Bacterial Memetic Algorithm Trained Fuzzy System-Based Model of Single Weld Bead Geometry

This article presents a fuzzy system-based modeling approach to estimate the weld bead geometry (WBG) from the welding process variables (WPVs) and to achieve a specific weld bead shape. The bacterial memetic algorithm (BMA) is applied to solve these problems in two different roles, as a supervised trainer, and as an optimizer. As a supervised trainer, the BMA is applied to tune two different WBG models. The bead geometry properties (BGP) model follows a traditional approach providing the WBG properties as outputs. The direct profile measurement (DPM) model describes the bead profiles points by a non-linear function realized in the form of fuzzy rules. As an optimizer, the BMA utilizes the developed fuzzy systems to find the solution sets of WPVs to acquire the desired WBG. The best performance is achieved by applying six rules in the BGP model and eleven rules in the DPM model. The results indicate that the normalized root means square error for the validation data set lies in the range of 0.40 – 1.56% for the BGP model and 4.49 – 7.52% for the DPM model. The comparative analysis suggests that the BGP model estimates the BWG in a superior manner when several WPVs are altered. The developed fuzzy systems provide a tool for interpreting the effects of the WPVs. The developed optimizer provides multiple valid set of WPVs to produce the desired WBG, thus supporting the selection of those process variables in applications.

[1]  Pradeep Kumar Jha,et al.  Prediction of the weld pool geometry of TIG arc welding by using fuzzy logic controller , 2012 .

[2]  László T. Kóczy,et al.  Comparative Investigation of Various Evolutionary and Memetic Algorithms , 2010 .

[3]  Bart Kosko,et al.  Fuzzy Systems as Universal Approximators , 1994, IEEE Trans. Computers.

[4]  Paulo José Modenesi,et al.  The chemistry of TIG weld bead formation , 2015 .

[5]  Dali Wang,et al.  DeepWelding: A Deep Learning Enhanced Approach to GTAW Using Multisource Sensing Images , 2020, IEEE Transactions on Industrial Informatics.

[6]  YuMing Zhang,et al.  Iterative Local ANFIS-Based Human Welder Intelligence Modeling and Control in Pipe GTAW Process: A Data-Driven Approach , 2015, IEEE/ASME Transactions on Mechatronics.

[7]  László T. Kóczy,et al.  Fuzzy if... then rule models and their transformation into one another , 1996, IEEE Trans. Syst. Man Cybern. Part A.

[8]  Vera Kurková,et al.  Kolmogorov's theorem and multilayer neural networks , 1992, Neural Networks.

[9]  Soh-Khim Ong,et al.  Optimal pass planning for robotic welding of large-dimension joints with nonuniform grooves , 2018 .

[10]  László T. Kóczy,et al.  1 On functional equivalence of certain fuzzy controllers and RBF type approximation schemes ? , 2000 .

[11]  Tarasankar DebRoy,et al.  Tailoring gas tungsten arc weld geometry using a genetic algorithm and a neural network trained with convective heat flow calculations , 2007 .

[12]  María José del Jesús,et al.  Evolutionary Fuzzy Systems for Explainable Artificial Intelligence: Why, When, What for, and Where to? , 2019, IEEE Computational Intelligence Magazine.

[13]  Lianfa Bai,et al.  Weld Reinforcement Analysis Based on Long-Term Prediction of Molten Pool Image in Additive Manufacturing , 2020, IEEE Access.

[14]  T. Jayakumar,et al.  Intelligent Modeling Combining Adaptive Neuro Fuzzy Inference System and Genetic Algorithm for Optimizing Welding Process Parameters , 2011 .

[15]  Kenneth Levenberg.  A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .

[16]  Tarasankar DebRoy,et al.  A heat-transfer and fluid-flow-based model to obtain a specific weld geometry using various combinations of welding variables , 2005 .

[17]  Madavan Vasudevan.  Soft Computing Techniques in Stainless Steel Welding , 2009 .

[18]  Lin Wu,et al.  Bead geometry prediction for robotic GMAW-based rapid manufacturing through a neural network and a second-order regression analysis , 2012, Journal of Intelligent Manufacturing.

[19]  Yu Xue,et al.  Fuzzy regression method for prediction and control the bead width in the robotic arc-welding process , 2005 .

[20]  Alain Bernard,et al.  Weld bead modeling and process optimization in Hybrid Layered Manufacturing , 2011, Comput. Aided Des..

[21]  Francisco Herrera,et al.  Genetic Fuzzy Systems - Evolutionary Tuning and Learning of Fuzzy Knowledge Bases , 2002, Advances in Fuzzy Systems - Applications and Theory.

[22]  Heikki Handroos,et al.  Development of an Artificial Intelligence Powered TIG Welding Algorithm for the Prediction of Bead Geometry for TIG Welding Processes using Hybrid Deep Learning , 2020, Metals.

[23]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[24]  Soh-Khim Ong,et al.  Adaptive pass planning and optimization for robotic welding of complex joints , 2017 .

[25]  José A. R. Vargas,et al.  Sensor Fusion to Estimate the Depth and Width of the Weld Bead in Real Time in GMAW Processes , 2018, Sensors.

[26]  Richard Thomas Lermen,et al.  Optimizing the Parameters of TIG-MIG/MAG Hybrid Welding on the Geometry of Bead Welding Using the Taguchi Method , 2017 .

[27]  Prasad K. Yarlagadda,et al.  A study on prediction of bead height in robotic arc welding using a neural network , 2002 .

[28]  László T. Kóczy,et al.  Fuzzy rule extraction by bacterial memetic algorithms , 2009, Int. J. Intell. Syst..

[29]  A. Karpagaraj,et al.  Optimization techniques used in gas tungsten arc welding process – A review , 2020 .

[30]  Gabor Sziebig,et al.  Aspects of Multi-pass GTAW of Low Alloyed Steels , 2019 .

[31]  Chuen-Tsai Sun,et al.  Functional equivalence between radial basis function networks and fuzzy inference systems , 1993, IEEE Trans. Neural Networks.

[32]  Naoyuki Kubota,et al.  Bacterial memetic algorithm for offline path planning of mobile robots , 2012, Memetic Comput..

[33]  Honghai Liu,et al.  Bacterial memetic algorithm based feature selection for surface EMG based hand motion recognition in long-term use , 2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI).

[34]  Shanben Chen,et al.  Robot Path Planning in Multi-pass Weaving Welding for Thick Plates , 2011 .

[35]  J. S. Zuback,et al.  Additive manufacturing of metallic components – Process, structure and properties , 2018 .

[36]  Huijun Li,et al.  A multi-bead overlapping model for robotic wire and arc additive manufacturing (WAAM) , 2015 .

[37]  Péter Korondi,et al.  Supportive Robotic Welding System for Heavy, Small Series Production with Non-Uniform Welding Grooves , 2019 .

[38]  László T. Kóczy,et al.  Estimating fuzzy membership functions parameters by the Levenberg-Marquardt algorithm , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).

[39]  Yu Shi,et al.  Progress and Trend in Intelligent Sensing and Control of Weld Pool in Arc Welding Process , 2019, Transactions on Intelligent Welding Manufacturing.

[40]  Thomas Bäck,et al.  Theory of Evolutionary Computation: Recent Developments in Discrete Optimization , 2020, Theory of Evolutionary Computation.

[41]  G. Kaptay,et al.  An Improved Theoretical Model for A-TIG Welding Based on Surface Phase Transition and Reversed Marangoni Flow , 2012, Metallurgical and Materials Transactions A.

[42]  Sheng Zhu,et al.  Overlapping model of beads and curve fitting of bead section for rapid manufacturing by robotic MAG welding process , 2011 .

[43]  Shengsun Hu,et al.  Simulation and analysis of heat transfer and fluid flow characteristics of variable polarity GTAW process based on a tungsten–arc-specimen coupled model , 2016 .

[44]  Madavan Vasudevan,et al.  Adaptive Neuro-Fuzzy Inference System (ANFIS)-Based Models for Predicting the Weld Bead Width and Depth of Penetration from the Infrared Thermal Image of the Weld Pool , 2012, Metallurgical and Materials Transactions B.

[45]  A. G. Olabi,et al.  Optimization of different welding processes using statistical and numerical approaches - A reference guide , 2008, Adv. Eng. Softw..

[46]  Dilip Kumar Pratihar,et al.  Expert systems in manufacturing processes using soft computing , 2015, The International Journal of Advanced Manufacturing Technology.

[47]  Akhil Garg,et al.  Evaluation of genetic programming-based models for simulating bead dimensions in wire and arc additive manufacturing , 2019, J. Intell. Manuf..

[48]  N. Murugan,et al.  Prediction and control of weld bead geometry and shape relationships in submerged arc welding of pipes , 2005 .

[49]  Marco Tomassini,et al.  Evolutionary Algorithms , 1995, Towards Evolvable Hardware.

[50]  Takeshi Furuhashi,et al.  Fuzzy system parameters discovery by bacterial evolutionary algorithm , 1999, IEEE Trans. Fuzzy Syst..

[51]  János Botzheim,et al.  Novel calculation of fuzzy exponent in the sigmoid functions for fuzzy neural networks , 2014, Neurocomputing.

[52]  J. Norberto Pires,et al.  Welding Robots: Technology, System Issues and Application , 2006 .

[53]  Jinglong Li,et al.  Optimization of wire feed for GTAW based additive manufacturing , 2017 .

[54]  János Botzheim,et al.  Energy and cost optimal design for the reconstruction of residential building envelopes by bacterial memetic algorithms , 2012, The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems.

[55]  Qian Han-cheng,et al.  Fuzzy neural network modeling of material properties , 2002 .

[56]  Imre Horváth,et al.  Enhanced beads overlapping model for wire and arc additive manufacturing of multi-layer multi-bead metallic parts , 2018 .

[57]  P. Castro,et al.  Numerical modelling of welded T-joint configurations using SYSWELD , 2018, Science and Technology of Materials.

[58]  Pablo Moscato,et al.  On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts : Towards Memetic Algorithms , 1989 .

[59]  A. K. Bhaduri,et al.  Genetic-Algorithm-Based Computational Models for Optimizing the Process Parameters of A-TIG Welding to Achieve Target Bead Geometry in Type 304 L(N) and 316 L(N) Stainless Steels , 2007 .

[60]  Akira Matsunawa,et al.  Role of Surface Tension in Fusion Welding (Part 2) : Hydrostatic Effect , 1983 .

[61]  Dilip Kumar Pratihar,et al.  Modeling of TIG welding process using conventional regression analysis and neural network-based approaches , 2007 .

[62]  James J. Filliben,et al.  Taguchi’s Orthogonal Arrays Are Classical Designs of Experiments , 1991, Journal of research of the National Institute of Standards and Technology.

[63]  Bintao Wu,et al.  Application of Multidirectional Robotic Wire Arc Additive Manufacturing Process for the Fabrication of Complex Metallic Parts , 2020, IEEE Transactions on Industrial Informatics.

[64]  G. L. Datta,et al.  Genetic algorithm for optimization of welding variables for height to width ratio and application of ANN for prediction of bead geometry for TIG welding process , 2010, Appl. Soft Comput..

[65]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .