PARAMETRIC MODELLING AND OPTIMISATION OF THE LASER CUTTING AND WELDING USING ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHM BY SUDIPTO CHAKI A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY (ENGINEERING) UNDER THE SUPERVISION OF PROF. SUJIT GHOSAL DEPARTMENT OF MECHANICAL ENGINEERING FACULTY OF ENGINEERING AND TECHNOLOGY JADAVPUR UNIVERSITY KOLKATA-700032, WEST BENGAL, INDIA JULY, 2012
SYNOPSIS OF PARAMETRIC MODELLING AND OPTIMISATION OF THE LASER CUTTING AND WELDING USING ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHM BY SUDIPTO CHAKI A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY (ENGINEERING) UNDER THE SUPERVISION OF PROF. SUJIT GHOSAL DEPARTMENT OF MECHANICAL ENGINEERING FACULTY OF ENGINEERING AND TECHNOLOGY JADAVPUR UNIVERSITY KOLKATA-700032, WEST BENGAL, INDIA JULY, 2012
JADAVPUR UNIVERSITY KOLKATA-700032, INDIA INDEX NO. 99/08/E 1. Title of Thesis: Parametric Modelling and Optimisation of the Laser Cutting and Welding Using Artificial Neural Networks and Genetic Algorithm 2. Name, Designation and Institution of the Supervisor: Dr. Sujit Ghosal Professor, Mechanical Engineering Department Jadavpur University, Kolkata-700032 3. List of Publications : a) Refereed Journals: i) Chaki, S, Ghosal, S & Bathe, RN 2012, Kerf quality prediction and optimization for pulsed Nd:YAG laser cutting of aluminium alloy sheets using GA-ANN hybrid model, International Journal of Mechatronics and Manufacturing Systems, Special Issue on: Lasers in Manufacturing, Inderscience Publishers, (in press). ii) iii) Chaki, S & Ghosal, S 2011, Application of an Optimized SA-ANN Hybrid Model for Parametric Modeling and Optimization of LASOX Cutting of Mild Steel, Production Engineering: Research and Development, Springer Berlin Heidelberg, vol.5, no.3, pp.251-262. Ghosal, S & Chaki, S 2010, Estimation and Optimization of Depth of Penetration in Hybrid CO 2 Laser-MIG Welding Using ANN-Optimization Hybrid Model, International Journal of Advanced Manufacturing Technology, Springer London, vol.47, no. 9-12, pp.1149-1157. b) Conference Proceedings: i) Chaki, S & Ghosal, S 2010, A RSM-GA Hybrid Modeling Approach for Optimisation of Cutting Quality in LASOX Cutting Process, Proceedings of National Conference on Recent Advances in Manufacturing (RAM-2010), Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, 19-21 July, pp.119-126. ii) Chaki, S & Ghosal, S 2010, Prediction of Cutting Quality in LASOX Cutting of Mild Steel Using ANN and Regression Model, Proceedings of National Conference on Recent Advances in Manufacturing Technology and Management (RAMTM2010) in Jadavpur University, Kolkata, 19-20 Feb, pp. 163-168. i
iii) Chaki, S & Ghosal, S 2009, Neural Network Approaches For Determination of Penetration Depth of CO 2 MIG Laser Hybrid Welding, Proceedings of National Conference on Computer Aided Modelling and Simulation in Computational Mechanics (CAMSCM 09) in North Eastern Regional Institute of Science and Technology (NERIST), Arunachal Pradesh, 13-14 March, pp. 62-74. 4. List of Patents: Nil 5. List of Presentations in National / International Conferences: i) Chaki, S & Ghosal, S 2010, Prediction of Cutting Quality in LASOX Cutting of Mild Steel Using ANN and Regression Model, paper presented to National Conference on Recent Advances in Manufacturing Technology and Management (RAMTM2010) in Production Engineering Department, Jadavpur University, Kolkata, 19-20 Feb. ii
Introduction: Laser beam machining (LBM) is a thermal energy based advanced machining process in which a high energy density laser bream is focused on work surface and the thermal energy so absorbed heats and transforms the work volume into a molten, vaporised or chemically changed state that can easily be removed by flow of high pressure assist gas jet. However, the process parameters of those LBM techniques bear complex nonlinear relationships with the output(s) or quality characteristics and thus not amenable to classical mathematical analyses to generate closed form solutions. Present thesis has been focused on modelling and optimisation of process parameters of popular and emerging LBM techniques such as, laser cutting, laser assisted oxygen (LASOX) cutting and laser-mig hybrid welding processes using soft computing techniques. The state of the art: From a detailed literature review it has been observed that, standard statistical design of experiment techniques such as Taguchi method (TM), response surface methodology (RSM) and factorial design (FD) have been widely used for optimisation of the process parameters. All the studies using TM have developed a combined quality function based on weighted effects of signal-to-noise (S/N) ratio of quality characteristics and employed the function as objective function during optimisation. Factorial design determines optimised condition from the main effect plot of the parameters. Studies on RSM indicates that, RSM initially develops separate regression models for each quality parameters and during optimisation, a combined quality function consisting of weighted sum of those regression models is used as objective function. In recent years, online process monitoring has become essential for maintaining uniform product quality with increased productivity in process industries integrated with computer based automation. Though statistical techniques such as TM, FD and RSM as stated above can efficiently analyse and optimise the process parameters, they are not suitable for implementation in online process control. In such condition, soft computing, an innovative approach to construct computationally intelligent systems has emerged as an essential tool for process modelling and optimisation. Soft computing is an emerging approach to computing that mimic the remarkable ability of the human mind to reason and learn in an environment of uncertainty and imprecision. It consists of artificial neural networks (ANN), fuzzy set theory, and derivative-free optimisation methods such as genetic algorithm (GA) and simulated annealing (SA). Such modelling tools can be efficiently employed for intelligent process control technique overcoming the inflexibility of conventional closed-loop sensor based process control system to assess the dynamic characteristics of the process. 1
A few literatures on soft computing techniques for process modelling and optimisation of laser cutting and welding has been observed. ANN has been mainly used for prediction of output quality parameters. In all ANN applications back propagation neural networks (BPNN) with either gradient descent momentum or with Levenberg Marquardt (LM) is used as training algorithm. Only one application is available where a learning vector quantisation (LVQ) network has been employed. GA is used in laser cutting and welding where a separate regression equation is developed and used as fitness function. Though some works have included ANN and GA, they have treated prediction and optimisation separately. GA optimisation is completed with separate regression equation with no influence of ANN. Some studies have integrated Taguchi and ANN where a trained ANN is employed to predict output parameters for a design matrix of Taguchi and Taguchi completes the optimisation. Scope of present work: Literature review indicates, ANN prediction models have used mainly BPNN with gradient descent momentum and with LM as training algorithm. Though LM is faster in training but it does not perform well during prediction while training small and noisy dataset. In such cases, BPNN with Bayesian Regularisation (BR) technique perform particularly well and produce better prediction output. But, so far, application of BR was not observed in the field of laser cutting and welding. Apart from BPNN no other feed forward network such as radial basis function networks (RBFN) has been used in laser cutting and welding applications. Moreover, no integrated model or algorithm based on ANN and evolutionary optimisation techniques like GA, SA etc has been developed in the field of LMP that can be implemented online for process modelling and optimisation. Such models can eliminate the need of closed form objective functions and can handle the problems more efficiently that bear complex input-output relationship. In the present thesis, three different integrated soft computing based models (ANN- GA, ANN-SA and ANN-Quasi Newton) have been developed and implemented for process modelling and optimisation of above mentioned LBM processes. Extensive experimentations based on design of experiments have been carried out for all the processes. The dataset generated from the experimentation carried out for the purpose are employed for ANN modelling. ANN modelling in the present study uses different architectures of three different ANN algorithms such as BPNN with LM, BPNN with BR and RBFN. Best ANN architecture is selected based on prediction/testing performances and integrated with GA, SA and Quasi Newton to develop integrated ANN-GA, ANN- SA and ANN-Quasi Newton models. Multi-objective optimisation problems have been converted into single objective one by weighted sum of the output parameters. During 2
iterations, input parameters generated by optimisation algorithms are sent to best ANN that computes and returns the corresponding output parameter values to optimisation for calculation of objective function value. Such integration of ANN eliminates the need of closed form objective function. A comparison of best ANN with regression models has also been carried out during study of each process. Finally, best optimisation model has been selected on comparing all integrated models based on optimisation capability. Chapter contents: Present thesis has been divided into six chapters. Chapter 1 provides a brief introduction to the subject of LBM with particular emphasis on Laser cutting and Laser welding; a detailed state of the art in the field with special reference to design of experiments and different soft computing techniques being employed therein and finally the objective of the present work. Chapter 2 describes the methodologies adopted in the present work. In this section, working of ANN models, GA, SA and Quasi-Newton algorithms in general along with working of developed integrated ANN-GA, ANN-SA and ANN-Quasi Newton models have been explained in some detail. Chapter 3 includes modelling and optimisation of Nd:YAG laser cutting of AA1200 aluminium alloy sheets where cutting speed, laser power and pulse width have been considered as process variables for modelling and optimisation of kerf width, kerf deviation, surface roughness and material removal rate(mrr). Twenty seven numbers of experiments have been conducted based on 3 factor 3 level full factorial experimental design without replication in 135 W pulsed Nd:YAG laser system. During process modelling, 34 architectures of ANN with three different training algorithms have been trained and tested. 3-7-4 network trained and tested using BPNN with BR yields best prediction performance with MSE of 3.04E-04 and is considered as best ANN. Prediction capability of the best ANN (maximum absolute % error of 2.81%) is found superior compared to the second order regression models (maximum absolute % error of 7.95%) developed for the purpose. The regression models have been found adequate during ANOVA test. The best ANN is then coupled with GA, SA and Quasi Newton algorithms for subsequent optimisation. ANN-GA is found to show best optimisation performance during optimisation with maximum absolute % error of 2.11% during experimental validation. Medium cutting speed, low pulse energy and pulse width has been found to produce optimum cut quality with maximum MRR. ANOVA indicates cutting speed and pulse energy are the main contributing factors on combined objective function while pulse width has negligible influence. Pulse energy is found to be main influencing factor for kerf width and kerf deviation. Cutting speed is main influencing 3
factor for MRR but it has negligible effect on kerf deviation. Surface roughness is equally influenced by cutting speed and pulse energy. Pulse width has significant influence on kerf deviation and surface roughness. Finally, as a special case study with laser cutting operation, an integrated model of ANN and non-dominated sorting genetic algorithm II (ANN-NSGAII) model has been developed and successfully employed for multi-objective optimisation with development of pareto-optimal front. Unique feature of ANN-NSGAII model is generation of number of optimal solutions known as pareto-optimal solutions getting rid of any subjective weight factor associated with output parameters as used in single objective function formulation. ANN-NSGAII model has been found to produce less than 1% error during experimental validation of optimised outputs. Characterisation of process parameters in pareto-optimal region has been also explained in detail. In pareto-optimal region, surface roughness increases in parabolic nature with cutting speed and increases linearly with pulse energy. MRR increases almost linearly with cutting speed and pulse energy. In chapter 4, modelling and optimisation of CO 2 LASOX cutting of mild steel plates have been carried out where cutting speed, gas pressure, laser power and stand off distance have been considered as process variables for modelling and optimisation of HAZ width, kerf width and surface roughness. 4-8-3 network during BPNN with BR training and testing results best prediction performance with MSE of 8.63E-04 among all 36 tested network architectures and is considered as best ANN. Prediction capability of the best ANN (maximum absolute % error of 12.8%) is found superior compared to the second order regression models (maximum absolute % error of 30.8%) developed for the purpose. During optimisation ANN-SA is found to show best optimisation performance with maximum absolute % error of 5.18% during experimental validation. Low gas pressure and high cutting speed, laser power and stand off distance produces optimum cut quality. Performance of the ANN-SA model is further improved by development and implementation of an optimised ANN-SA model where Quasi Newton optimisation technique is used for optimisation of ANN-SA model. In Chapter 5, two different modelling and optimisation studies on CO 2 laser - MIG hybrid welding has been carried out. In one study prediction modelling and optimisation for tensile strength of weldments are carried out considering laser power, welding speed and wire feed rate as controllable input process parameters. To this end twenty seven numbers of experiments have been conducted based on a 3 factor 3 level full factorial experimental design without replication using cold rolled AA8011 aluminium alloy sheets in 3.5kW CO 2 laser - MIG hybrid welding setup. During process modelling, 3-11- 1 network trained and tested using BPNN with BR results best prediction performance with MSE of 3.24E-04 during prediction of welding strength out of a total of 32 network 4
architectures and is considered as best ANN. Best ANN shows marginally better prediction performance (maximum absolute % error of 3.21%) compared to second order regression model developed (maximum absolute % error of 3.37 %). Adequacy of the regression models has been tested through ANOVA. ANN-GA is found to show best performance during optimisation with absolute % error of only 0.0503% during experimental validation. Maximum tensile strength is obtained at low laser power, welding speed and wire feed rate. ANOVA indicates welding speed is most influencing factor on welding strength while wire feed rate and laser power has negligible influence. In the other study, laser power, focal distance from the work piece surface, torch angle and the distance between the laser and the welding torch have been considered as input process parameters for optimisation of weld penetration of 5005 aluminium alloy (0.6% magnesium) sheets. 4-7-1 network during BPNN with BR training and testing results best prediction performance among different architectures with MSE of 5.06E-04 during prediction of welding strength among all 32 tested network architectures and is considered as best ANN. ANN-GA is found to show best optimisation performance during optimisation. Maximum weld penetration is obtained at high laser power and focal distance from the work piece surface with low torch angle and the distance between the laser and the welding torch. Chapter 6 presents the comparative studies and final conclusions of the work. During process modelling, the BPNN with BR algorithm has been found to produce best prediction capability during prediction of output characteristics for all the laser material processes tested. Prediction performance of best ANN has been found superior compared to second order regression model for all the processes tested. During optimisation, ANN- GA hybrid model is found to show superior optimisation performance with maximum absolute % error of 2.11% and 0.0503% during experimental validation for laser cutting and laser-mig hybrid welding process. But in LASOX cutting, ANN-SA is found to produce marginally better optimisation performance (maximum absolute % error of 5.18%) during experimental validation compared to ANN-GA (maximum absolute % error of 6.36%). Optimised parameter setting for all optimisation carried out and their significance has been detailed. Influence of different input process parameters on optimised output performance for optimisation based on combined objective function as well as for individual output parameters have been detailed for each of the processes. It is believed that the present thesis work will add significant contribution to the existing literature from the point of view of both industrial importance and academic interest. 5