Skip to main content

Advertisement

Springer Nature Link
Log in

Application of genetic algorithm to external noise cancellation and compensation in automatic arc welding system

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

This paper develops an automatic welding control system which uses a genetic algorithm to carry out external noise cancellation and compensation. In the proposed approach, the genetic algorithm is used to identify the polynomial form or parameters of the external force or disturbance, including the amplitude, frequency and phase to compensate for nonlinear phenomenon such as disturbance in the mechanical system. In compensating for the nonlinear phenomenon, a piecewise linearization of the approximation polynomial is performed and the genetic algorithm optimization process is then used to identify the parameters of the polynomial function. The simulation and experimental results confirm that the proposed automatic welding control system provides an effective means of compensating for the effects of the external force or disturbance and therefore results in an enhanced welding performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Abbreviations

a :

Friction damping coefficient of electrode feed-rate mechanism

se :

Steady state error

r :

Reference input

y :

System output

na :

Number of poles of ARX model to be identified

nb :

Number of zeros plus one of ARX model to be identified

nk :

Identification delay

J :

Total inertia of electrode feed-rate mechanism

k i :

Coefficient ratio of melting rate to arc current

k u :

Coefficient ratio of melting rate to arc voltage

v f :

Electrode feed rate

v m :

Electrode melting rate

K d :

Derivative gain of PID controller

K i :

Integral gain of PID controller

K p :

Proportional gain of PID controller

K u :

Oscillation gain at stability limit under P-controller

K t :

Motor constant of electrode feed-rate mechanism

T u :

Period of oscillation at stability limit under P-controller

U a :

Arc voltage

C(s):

Controller of welding control system

E(s):

Error signal of reference input I r (s) and welding current I a .

G(s):

Control plant of welding control system

H(s):

Current sensor of welding control system

U(s):

Output signal of current controller

I a (s):

Transfer-function of arc current I a of welding control system

I r (s):

Transfer-function of reference input I r (s)of welding control system

References

  • Baker, J. E. (1985). Adaptive selection methods for genetic algorithms. Proc. Int. Conf. Genetic Algorithms and Their Applications (pp. 101–111).

  • Bauchspiess A., Absi Alfaro S.C., Dobrzanski L.A. (2001) Predictive sensor guided robotic manipulators in automated welding cells. Journal of Materials Processing Technology 109(1): 13–19

    Article  Google Scholar 

  • Canudasde Wit C., Olsson H., Astrom K.J., Lischinsky P. (1995) A new model for control of systems with friction. Proceedings of IEEE Transactions on Automatic Control 40(3): 419–425

    Article  Google Scholar 

  • Chen W.H. (2004) Disturbance observer based control for nonlinear systems. Proceedings of IEEE/ASME Transactions on Mechatronics 9(4): 706–716

    Article  Google Scholar 

  • Chen X., Komada S., Fukuda T. (2000) Design of a nonlinear disturbance observer. Proceeding of IEEE Transactions on Industrial Electrics 47(2): 429–437

    Article  Google Scholar 

  • Friedland B., Park Y.J. (1992) On adaptive friction compensation. Proceeding of IEEE Transactions on Automatic Control 37(10): 1609–1612

    Article  Google Scholar 

  • Goldberg D.E. (1989) Genetic Algorithms in search, optimization and machine learning. Reading, MA, Addsion-Wesley

    Google Scholar 

  • Ha, Q. P., Rye, D. C., & Druuant-Whyte, H. F. (2000). Variable structure systems approach to friction estimation and compensation. In Proc. IEEE International Conference on Robotics and Automation (pp. 3543–3548). San Francisco, CA.

  • Holland, J. H. (1975). Adaptation in neural and artificial systems. Ann Arbor, MI: Univ. Mich. Press.

  • Howard B.C. (1979) Modern welding technology. Prentice-Hall, Inc., Englewood Cliffs

    Google Scholar 

  • Hu T.S. (1994) Arc welding (2nd edn.). Peking, China, Machine Industrial Book Co.

    Google Scholar 

  • Huang X., Chen S.B. (2006) SVM-based fuzzy modeling for the arc welding process. Materials Science and Engineering A-Structural Materials Properties Microstructure and Processing 427(1): 181–187

    Google Scholar 

  • Huang Y., Messner W. (1998) A novel disturbance observer design for magnetic hard drive servo system with a rotary actuator. Proceedings of IEEE Transactions on Magnetic 34(4): 1892–1894

    Article  Google Scholar 

  • Iwasaki M., Shibata T., Matsui N. (1999) Disturbance-observer-based nonlinear friction compensation in table drive system. Proceedings of IEEE/ASME Transactions on Mechatronics 4(1): 3–8

    Article  Google Scholar 

  • Khalil, H. K. (2000). Nonlinear systems. Prentice-Hall.

  • Kim E. (2002) A fuzzy disturbance observer and its application to control. Proceedings IEEE Transactions on Fuzzy Systems 10(1): 77–84

    Article  Google Scholar 

  • Kim B.K., Chung W.K. (2003) Advanced disturbance observer design for mechanical positioning systems. Proceeding of IEEE Transactions on Industrial Electrics 50(6): 1207–1216

    Article  Google Scholar 

  • Komada S., Machii N., Hori T. (2000) Control of redundant manipulators considering order of disturbance observer. Proceedings of IEEE Transactions on Industrial Electrics 47(2): 413–420

    Article  Google Scholar 

  • Meran C. (2006) Prediction of the optimized welding parameters for the joined brass plates using genetic algorithm. Materials and Design 27(5): 356–363

    Article  Google Scholar 

  • Pan T.T., Chung T.L., Ngo M.D., Kim H.K., Kim S.B. (2005) Decentralized control design for welding mobile manipulator. Journal of Mechanical Science and Technology 19(3): 756–767

    Article  Google Scholar 

  • Senjyu T., Kashiwagi T., Uezato K. (2001) Position control of ultrasonic motors using MRAC with dead-zone compensation. Proceedings of IEEE Transactions on Industrial Electrics 48(6): 1278–1285

    Article  Google Scholar 

  • Smith, D. (1989). Welding skills and technology. McGraw-Hill Book Co.

  • Tan Y., Chang J., Tan H. (2003) Adaptive backstepping control and friction compensation for ac servo with inertia and load uncertainties. Proceedings IEEE Transactions on Industrial Electrics 50(5): 944–952

    Article  Google Scholar 

  • Verdelho P., Silva M.P., Margato E., Esteves J. (1998) An electronic welder control circuit. IEEE International Conference, Industrial Electronics Society 2: 612–617

    Google Scholar 

  • Wang Y.W., Chen Q. (2002) On-line quality monitoring in plasma-arc welding. Journal of Materials Processing Technology 120(1): 270–274

    Article  Google Scholar 

  • Yamamoto H. (1990) The development of welding control system for spatter reduction. Welding International 4(5): 398–403

    Article  Google Scholar 

  • Yamane S., Sharif L.H., Zeniya S., Oshima K. (2005) Feed forward control of back bead and bead height in narrow gap robotic welding. Science and Technology of Welding and Joining 10(1): 23–26

    Article  Google Scholar 

  • Zhang Y.M., Liu Y.C. (2003) Modeling and control of quasi-keyhole arc welding process. Control Engineering Practice 11(12): 1401–1411

    Article  Google Scholar 

  • Zhang, Y. S., Chen, G. L., & Lin, Z. Q. (2004). Study on weld quality control of resistance spot welding using a neuro-fuzzy algorithm. In Intelligent Information and Engineering Systems, PT 3, Proceedings Lecture Notes in Artificial Intelligence 3215 (pp. 544–550).

  • Zhang Y.M., Liguo E., Walcott B.L. (1998) Interval model based control of gas metal arc welding. IEEE International Conference, American Control Conference 3: 1752–1756

    Google Scholar 

  • Zheng Y.T., Hang S.S. (1987) Arc welding power supply (2nd edn.). Peking, China, Machine Industrial Book Co.

    Google Scholar 

  • Zhou L., Lin T., Chen S.B. (2006) Autonomous acquisition of seam coordinates for arc welding robot based on visual servoing. Journal of Intelligent and Robotic Systems 47(3): 239–255

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, National Central University, Chung-Li, 320, Taiwan

    Cheng-Yu Wu & Pi-Cheng Tung

Authors
  1. Cheng-Yu Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Pi-Cheng Tung
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Pi-Cheng Tung.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wu, CY., Tung, PC. Application of genetic algorithm to external noise cancellation and compensation in automatic arc welding system. J Intell Manuf 19, 249–256 (2008). https://doi.org/10.1007/s10845-008-0078-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-008-0078-4

Keywords