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ANFIS-TLBO-based optimization of drilling parameters to minimize burr formation in aluminum 6061

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Abstract

This research paper presents an experimental investigation conducted on a radial drilling machine, focusing on burr formation on aluminum 6061 material under wet conditions. The primary objective is to identify the best drilling parameters that lead to minimal burr height and thickness while also developing an accurate burr prediction model. A total of 27 experimental runs were conducted according to the Taguchi method, involving three main drilling parameters: drill diameter, point angle, and spindle speed. Next, two distinct prediction models for burr height and thickness were developed using Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) based on the experimental data. Key findings reveal the superiority of the ANFIS-based predicted model over the ANN model in terms of predictive accuracy. In the next phase of the work, the developed ANFIS models underwent optimization utilizing the Teaching–Learning-Based Optimization (TLBO) algorithm, culminating in further enhancements. The research identifies optimal values for drill diameter, point angle, and spindle speed, demonstrating their effectiveness in minimizing burr height and thickness. Specifically, the optimal parameters for minimizing burr height were determined as 12 mm for drill diameter, 99.8 degrees for point angle, and 2000 rpm for spindle speed, while for minimizing burr thickness, the optimal values were 8 mm for drill diameter, 111.6 degrees for point angle, and 2000 rpm for spindle speed, respectively. Crucially, the prediction models were validated through new experiments, affirming their reliability and efficacy. This study is notable for its novel merging of ANFIS and TLBO, a combination that is rarely investigated in burr reduction studies. This hybrid approach has shown to be a significant advancement in precision production, providing a potential path for industries to improve efficiency, save costs, and improve product quality through better burr control.

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References

  • Asadi R, Yeganefar A, Niknam AS (2019) Optimization and prediction of surface quality and cutting forces in the milling of the aluminium alloys using ANFIS and interval type 2 neuro fuzzy network couple with population-based meta-heuristic learning methods. Int J Adv Manuf Technol 105:2271–2287

    Article  Google Scholar 

  • Bahçe E, Özdemir B (2019) Investigation of the burr formation during the drilling of free-form surfaces in al 7075 alloy. J Market Res 8(5):4198–4208

    Google Scholar 

  • Chandrasekaran M, Tamang T (2017) ANN–PSO integrated optimization methodology for intelligent control of MMC machining. J Inst Eng (india) Ser C 98(4):395–401

    Article  Google Scholar 

  • Dey B, Mondal N, Mondal S (2018) Experimental study to minimize the burr formation in drilling process with artificial neural network analysis. IOP Conf Ser: Mater Sci Eng 377:1–6

    Article  Google Scholar 

  • Fenghe W, Zijian L, Baosu G, Yingbing S, Junyun C (2021) Research on the burr-free interrupted cutting model of metals. J Mater Process Technol 295:117190

    Article  Google Scholar 

  • Gaitonde NV, Karnik RS (2012) Minimizing burr size in drilling using artificial neural network (ANN)-particle swarm optimization (PSO) approach. Int J Adv Manuf Technol 23:1783–1793

    Google Scholar 

  • Gaitonde VN, Karnik SR, Achyutha BT (2006) Methodology of Taguchi optimization for multi-objective drilling problem to minimize burr size. Int J Adv Manuf Technol 34(1):1–8

    Google Scholar 

  • Gaitonde NV, Karnik RS, Achyutha TB, Siddeswarappa B (2008) Genetic algorithm-based burr size minimization in drilling of AISI 316L stainless steel. J Mater Process Technol 197:225–236

    Article  Google Scholar 

  • Gaitonde VN, Karnik SR, Davim JP (2012) Minimising burr size in drilling: integrating response surface methodology with particle swarm optimisation. Mechanical Engineering Series. Research and Development Woodhead Publishing, Cambridge, pp 259–292

    Google Scholar 

  • Gunay M, Meral T (2020) Modelling and multiresponse optimization for minimizing burr height, thrust force and surface roughness in drilling of ferritic stainless steel. Sadhana 45:273. https://doi.org/10.1007/s12046-020-01490-3

    Article  Google Scholar 

  • Huan Z, Wen FD, Zheng L et al (2018) Prediction the grinding force of titanium matrix composites using the genetic algorithm optimizing back propagation neural network model. Proc Inst Mech Eng Part B: J Eng Manuf 233:1157–1167

    Google Scholar 

  • HuaNa Tools (2024) All you need to know about HSS drill bits. https://huanatools.com/introduction-of-high-performance-drill-bits/. Accessed 12 Apr 2024

  • Jang SR (1993) ANFIS adaptive-network-based fuzzy inference systems. IEEE Trans Syst Man Cybern 23(3):665–684

    Article  Google Scholar 

  • Jang J-SR (1993) ANFIS: Adaptive-Network-Based Fuzzy Inference System. Computer Methods and Programs in Biomedicine, IEEE Transactions on Systems, University of California

  • Karnik SR, Gaitonde VN, Davim JP (2008) A comparative study of the ANN and RSm modelling approaches for predicting burr size in drilling. Int J Adv Manuf Technol 38:868–883

    Article  Google Scholar 

  • Kilickap E (2010) Modeling and optimization of burr height in drilling of Al-7075 using Taguchi method and response surface methodology. Int J Adv Manuf Technol 49:911–923

    Article  Google Scholar 

  • Ko L-S, Chang J-E, Yang G-E (2003) Burr minimizing scheme in drilling. J Mater Process Technol 140:237–242

    Article  Google Scholar 

  • Kundu S, Das S, Partha PD (2014) Optimization of drilling parameters to minimize burr by providing back-up support on aluminium alloy. Procedia Eng. https://doi.org/10.1016/j.proeng.2014.12.246

    Article  Google Scholar 

  • Li S, Zhang D, Guo D, Shi Z, Tang H (2018) Modeling and drilling parameters optimization on burr height using harmony search algorithm in low frequency vibration assisted drilling. Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-018-2997-3

    Article  Google Scholar 

  • Li S, Zhang D, Li C, Tang H (2020) Exit burr height mechanistic modeling and experimental validation for low-frequency vibration-assisted drilling of aluminum 7075–T6 alloy. J Manuf Process 56:350–361

    Article  Google Scholar 

  • Mandal RS, Das S, Saha PP (2020) An investigation on formation of burrs during milling of aluminium alloy under wet condition. ProcIMechE Part B: J Eng Manuf. https://doi.org/10.1177/0954405420972012

    Article  Google Scholar 

  • Min S, Dornfeld DA, Kim J et al (2001) Finite element modelling of burr formation in metal cutting. Mach Sci Technol 5:307–322

    Article  Google Scholar 

  • Mondal N, Sardar BS, Halder RN, Das S (2014) Observation of drilling burr and finding out the condition for minimum burr formation. Int J Manuf Eng 2014:1–12

    Google Scholar 

  • Mondal N, Mandal CM, Dey B, Das S (2019) Genetic algorithm based drilling burr minimization using ANFIS and SVR. Proc Inst Mech Eng Part B: J Eng Manuf 221(12):1–13

    Google Scholar 

  • Mondal N, Mandal S, Mandal CM (2020) FPA based optimization of drilling burr using regression analysis and ANN model. Measurement 152:107327

    Article  Google Scholar 

  • Mondal N, Mandal S, Mandal CM, Das S, Halder B (2021) ANN-FPA based modeling and optimization of drilling burrs using RSM and GA. In: Advance in Manufacturing process, Intelligent methods and system in production engineering. vol 335, pp 180–195

  • More JJ (1987) The Levenberg-Marquardt algorithm: implementation and theory. In Numerical analysis lecture notes in mathematics. Springer Berlin Heidelberg, vol 630, pp 105–116

  • Nandi KA, Davim PJ (2009) A study of drilling performances with minimum quantity of lubricant using fuzzy logic rules. Mechatronics 19:218–232

    Article  Google Scholar 

  • Penghui Z, Jinglin T, Ziqiang L, Zhipeng Z, Chaosheng S, Bo Z (2021) Analytical model of exit burr height and experimental investigation on ultrasonic-assisted high-speed drilling micro-holes. J Manuf Process 68:807–817

    Article  Google Scholar 

  • Pereia B, Griffiths AC, Birch B, Rees A (2022) Optimization of an autonomous robotic drilling system for the machining of aluminium aerospace alloys. Int J Adv Manuf Technol 119:2429–2444

    Article  Google Scholar 

  • Ramesh B, Elayaperumal A, Venkatesh R, Madhav S, Jain K (2014) Int J Innov Res Sci EngTechnol (An ISO 3297: 2007 Certified Organization)

  • Ramulu M, Branson T, Kim D (2001) A study on the drilling of composite and titanium stacks. Compos Struct 54:67–77

    Article  Google Scholar 

  • Rao PN (2005) Manufacturing technology metal cutting and machine tools. Tata McGraw-Hill

    Google Scholar 

  • Rao RV, Kalyankar VD (2012) Parameter optimization of machining processes using a new optimization algorithm. Mater Manuf Process: https://doi.org/10.1080/10426914.2011.602792

    Article  Google Scholar 

  • Ruko Shop UK (2023) A-Z guide for drilling aluminium. https://ruko.shop/collections/drills/brand_ruko. Accessed 12 Apr 2024

  • Saha PP, Das S (2011) Burr minimization in face milling: an edge beveling approach. Proc Inst Mech Eng Part B: J Eng Manuf 225:1528–1534

    Article  Google Scholar 

  • Singh J, Garg K, Singh P (2018) Optimization of performance parameters in drilling process for minimizing the burr formation. Asian J Eng Appl Technol 7(S2):127–131

    Article  Google Scholar 

  • Singh SK, Kacker R, Gautam SS, Tamang SK (2023) Multi-objective optimization of thermo-hydraulic behavior of heat exchanger with v-cut twisted tape in axial and radial direction using NSGA-II. Energy Sources Part a: Recovery Utilization Environ Effects 45(4):13057–13076. https://doi.org/10.1080/15567036.2023.2278723

    Article  Google Scholar 

  • Sreenivasulua R, Rao CS, Ravindraa K (2020) Effect of thrust and torque exerted during drilling to optimize exit burr height and thickness by choosing variable drill bit geometry: a simplified theoretical model approach. Int J Data Netw Sci 4:43–56

    Article  Google Scholar 

  • Stein JM (1996) Influence of workpiece exit angle on burr formation in drilling intersection holes. Trans NAMRI/SME 24(1):39–44

    Google Scholar 

  • Tandon P, Khan MR (2009) Three-dimensional modeling and finite element simulation of a generic end mill. Comput Aided Des 41:106–114

    Article  Google Scholar 

  • Varatharajulu M, Jayaprakash G, Baskar N et al (2018) Experimental investigation and multi-objective optimization of Duplex 2304 drilling operation using evolutionary algorithm. Int J Manuf Technol Manage 32:336–357

    Article  Google Scholar 

  • Varatharajulu M, Shahithya R, Jayaprakash G, Baskar N, Paulo Davim J (2020) Evaluation of desirability function approach and grey relation analysis for modeling circularity, perpendicularity and cylindricity in drilling magnesium AZ31. Proc Inst Mech Eng Part E: J Process Mech Eng 235(2):1–11. https://doi.org/10.1177/0954408920952753

    Article  Google Scholar 

  • Varatharajulu M, Duraiselvam M, Jayaprakash G, Baskar N, Karthick P, Anbarasu R (2021a) Modeling and optimization of hole accuracy and drilling time in magnesium AZ31 plate. Surf Rev Lett. https://doi.org/10.1142/S0218625X21500591

    Article  Google Scholar 

  • Varatharajulu M, Duraiselvam M, Kumar A, GabrialKanniyan C, Sathiyamurthy R (2021b) Experimental investigation of the effect of independent parameters in the face milling of aluminum 6082 alloy. Trans Indian Inst Met 74(3):659–677

    Article  Google Scholar 

  • Varghese V, Ramesh MR, Chakradhar D (2018) Experimental investigation and optimization of machining parameters for sustainable machining. Mater Manuf Processes 33:1782–1792

    Article  Google Scholar 

  • Yuvaraj T, Tamang SK, Arivazhagan R, Swapna Sri MN (2024) Enhancing EDM performance on TiN-Si3N4 using a hybrid computation intelligence algorithm (Grey-ANFIS). J Aust Ceram Soc. https://doi.org/10.1007/s41779-024-00994-z

    Article  Google Scholar 

  • Zhang Z, Babitsky IV (2011) Finite element modeling of a micro-drill and experiments on high-speed ultrasonically assisted micro-drilling. J Sound Vib 330:2124–2137

    Article  Google Scholar 

Download references

Acknowledgements

This study received no specific funding from any government, commercial, or non-profit organization. This research work contains no conflicts of interest.

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Authors and Affiliations

  1. Mechanical Engineering Department, Jalpaiguri Government Engineering College, Jalpaiguri, 735102, India

    Nripen Mondal, Soumil Banik, Sumitava Paul & Srija Sarkar

  2. Department of Electronics and Communication Engineering, Jalpaiguri Government Engineering College, Jalpaiguri, 735102, India

    Sudip Mandal

  3. Mechanical Engineering Department, Durgapur Institute of Advanced Technology and Management, Durgapur, 713212, India

    Sudipta Ghosh

Authors
  1. Nripen Mondal
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  6. Sudipta Ghosh
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Contributions

Nripen Mondal: Conceptualization, methodology, Writing—Original Draft, Soumil Banik: methodology, Conceptualization, Writing—Original Draft Sumitava Paul: Software, data curation. Srija Sarkar: Software, data curation, initial paper draft checking Sudip Mandal: Visualization, Investigation, Writing—Review & Editing. Sudipta Ghosh: Software, data curation, Writing—Review & Editing.

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Correspondence to Nripen Mondal.

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This study received no specific funding from any government, commercial, or non-profit organisation. This research work contains no conflicts of interest.

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Mondal, N., Banik, S., Paul, S. et al. ANFIS-TLBO-based optimization of drilling parameters to minimize burr formation in aluminum 6061. Multiscale and Multidiscip. Model. Exp. and Des. (2024). https://doi.org/10.1007/s41939-024-00433-3

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