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Optimization of cutting parameters for improving exit delamination, surface roughness, and production rate in drilling of CFRP composites

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Abstract

Drilling of carbon fiber reinforced polymer (CFRP) composites is an indispensible operation to produce holes for the assembly of components. This study optimizes the cutting parameters to reduce exit delamination and surface roughness, increase production rate in CFRP drilling. Firstly, a full factorial experiment is carried out to examine the effects of spindle speed and feed rate upon exit delamination and surface roughness of drilled holes. Analysis of variance (ANOVA) of experimental data indicates that feed rate has predominant influences on both delamination factor (Fd-out) and average surface roughness (Ra), accounting for large contributions of 93.74% and 70.39%, respectively. Secondly, a multi-objective optimization model is constructed with Fd-out, Ra, and material removal rate (MRR) as the optimization goals; Fd-out and Ra are related to the cutting parameters by regression analysis. Modified non-dominated sorting genetic algorithm II (NSGA-II) is applied to solve the optimization model. The obtained Pareto front consists of 195 Pareto optimal solutions widely distributed in the objective space, and the reliability of Pareto front is checked with the global convergence and spacing distance. Finally, preference relations-based decision-making is implemented to identify key solutions of better performance tradeoffs from the Pareto front. This study provides a feasible way to determine the appropriate cutting parameters, with which demands for multiple responses could be satisfied simultaneously in practical machining operations.

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References

  1. Gaugel S, Sripathy P, Haeger A, Meinhard D, Bernthaler T, Lissek F, Kaufeld M, Knoblauch V, Schneider G (2016) A comparative study on tool wear and laminate damage in drilling of carbon-fiber reinforced polymers (CFRP). Compos Struct 155:173–183

    Article  Google Scholar 

  2. Xu JY, Mansori ME (2016) Experimental study on drilling mechanisms and strategies of hybrid CFRP/Ti stacks. Compos Struct 157:461–482

    Article  Google Scholar 

  3. Geier N, Pereszlai C (2020) Analysis of characteristics of surface roughness of machined CFRP composites. Periodica Polytechnica Mechanical Engineering 64(1):67–80

    Article  Google Scholar 

  4. Isbilir O, Ghassemieh E (2013) Numerical investigation of the effects of drill geometry on drilling induced delamination of carbon fiber reinforced composites. Compos Struct 105:126–133

    Article  Google Scholar 

  5. Karpat Y, Değer B, Bahtiyar O (2012) Drilling thick fabric woven CFRP laminates with double point angle drills. J Mater Process Technol 212:2117–2127

    Article  Google Scholar 

  6. Geier N, Davim JP, Szalay T (2019) Advanced cutting tools and technologies for drilling carbon fibre reinforced polymer (CFRP) composites: a review. Compos A Appl Sci Manuf 125:105552

    Article  Google Scholar 

  7. Qi ZC, Zhang KF, Li Y, Liu SN, Cheng H (2014) Critical thrust force predicting modeling for delamination-free drilling of metal-FRP stacks. Compos Struct 107:604–609

    Article  Google Scholar 

  8. Pereszlai C, Geier N, Poór DI, Balázs BZ, Póka G (2021) Drilling fibre reinforced polymer composites (CFRP and GFRP): an analysis of the cutting force of the tilted helical milling process. Compos Struct 262:113646

    Article  Google Scholar 

  9. Krishnamoorthy A, Boopathy SR, Palanikumar K, Davim JP (2012) Application of grey fuzzy logic for the optimization of drilling parameters for CFRP composites with multiple performance characteristics. Measurement 45(5):1286–1296

    Article  Google Scholar 

  10. Ameur MF, Habak M, Kenane M, Aouici H, Cheikh M (2016) Machinability analysis of dry drilling of carbon/epoxy composites: cases of exit delamination and cylindricity error. Int J Adv Manuf Technol 88(9-12):1–15

    Google Scholar 

  11. Romoli L, Lutey AHA (2019) Quality monitoring and control for drilling of CFRP laminates. J Manuf Process 40:16–26

    Article  Google Scholar 

  12. Krishnaraj V, Prabukarthi A, Ramanathan A, Elanghovan N, Kumar MS, Zitoune R, Davim JP (2012) Optimization of machining parameters at high speed drilling of carbon fiber reinforced plastic (CFRP) laminates. Compos B Eng 43:1791–1799

    Article  Google Scholar 

  13. Abhishek K, Datta S, Mahapatra SS (2016) Multi-objective optimization in drilling of CFRP (polyester) composites: application of a fuzzy embedded harmony search (HS) algorithm. Measurement 77:222–239

    Article  Google Scholar 

  14. Shahrajabian H, Farahnakian M (2013) Modeling and multi-constrained optimization in drilling process of carbon fiber reinforced epoxy composite. Int J Precis Eng Manuf 14(10):1829–1837

    Article  Google Scholar 

  15. Das I (1999) On characterizing the knee of the Pareto curve based on normal-boundary intersection. Struct Optim 18(2–3):107–115

    Article  Google Scholar 

  16. Su F, Zheng L, Sun FJ, Wang ZH, Deng ZH, Qiu XY (2018) Novel drill bit based on the step-control scheme for reducing the CFRP delamination. J Mater Process Technol 262:157–167

    Article  Google Scholar 

  17. Karnik SR, Gaitonde VN, Rubio JC, Correia AE, Abrão AM, Davim JP (2008) Delamination analysis in high speed drilling of carbon fiber reinforced plastics (CFRP) using artificial neural network model. Mater Des 29:1768–1776

    Article  Google Scholar 

  18. Wang CY, Ming WW, An QL, Chen M (2017) Machinability characteristics evolution of CFRP in a continuum of fiber orientation angles. Mater Manuf Process 32(9):1041–1050

    Article  Google Scholar 

  19. Xu JY, An QL, Chen M (2014) A comparative evaluation of polycrystalline diamond drills in drilling high-strength T800S/250F CFRP. Compos Struct 117:71–82

    Article  Google Scholar 

  20. Girot F, Dau F, Gutiérrez-Orrantia ME (2017) New analytical model for delamination of CFRP during drilling. J Mater Process Technol 240:332–343

    Article  Google Scholar 

  21. Karimi NZ, Heidary H, Minak G (2016) Critical thrust and feed prediction models in drilling of composite composites. Compos Struct 148:19–26

    Article  Google Scholar 

  22. Joshi S, Rawat K, Balan ASS (2018) A novel approach to predict the delamination factor for dry and cryogenic drilling of CFRP. J Mater Process Technol 262:521–531

    Article  Google Scholar 

  23. International Standard Organization I. Geometrical product specifications (GPS) — surface texture: profile method. Rules and procedures for the assessment of surface texture (1996). p8

  24. Wang HX, Zhang XH, Duan YG (2018) Effects of drilling area temperature on drilling of carbon fiber reinforced polymer composites due to temperature-dependent properties. Int J Adv Manuf Technol 96(5-8):2943–2951

    Article  Google Scholar 

  25. Hou GY, Zhang KF, Fan XT, Luo B, Cheng H, Yan XY, Li Y (2020) Analysis of exit-ply temperature characteristics and their effects on occurrence of exit-ply damages during UD CFRP drilling. Compos Struct 231:111456

    Article  Google Scholar 

  26. Wang Q, Jia XL (2020) Multi-objective optimization of CFRP drilling parameters with a hybrid method integrating the ANN, NSGA-II and fuzzy C-means. Compos Struct 235:111803

    Article  Google Scholar 

  27. Khashaba UA, IA EI-S, Selmy AI, Megahed AA (2010) Machinability analysis in drilling woven GFR/epoxy composites: part I-effect of machining parameters. Compos A Appl Sci Manuf 41(3):391–400

    Article  Google Scholar 

  28. Sánchez MS, Ortiz MC, Sarabia LA (2016) A useful tool for computation and interpretation of trading-off solutions through Pareto-optimal front in the field of experimental designs for mixtures. Chemom Intell Lab Syst 158:210–217

    Article  Google Scholar 

  29. Srinivas N, Deb K (1995) Multiobjective optimization using nondominated sorting in genetic algorithms. Evol Comput 2:221–248

    Article  Google Scholar 

  30. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  31. Cao X, Wen ZG, Xu JJ, Clercq DD, Wang YH, Tao YJ (2020) Many-objective optimization of technology implementation in the industrial symbiosis system based on a modified NSGA-III. J Clean Prod 245:118810

    Article  Google Scholar 

  32. Wang YH, Chen C, Tao Y, Wen ZG, Chen B, Zhang H (2019) A many-objective optimization of industrial environmental management using NSGA-III: a case of China’s iron and steel industry. Appl Energy 242:46–56

    Article  Google Scholar 

  33. Bhattacharjee KS, Singh HK, Ryan M, Ray T (2017) Bridging the gap: many-objective optimization and informed decision-making. IEEE Trans Evol Comput 21(5):813–820

    Article  Google Scholar 

  34. Xu ZS (1999) Study on the relation between two classes of scales in AHP. Syst Eng-Theory & Pract 19(7):97–101

    Google Scholar 

  35. Zhang HM (2016) A goal programming model of obtaining the priority weights from an interval preference relation. Inf Sci 354:197–210

    Article  Google Scholar 

  36. Rajmohana T, Palanikumar K, Prakash S (2013) Grey-fuzzy algorithm to optimise machining parameters in drilling of hybrid metal matrix composites. Compos B Eng 50:297–308

    Article  Google Scholar 

  37. Hong ZN, Liu CB, Li JL (2012) Parameter optimization for machined round parts by using grey relational analysis. In: Luo J. (eds) Affective computing and intelligent interaction. AISC 137:441–448

    Google Scholar 

  38. Torres M, Pelta DA, Lamata MT, Yager RR (2021) An approach to identify solutions of interest from multi and many-objective optimization problems. Neural Comput & Applic 33:2471–2481

    Article  Google Scholar 

  39. Bechikh S, Said LB, Ghédira K (2011) Searching for knee regions of the Pareto front using mobile reference points. Soft Comput 15:1807–1823

    Article  Google Scholar 

  40. Rachmawati L, Srinivasan D (2009) Multiobjective evolutionary algorithm with controllable focus on the knees of the Pareto front. IEEE Trans Evol Comput 13(4):810–824

    Article  Google Scholar 

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Availability of data and materials

The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.

Funding

This work was supported by the National Natural Science Foundation of China under grant 52075452.

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Contributions

Qian Wang: methodology, software, validation, investigation, data curation, visualization, writing—original draft, writing—review and editing.

Xiaoliang Jia: supervision, resources.

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Correspondence to Xiaoliang Jia.

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Wang, Q., Jia, X. Optimization of cutting parameters for improving exit delamination, surface roughness, and production rate in drilling of CFRP composites. Int J Adv Manuf Technol 117, 3487–3502 (2021). https://doi.org/10.1007/s00170-021-07918-2

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  • DOI: https://doi.org/10.1007/s00170-021-07918-2

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