Example , 1979 .

[35]  Jasmine Siu Lee Lam,et al.  Improving environmental sustainability by formulation of generalized power consumption models using an ensemble based multi-gene genetic programming approach , 2015 .

[36]  Suhas S. Joshi,et al.  An analytical model to predict specific shear energy in high-speed turning of Inconel 718 , 2009 .

[37]  Indrajit Mukherjee,et al.  A review of optimization techniques in metal cutting processes , 2006, Comput. Ind. Eng..

[38]  Yan Li,et al.  Optimizing the conditions for the microwave-assisted direct liquefaction of Ulva prolifera for bio-oil production using response surface methodology , 2013 .

[39]  Lin Li,et al.  Multi-objective optimization of milling parameters – the trade-offs between energy, production rate and cutting quality , 2013 .

[40]  J. Friedman.  Multivariate adaptive regression splines , 1990 .

[41]  Ahmed A. D. Sarhan,et al.  Investigating the Minimum Quantity Lubrication in grinding of Al2O3 engineering ceramic , 2014 .

[42]  John Edwin Raja Dhas,et al.  Modeling and prediction of machining quality in CNC turning process using intelligent hybrid decision making tools , 2013, Appl. Soft Comput..

[43]  M. M. Alinia,et al.  Behavior appraisal of steel semi-rigid joints using Linear Genetic Programming , 2009 .

[44]  Girish Kant,et al.  Prediction and optimization of machining parameters for minimizing power consumption and surface roughness in machining , 2014 .

[45]  Y. S. Tarng,et al.  Design optimization of cutting parameters for turning operations based on the Taguchi method , 1998 .

[46]  Conor Ryan,et al.  Handbook of Genetic Programming Applications , 2015, Springer International Publishing.

[47]  Akhil Garg,et al.  Empirical analysis of model selection criteria for genetic programming in modeling of time series system , 2013, 2013 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr).

[48]  Carmita Camposeco-Negrete,et al.  Optimization of cutting parameters for minimizing energy consumption in turning of AISI 6061 T6 using Taguchi methodology and ANOVA , 2013 .

[49]  Amir Hossein Gandomi,et al.  A hybrid computational approach to derive new ground-motion prediction equations , 2011, Eng. Appl. Artif. Intell..

[50]  Hari Singh,et al.  Optimizing power consumption for CNC turned parts using response surface methodology and Taguchi's technique—A comparative analysis , 2008 .

[51]  Gianni Campatelli,et al.  Optimization of process parameters using a Response Surface Method for minimizing power consumption in the milling of carbon steel , 2014 .

[52]  Bilgin Tolga Simsek,et al.  Optimization of cutting fluids and cutting parameters during end milling by using D-optimal design of experiments , 2013 .

[53]  Adebisi A. Okeleye,et al.  Mathematical modeling and process parameters optimization studies by artificial neural network and response surface methodology: A case of non-edible neem (Azadirachta indica) seed oil biodiesel synthesis , 2014 .

[54]  G. Vining,et al.  Data Analysis: A Model-Comparison Approach , 1989 .

[55]  Abbas Khosravi,et al.  Uncertainty handling using neural network-based prediction intervals for electrical load forecasting , 2014 .

[56]  Anirban Bhattacharya,et al.  Estimating the effect of cutting parameters on surface finish and power consumption during high speed machining of AISI 1045 steel using Taguchi design and ANOVA , 2009, Prod. Eng..

[57]  Steven Y. Liang,et al.  A generalised model of milling forces , 1998 .

[58]  Amir Hossein Alavi,et al.  An evolutionary approach for modeling of shear strength of RC deep beams , 2013 .

[59]  Bibhuti Bhusan Biswal,et al.  Application of Artificial Intelligence Methods to Spot Welding of Commercial Aluminum Sheets (B.S. 1050) , 2014, SocProS.

[60]  Rosa F. Ropero,et al.  Regression using hybrid Bayesian networks: Modelling landscape-socioeconomy relationships , 2014, Environ. Model. Softw..

[61]  Zhanshan Wang,et al.  Mode-dependent stochastic stability criteria of fuzzy Markovian jumping neural networks with mixed delays. , 2015, ISA transactions.

[62]  Babur Ozcelik,et al.  Evaluation of vegetable based cutting fluids with extreme pressure and cutting parameters in turning of AISI 304L by Taguchi method , 2011 .

[63]  Shen Lan,et al.  Numerical simulation and process optimization of an aluminum holding furnace based on response surface methodology and uniform design , 2014 .

[64]  Qiongfen Yang,et al.  New delay dependent stability criteria for recurrent neural networks with interval time-varying delay. , 2014, ISA transactions.

[65]  Sami Kara,et al.  An empirical model for predicting energy consumption of manufacturing processes: a case of turning process , 2011 .

[66]  Pin-Lin Liu,et al.  Further improvement on delay-dependent robust stability criteria for neutral-type recurrent neural networks with time-varying delays. , 2015, ISA transactions.

[67]  Mustafa Gölcü,et al.  Modeling of cutting forces as function of cutting parameters for face milling of satellite 6 using an artificial neural network , 2007 .

[68]  Gisbert Schneider,et al.  Support vector machine applications in bioinformatics. , 2003, Applied bioinformatics.

[69]  Kang Tai,et al.  Review of empirical modelling techniques for modelling of turning process , 2013, Int. J. Model. Identif. Control..

[70]  George Chryssolouris,et al.  Confidence interval prediction for neural network models , 1996, IEEE Trans. Neural Networks.

[71]  John Langford,et al.  Beating the hold-out: bounds for K-fold and progressive cross-validation , 1999, COLT '99.

[72]  Kornel Ehmann,et al.  A cutting force model for face milling operations , 1993 .

[73]  Murat Sarıkaya,et al.  Taguchi design and response surface methodology based analysis of machining parameters in CNC turning under MQL , 2014 .

[74]  Satyender Singh,et al.  Thermal and thermohydraulic performance evaluation of a novel type double pass packed bed solar air heater under external recycle using an analytical and RSM (response surface methodology) combined approach , 2014 .

[75]  Sami Kara,et al.  Unit process energy consumption models for material removal processes , 2011 .