Optimisation process for robotic assembly of electronic components

  • K. T. Andrzejewski
  • M. P. Cooper
  • C. A. Griffiths
  • C. Giannetti
Open Access


Adoption of robots in the manufacturing environment is a way to improve productivity, and the assembly of electronic components has benefited from the adoption of highly dedicated automation equipment. Traditionally, articulated 6-axis robots have not been used in electronic surface mount assembly. However, the need for more flexible production systems that can be used for low to medium production builds means that these robots can be used due to their high degrees of flexibility, excellent repeatability and increasingly lower investment costs. This research investigated the application of an articulated robot with six degrees of freedom to assemble a multi-component printed circuit board (PCB) for an electronic product. A heuristic methodology using a genetic algorithm was used to plan the optimal sequence and identify the best location of the parts to the assembly positions on the PCB. Using the optimised paths, a condition monitoring method for cycle time evaluation was conducted using a KUKA KR16 assembly cell together with four different robot path motions. The genetic algorithm approach together with different assembly position iterations identified an optimisation method for improved production throughput using a non-traditional but highly flexible assembly method. The application of optimised articulated robots for PCB assembly can bridge the gap between manual assembly and the high-throughput automation equipment.


Sequencing optimisation Electronics assembly KUKA robotics Flexible manufacture Genetic algorithm 


Funding information

This work was supported by the Advanced Sustainable Manufacturing Technologies (ASTUTE 2022) project, which is partly funded from the EU’s European Regional Development Fund through the Welsh European Funding Office, in enabling the research upon which this paper is based. Further information on ASTUTE can be found at


  1. 1.
    Crama Y, Flippo OE, van de Klundert J, Spieksma F (1997) The assembly of printed circuit boards: a case with multiple machines and multiple board types. Eur J Oper Res 98:457–472CrossRefGoogle Scholar
  2. 2.
    Crama Y, van de Klundert J, Spieksma F (2002) Production planning problems in printed circuit board assembly. Discret Appl Math 123:339–361MathSciNetCrossRefGoogle Scholar
  3. 3.
    Moghaddam M, Nof SY (2016) Parallelism of pick-and-place operations by multi-gripper robotic arms. Robot Comput Integr Manuf 42:135–146CrossRefGoogle Scholar
  4. 4.
    Milutinović D, Dejan JR (2013) Redundancy in robot manipulators and multi-robot systems. Springer-Verlag, Berlin. CrossRefGoogle Scholar
  5. 5.
    Erdos G, Kovacs A, Vancza J (2016) Optimized joint motion planning for redundant industrial robots. CIRP Ann Manuf Technol 65:451–454CrossRefGoogle Scholar
  6. 6.
    Rubio F, Llopis-Albert C, Valero a F, Suñera JL (2016) Industrial robot efficient trajectory generation without collision through the evolution of the optimal trajectory. Robot Auton Syst 86:106–112CrossRefGoogle Scholar
  7. 7.
    Fu B, Chen L, Zhou Y, Zheng D, Wei Z, Dai J, Pan H (2018) An improved A* algorithm for the industrial robot path planning with high success rate and short length. Robot Auton Syst 106:26–37CrossRefGoogle Scholar
  8. 8.
    Wang C, Ho L-S, J. Cannon D (1998) Heuristics for assembly sequencing and relative magazine assignment for robotic assembly. Comput Ind Eng 34(2):423–431CrossRefGoogle Scholar
  9. 9.
    Mukund Nilakantan J, Huang GQ, Ponnambalam SG (2015) An investigation on minimizing cycle time and total energy consumption in robotic assembly line systems. J Clean Prod 90:311–325CrossRefGoogle Scholar
  10. 10.
    Ji P, Wan YF (2008) Minimizing cycle time for PCB assembly lines: an integer programming model and a branchand-bound approach. Inf Manag Sci 19(2):237–243MathSciNetzbMATHGoogle Scholar
  11. 11.
    Kodek DM, Krisper M (2007) Optimal algorithm for minimizing production cycle time of a printed circuit board assembly line. Int J Prod Res 42(23):5031–5048CrossRefGoogle Scholar
  12. 12.
    Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press, Ann ArborzbMATHGoogle Scholar
  13. 13.
    Wang W, Brunn P (2000) An effective genetic algorithm for job shop scheduling. Proc Inst Mech Eng B J Eng Manuf 214(4):293–300CrossRefGoogle Scholar
  14. 14.
    Lu C, Wong YS, Fuh JYH (2006) An enhanced assembly planning approach using a multi-objective genetic algorithm. Proc Inst Mech Eng B J Eng Manuf 220(2):255–272CrossRefGoogle Scholar
  15. 15.
    Yildiz AR, Ozturk F (2006) Hybrid enhanced genetic algorithm to select optimal machining parameters in turning operation. Proc Inst Mech Eng B J Eng Manuf 220(12):2041–2053CrossRefGoogle Scholar
  16. 16.
    Liao YG (2003) A genetic algorithm-based fixture locating positions and clamping schemes optimization. Proc Inst Mech Eng B J Eng Manuf 217(8):1075–1083CrossRefGoogle Scholar
  17. 17.
    Sing PK, Jain SC, Jain PK (2005) Comparative study of genetic algorithm and simulated annealing for optimal tolerance design formulated with discrete and continuous variables. Proc Inst Mech Eng B J Eng Manuf 219(10):735–758CrossRefGoogle Scholar
  18. 18.
    Ball MO, Magazine MJ (1988) Sequencing of insertions in printed circuit board assembly. Oper Res 36(2):192–201MathSciNetCrossRefGoogle Scholar
  19. 19.
    Ahmadi RH, Mamer JW (1999) Routing heuristics for automated pick and place machines. Eur J Oper Res 117:533–552CrossRefGoogle Scholar
  20. 20.
    Klomp C, van de Klundert J, Spieksma F, Voogt S (2000) The feeder rack assignment problem in PCB assembly: a case study. Int J Prod Econ 64:399–407CrossRefGoogle Scholar
  21. 21.
    Li S, Hu C, Tian F (2008) Enhancing optimal feeder assignment of the multi-head surface mounting machine using genetic algorithms. Appl Soft Comput 8:522–529CrossRefGoogle Scholar
  22. 22.
    Grunow M, Günther HO, Schleusener M, Yilmaz IO (2004) Operations planning for collect-and-place machines in PCB assembly. Comput Ind Eng 47:409–429CrossRefGoogle Scholar
  23. 23.
    Sohn J, Park S (1996) Efficient operation of a surface mounting machine with a multihead turret. Int J Prod Res 34(4):1131–1143CrossRefGoogle Scholar
  24. 24.
    Broad K, Mason A, Ronnqvist M, Frater M (1996) Optimal robotic component placement. J Oper Res Soc 47:1343–1354CrossRefGoogle Scholar
  25. 25.
    Deo S, Javadpour R, Knapp GM (2002) Multiple setup PCB assembly planning using genetic algorithms. Comput Ind Eng 42:1–16CrossRefGoogle Scholar
  26. 26.
    Ho W, Ji P PCB assembly line assignment: a genetic algorithm approach. J Manuf Technol Manag 16(6):682–692CrossRefGoogle Scholar
  27. 27.
    Ellis KP, Vittes FJ, Kobza JE (2001) Optimizing the performance of a surface mount placement machine. IEEE Trans Electron Packag Manuf 24(3):160–170CrossRefGoogle Scholar
  28. 28.
    Magyar G, Johnsson M, Nevalainen O (1999) On solving single machine optimization problems in electronics assembly. J Electron Manuf 9(4):249–267CrossRefGoogle Scholar
  29. 29.
    Suna D, Lee T, Kim KH (2005) Component allocation and feeder arrangement for a dual-gantry multi-head surface mounting placement tool. Int J Prod Econ 95:245–264CrossRefGoogle Scholar
  30. 30.
    Kulak O, Yilmaz IO, Günther HO (2007) PCB assembly scheduling for collect-and-place machines using genetic algorithms. Int J Prod Res 45(17):3949–3969CrossRefGoogle Scholar
  31. 31.
    Garcia-Naijera A, Brizuela CA (2005) PCB assembly: an efficient genetic algorithm for slot assignment and component pick and place sequence problems. 2005 IEEE Congress on Evol Comput 2:1485–1492CrossRefGoogle Scholar
  32. 32.
    Hong DS, Cho HS (1999) A genetic-algorithm-based approach to the generation of robotic assembly sequences. Control Eng Pract 7:151–159CrossRefGoogle Scholar
  33. 33.
    Taha Z, Tahriri F (2010) Optimizing the robot traveling time (ORTT) in robot assembly line balancing problem (RALBP), The 11th Asia Pacific Industrial Engineering and Management Systems ConferenceGoogle Scholar

Copyright information

© The Author(s) 2018

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • K. T. Andrzejewski
    • 1
  • M. P. Cooper
    • 1
  • C. A. Griffiths
    • 1
  • C. Giannetti
    • 1
  1. 1.College of EngineeringSwansea UniversitySwanseaUK

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