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Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

  • Ricardo Coppel
  • Jose V. Abellan-Nebot
  • Hector R. SillerEmail author
  • Ciro A. Rodriguez
  • Federico Guedea
ORIGINAL ARTICLE

Abstract

Nowadays, the miniaturization of many consumer products is extending the use of micro-milling operations with high-quality requirements. However, the impacts of cutting-tool wear on part dimensions, form and surface integrity are not negligible and part quality assurance for a minimum production cost is a challenging task. In fact, industrial practices usually set conservative cutting parameters and early cutting replacement policies in order to minimize the impact of cutting-tool wear on part quality. Although these practices may ensure part integrity, the production cost is far away to be minimized, especially in highly tool-consuming operations like mold and die micro-manufacturing. In this paper, an adaptive control optimization (ACO) system is proposed to estimate cutting-tool wear in terms of part quality and adapt the cutting conditions accordingly in order to minimize the production cost, ensuring quality specifications in hardened steel micro-parts. The ACO system is based on: (1) a monitoring sensor system composed of a dynamometer, (2) an estimation module with Artificial Neural Networks models, (3) an optimization module with evolutionary optimization algorithms, and (4) a CNC interface module. In order to operate in a nearly real-time basis and facilitate the implementation of the ACO system, different evolutionary optimization algorithms are evaluated such as particle swarm optimization (PSO), genetic algorithms (GA), and simulated annealing (SA) in terms of accuracy, precision, and robustness. The results for a given micro-milling operation showed that PSO algorithm performs better than GA and SA algorithms under computing time constraints. Furthermore, the implementation of the final ACO system reported a decrease in the production cost of 12.3 and 29 % in comparison with conservative and high-production strategies, respectively.

Keywords

Micro-milling Hardened steels Adaptive control Intelligent machining systems 

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References

  1. 1.
    Bissacco G, Hansen HN, Chiffre LD (2005) Micromilling of hardened tool steel for mould making applications. J Mater Process Technol 167:201–207CrossRefGoogle Scholar
  2. 2.
    Vázquez Lepe E, Amaro A, Ciurana QD, Rodríguez González CÁ (2015) Process planning considerations for micromilling of mould cavities used in ultrasonic moulding technology. Precis Eng 39:252–260CrossRefGoogle Scholar
  3. 3.
    Biermann D, Baschin A, Krebs E, Schlenker J (2011) Manufacturing of dies from hardened tool steels by 3-axis micromilling. Prod Eng Res Dev 5:209–217CrossRefGoogle Scholar
  4. 4.
    De Cristofano S, Funaro N, Feriti GC, Rostango M, Comoglio M, Merlo A, Stefanini C, Dario P (2012) High-speed micro-milling: novel coatings for tool wear reduction. Int J Mach Tools Manuf 63:16–20CrossRefGoogle Scholar
  5. 5.
    Aramcharoen A, Mativenga PT, Yang S, Cooke KE, Teer DG (2008) Evaluation and selection of hard coatings for micro milling of hardened tool steel. Int J Mach Tools Manuf 48:1578–1584CrossRefGoogle Scholar
  6. 6.
    Aramcharoen A, Mativenga PT (2008) Size effect and tool geometry in micromilling of tool steel. Precis Eng 33:402–407Google Scholar
  7. 7.
    Stephenson DA, Agapiou JS (1997) Metal cutting theory and practice. Marcel Dekker, New YorkGoogle Scholar
  8. 8.
    Abellan JV, Romero F, Siller HR, Estruch A, Vila C (2008) Adaptive control optimization of cutting parameters for high quality machining operations based on neural networks and search algorithms, Advances in robotics, automation and control. InTech, Austria, pp 472–491Google Scholar
  9. 9.
    Koren Y (1989) Adaptive control systems for machining. Manuf Rev 2:6–15Google Scholar
  10. 10.
    Coker SA, Shin YC (1996) In-process control of surface roughness due to tool wear using a new ultrasonic system. Int J Mach Tools Manuf 36:411–422CrossRefGoogle Scholar
  11. 11.
    Chiang S-T, Liu DI, Lee A-C, Chieng W-H (1995) Adaptive control optimization in end milling using neural networks. Int J Mach Tools Manuf 34:637.660Google Scholar
  12. 12.
    Malekian M, Park SS, Jun MBG (2009) Tool wear monitoring of micro-milling operations. J Mater Process Technol 209:4903–4914CrossRefGoogle Scholar
  13. 13.
    Tansel I, Trujillo M, Nedouyan A, Velez C, Bao W-Y, Arkan TT, Tansel B (1998) Micro-end-milling—III. Wear estimation and tool breakage detection using acoustic emission signals. Int J Mach Tools Manuf 38:1449–1466CrossRefGoogle Scholar
  14. 14.
    Jemenielniak K, Arrazola PJ (2008) Application of AE and cutting force signals in tool condition monitoring in micro-milling. CIRP J Manuf Sci Technol 1:97–102CrossRefGoogle Scholar
  15. 15.
    Hsieh W-H, Lu M-C, Chiou S-J (2012) Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling. Int J Adv Manuf Technol 61:53–61CrossRefGoogle Scholar
  16. 16.
    López de Lacalle LN, Lamikiz A, Sánchez JA, Fernández de Bustos I (2006) Recording of real cutting forces along the milling of complex parts. Mechatronics 16:21–32CrossRefGoogle Scholar
  17. 17.
    Huang S, Tan KK, Hong GS, Wong YS (2007) Cutting force control of milling machine. Mechatronics 17:533–541CrossRefGoogle Scholar
  18. 18.
    Kunpeng Z, San WY, Soon HG (2009) Wavelet analysis of sensor signals for tool condition monitoring: a review and some new results. Int J Mach Tools Manuf 49:537–553CrossRefGoogle Scholar
  19. 19.
    Abellán-Nebot JV, Subirón FR (2010) A review of machining monitoring systems based on artificial intelligence process models. Int J Adv Manuf Technol 47:237–257CrossRefGoogle Scholar
  20. 20.
    Elangovan M, Sugumaran V, Ramachandran KI, Ravikumar S (2011) Effect of SVM kernel functions on classification of vibration signals of a single point ``. Expert Syst Appl 38:15202–15207Google Scholar
  21. 21.
    Dimla DE Sr (2000) Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods. Int J Mach Tools Manuf 40:1073–1098CrossRefGoogle Scholar
  22. 22.
    Saedon JB, Soo SL, Aspinwall DK, Barnacle A, Saad NH (2012) Prediction and optimization of tool life in micromilling AISI D2 (62 HRC) hardened steel. Procedia Eng 41:1674–1683CrossRefGoogle Scholar
  23. 23.
    Silva JA, Abellán-Nebot JV, Siller HR, Guedea-Elizalde F (2012) Adaptive control optimization system for minimizing production cost in hard milling operations. Int J Comput Integr Manuf 27:1–13Google Scholar
  24. 24.
    Vallejo AJ, Morales-Mendez R (2010) Cost-effective supervisory control system in peripheral milling using HSM. Annu Rev Control 34:155–162CrossRefGoogle Scholar
  25. 25.
    Saikumar S, Shunmugam MS (2011) Development of a feed rate adaption control system for high-speed rough and finish end milling of hardened EN24 steel. Int J Adv Manuf Technol 59:869–884CrossRefGoogle Scholar
  26. 26.
    Zuperl U, Cus F, Reibenschuh M (2011) Neural control strategy of constant cutting force system in end milling. Robot Comput Integr Manuf 27:485–493CrossRefGoogle Scholar
  27. 27.
    Liu Y, Zuo L, Wang C (1999) Intelligent adaptive control in milling processes. Int J Comput Integr Manuf 12:453–460CrossRefGoogle Scholar
  28. 28.
    Dweiri F, Al-Jarrah M, Al-Wedyan H (2003) Fuzzy surface roughness modeling of CNC down milling of alumic-79. J Mater Process Technol 133:266–275CrossRefGoogle Scholar
  29. 29.
    Ho SY, Lee KC, Chen SS, Ho SJ (2002) Accurate modeling and prediction of surface roughness by computer vision operations using an adaptive neuro-fuzzy inference system. Int J Mach Tools Manuf 42:1441–1446CrossRefGoogle Scholar
  30. 30.
    Lou SJ, Chen JC (1999) In-process surface roughness recognition (ISRR) system in end-milling operations. Int J Adv Manuf Technol 15:200–209CrossRefGoogle Scholar
  31. 31.
    Chen JC, Savage M (2001) A fuzzy-net-based multilevel in-process surface roughness recognition system in milling operations. Int J Adv Manuf Technol 17:670–676CrossRefGoogle Scholar
  32. 32.
    Dey S, Stori JA (2005) A Bayesian network approach to root cause diagnosis of process variations. Int J Mach Tools Manuf 45:75–91CrossRefGoogle Scholar
  33. 33.
    Correa M, Bielza C, de Ramirez M, Alique J (2008) A Bayesian network model for surface roughness prediction in the machining process. Int J Syst Sci 39:1181–1192CrossRefzbMATHGoogle Scholar
  34. 34.
    Salgado DR, Alonso FJ, Cambero I, Marcelo A (2009) In-process surface roughness prediction system using cutting vibrations in turning. Int J Adv Manuf Technol 43(1–2):40–51CrossRefGoogle Scholar
  35. 35.
    Caydas U, Ekici S (2012) Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel. J Intell Manuf 23:639–650CrossRefGoogle Scholar
  36. 36.
    Shi D, Gindy NN (2007) Tool wear predictive model based on least squares support vector machines. Mech Syst Signal Process 21:1799–1814CrossRefGoogle Scholar
  37. 37.
    Abellan-Nebot JV (2010) A review of artificial intelligent approaches applied to part accuracy prediction. Int J Mach Mach Mater 1–2:6–37Google Scholar
  38. 38.
    Dimla DW Jr, Lister PM, Leighton NJ (1997) Neural network solutions to the tool condition monitoring problem in metal cutting—a critical review of methods. Int J Adv Manuf Technol 37:1219–1241Google Scholar
  39. 39.
    Yusup N, Zain AM, Hashim SZM (2012) Evolutionary techniques in optimizing machining parameters: review and recent applications (2007–2011). Expert Syst Appl 39:9909–9927CrossRefGoogle Scholar
  40. 40.
    Chandrasekaran M, Muralidhar M, Krishna USD (2010) Application of soft computing techniques in machining performance prediction and optimization: a literature review. Int J Adv Manuf Technol 46:445–464CrossRefGoogle Scholar
  41. 41.
    Chen XQ, Li HZ (2009) Development of a tool wear observer model for online tool. Int J Adv Manuf Technol 45:786–800CrossRefGoogle Scholar
  42. 42.
    De Brabanter K, Karsmakers P, Ojeda F, Alzate C, De Brabanter J, Pelckmans K (2011) LS-SVM lab toolbox users guide version 1.8, Leuven, BelgiumGoogle Scholar
  43. 43.
    Wang ZG, Wong YS, Rahman M (2004) Optimisation of multi-pass milling using genetic algorithm and genetic simulated annealing. Int J Adv Manuf Technol 24:727–732CrossRefGoogle Scholar
  44. 44.
    Aggarwal S, Xirouchakis P (2013) Selecting of optimal cutting conditions for pocket milling using genetic algorithm. Int J Adv Manuf Technol 66:1943–1958CrossRefGoogle Scholar
  45. 45.
    Rai JK, Brand D, Slama M, Xirouchakis P (2011) Optimal selection of cutting parameters in multi-tool milling operations. Int J Prod Res 49:3045–3068CrossRefGoogle Scholar
  46. 46.
    Wibowo A, Ishak Desa M (2012) Kernel based regression and genetic algorithms for estimating cutting conditions. Expert Syst Appl 39:11634–11641CrossRefGoogle Scholar
  47. 47.
    Deepak U (2011) Optimization of milling operation using genetic and PSO algorithm. International Journal of Software Engineering and Soft Computing 1Google Scholar
  48. 48.
    Lee YZ, Ponnambalam SG (2012) Optimisation of multipass turning operations using PSO and GA-AIS algorithms. Int J Prod Res 50:6499–6518CrossRefGoogle Scholar
  49. 49.
    Lin C-J, Lin P-T (2012) Particle swarm optimization based feedforward controller for a XY PZT positioning stage. Mechatronics 22:614–628CrossRefGoogle Scholar
  50. 50.
    Bharathi Raja S, Baskar N (2010) Optimization techniques for machining operations: a retrospective research based on various mathematical models. Int J Adv Manuf Technol 48:1075–1090CrossRefGoogle Scholar
  51. 51.
    Mohd Zain A, Haron H, Sharif S (2010) Simulated Annealing to estimate the optimal cutting conditions for minimizing surface roughness in end milling Ti–6Al–4V. Mach Sci Technol 14:43–62CrossRefGoogle Scholar
  52. 52.
    Somashekhar KP, Mathew J, Ramachandran N (2012) A feasibility approach by simulated annealing on optimization of micro-wire electric discharge machining parameters. Int J Adv Manuf Technol 61:1209–1213CrossRefGoogle Scholar
  53. 53.
    Olvera D, Elías-Zúñiga A, Martínez-Alfaro H, López de Lacalle LN, Rodríguez CA, Campa FJ (2014) Determination of the stability lobes in milling operations based on homotopy and simulated annealing techniques. Mechatronics 24:177–185CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  • Ricardo Coppel
    • 1
  • Jose V. Abellan-Nebot
    • 2
  • Hector R. Siller
    • 1
    Email author
  • Ciro A. Rodriguez
    • 1
  • Federico Guedea
    • 1
  1. 1.Tecnologico de MonterreyMonterreyMexico
  2. 2.Department of Industrial Systems Engineering and DesignUniversitat Jaume ICastellónSpain

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