Skip to main content
Springer Nature Link
Log in

Metaheuristic Optimized Extreme Gradient Boosting Milling Maintenance Prediction

  • Conference paper
  • First Online:
Fourth Congress on Intelligent Systems (CIS 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 868))

Included in the following conference series:

  • 140 Accesses

Abstract

Machining plays a crucial role in modern manufacturing, relying on automated processes to efficiently create complex parts through subtractive like lathe turning and cutting. However, a major concern in this manufacturing process is tool wear, necessitating a robust system for proactive malfunction detection. To keep up with advancements and meet the increasing demands of speed and precision, artificial intelligence (AI) emerges as a promising solution. However, AI algorithms often require fine-tuning of hyperparameters, which poses a challenge. Swarm intelligence algorithms, inspired by collaborative behaviors observed in nature, offer a potential solution. By applying swarm intelligence to hyperparameter optimization, AI algorithms can achieve optimized models that address time and hardware constraints. This work proposes a methodology based on Extreme Gradient Boosting (XGBoost) for forecasting malfunctions. Additionally, a modified optimization metaheuristic is introduced to specifically enhance the performance of this methodology. To evaluate the proposed approach, it has been applied to a real-world dataset and compared to several well-known optimizers. The results demonstrate admirable performance, highlighting the potential of swarm intelligence in achieving efficient and effective machining processes.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
€32.70 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Austria)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 171.19
Price includes VAT (Austria)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 219.99
Price includes VAT (Austria)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://www.kaggle.com/datasets/stephanmatzka/predictive-maintenance-dataset-ai4i-2020.

References

  1. Abdulrahman SM (2017) Using swarm intelligence for solving NP-Hard problems. Acad J Nawroz Univ 6(3):46–50

    Google Scholar 

  2. Ahmad MI, Saif Y, Yusof Y, Daud ME, Latif K, Kadir AZA (2022) A case study: monitoring and inspection based on IoT for milling process. Int J Adv Manuf Technol 1–11

    Google Scholar 

  3. Al Hosni N, Jovanovic L, Antonijevic M, Bukumira M, Zivkovic M, Strumberger I, Mani JP, Bacanin N (2022) The XGBoost model for network intrusion detection boosted by enhanced sine cosine algorithm. In: Third international conference on image processing and capsule networks: ICIPCN 2022. Springer, Berlin, pp 213–228

    Google Scholar 

  4. Bacanin N, Jovanovic L, Zivkovic M, Kandasamy V, Antonijevic M, Deveci M, Strumberger I (2023) Multivariate energy forecasting via metaheuristic tuned long-short term memory and gated recurrent unit neural networks. Inf Sci 642:119122. https://doi.org/10.1016/j.ins.2023.119122

  5. Bacanin N, Zivkovic M, Antonijevic M, Venkatachalam K, Lee J, Nam Y, Marjanovic M, Strumberger I, Abouhawwash M (2023) Addressing feature selection and extreme learning machine tuning by diversity-oriented social network search: an application for phishing websites detection. Complex Intell Syst. https://doi.org/10.1007/s40747-023-01118-z

  6. Bacanin N, Zivkovic M, Stoean C, Antonijevic M, Janicijevic S, Sarac M, Strumberger I (2022) Application of natural language processing and machine learning boosted with swarm intelligence for spam email filtering. Mathematics 10(22). https://doi.org/10.3390/math10224173; https://www.mdpi.com/2227-7390/10/22/4173

  7. Bahga A, Madisetti VK (2011) Analyzing massive machine maintenance data in a computing cloud. IEEE Trans Parallel Distrib Syst 23(10):1831–1843

    Google Scholar 

  8. Balaji BS, Paja W, Antonijevic M, Stoean C, Bacanin N, Zivkovic M (2023) IoT integrated edge platform for secure industrial application with deep learning. Hum Centric Comput Inf Sci 13

    Google Scholar 

  9. Chavoshi SZ, Goel S, Morantz P (2017) Current trends and future of sequential micro-machining processes on a single machine tool. Mater Des 127:37–53

    Google Scholar 

  10. Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 785–794

    Google Scholar 

  11. Djuric M, Jovanovic L, Zivkovic M, Bacanin N, Antonijevic M, Sarac M (2023) The AdaBoost approach tuned by SNS metaheuristics for fraud detection. In: Proceedings of the international conference on paradigms of computing, communication and data sciences: PCCDS 2022. Springer, Berlin, pp 115–128

    Google Scholar 

  12. Gurrola-Ramos J, Hernàndez-Aguirre A, Dalmau-Cedeño O (2020) COLSHADE for real-world single-objective constrained optimization problems. In: 2020 IEEE congress on evolutionary computation (CEC). IEEE, pp 1–8

    Google Scholar 

  13. Jovanovic L, Bacanin N, Antonijevic M, Tuba E, Ivanovic M, Venkatachalam K (2022) Plant classification using firefly algorithm and support vector machine. In: 2022 IEEE Zooming innovation in consumer technologies conference (ZINC). IEEE, pp 255–260

    Google Scholar 

  14. Jovanovic L, Bacanin N, Zivkovic M, Antonijevic M, Jovanovic B, Sretenovic MB, Strumberger I (2023) Machine learning tuning by diversity oriented firefly metaheuristics for industry 4.0. Expert Syst e13293

    Google Scholar 

  15. Jovanovic L, Djuric M, Zivkovic M, Jovanovic D, Strumberger I, Antonijevic M, Budimirovic N, Bacanin N (2023) Tuning XGBoost by planet optimization algorithm: an application for diabetes classification. In: Proceedings of fourth international conference on communication, computing and electronics systems: ICCCES 2022. Springer, Berlin, pp 787–803

    Google Scholar 

  16. Jovanovic L, Jovanovic D, Bacanin N, Jovancai Stakic A, Antonijevic M, Magd H, Thirumalaisamy R, Zivkovic M (2022) Multi-step crude oil price prediction based on LSTM approach tuned by Salp Swarm algorithm with disputation operator. Sustainability 14(21):14616

    Google Scholar 

  17. Jovanovic L, Jovanovic D, Antonijevic M, Nikolic B, Bacanin N, Zivkovic M, Strumberger I (2023) Improving phishing website detection using a hybrid two-level framework for feature selection and XGBoost tuning. J Web Eng 22(03):543–574. https://doi.org/10.13052/jwe1540-9589.2237, https://journals.riverpublishers.com/index.php/JWE/article/view/18475

  18. Jovanovic L, Milutinovic N, Gajevic M, Krstovic J, Rashid TA, Petrovic A (2022) Sine cosine algorithm for simple recurrent neural network tuning for stock market prediction. In: 2022 30th Telecommunications forum (TELFOR). IEEE, pp 1–4

    Google Scholar 

  19. Jovanovic L, Zivkovic M, Antonijevic M, Jovanovic D, Ivanovic M, Jassim HS (2022) An emperor penguin optimizer application for medical diagnostics. In: 2022 IEEE zooming innovation in consumer technologies conference (ZINC). IEEE, pp 191–196

    Google Scholar 

  20. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39:459–471

    Google Scholar 

  21. Luo W, Lin X, Li C, Yang S, Shi Y (2022) Benchmark functions for CEC 2022 competition on seeking multiple optima in dynamic environments. arXiv preprint arXiv:2201.00523

  22. Matzka S (2020) Explainable artificial intelligence for predictive maintenance applications. In: 2020 Third international conference on artificial intelligence for industries (ai4i). IEEE, pp 69–74

    Google Scholar 

  23. Mikic D, Desnica E, Asonja A, Stojanovic B, Epifanic-Pajic V (2016) Reliability analysis of ball bearing on the crankshaft of piston compressors. J Balkan Tribol Assoc

    Google Scholar 

  24. Milutinovic N, Cabarkapa S, Zivkovic M, Antonijevic M, Mladenovic D, Bacanin N (2023) Tuning artificial neural network for healthcare 4.0. by sine cosine algorithm. In: 2023 International conference on intelligent data communication technologies and internet of things (IDCIoT), pp 510–513. https://doi.org/10.1109/IDCIoT56793.2023.10053543

  25. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Google Scholar 

  26. Mirjalili S, Mirjalili S (2019) Genetic algorithm. In: Evolutionary algorithms and neural networks: theory and applications, pp 43–55

    Google Scholar 

  27. Móricz L, Viharos ZJ, Németh A, Szépligeti A, Büki M (2020) Off-line geometrical and microscopic & on-line vibration based cutting tool wear analysis for micro-milling of ceramics. Measurement 163:108025

    Google Scholar 

  28. Novakovic B, Durdev M, Radovanovic L, Speight JG (2018) Optimization of manufacturing processes using modern automated CNC milling machines. Appl Eng Lett J Eng Appl Sci 3(4):124–128. https://doi.org/10.18485/aeletters.2018.3.4.2, https://www.aeletters.com/wp-content/uploads/2019/01/AEL00078.pdf

  29. Petrovic A, Antonijevic M, Strumberger I, Jovanovic L, Savanovic N, Janicijevic S (2023) The XGBoost approach tuned by TLB metaheuristics for fraud detection. In: Proceedings of the 1st international conference on innovation in information technology and business (ICIITB 2022), vol 104. Springer Nature, Berlin, p 219

    Google Scholar 

  30. Said NHAM, Yusof Y (2022) Applied internet of things (IoT) in temperature and vibration monitoring system for milling machine. Res Prog Mech Manuf Eng 3(1):476–485

    Google Scholar 

  31. Salb M, Jovanovic L, Zivkovic M, Tuba E, Elsadai A, Bacanin N (2022) Training logistic regression model by enhanced moth flame optimizer for spam email classification. In: Computer networks and inventive communication technologies: proceedings of fifth, ICCNCT 2022. Springer, Berlin, pp 753–768

    Google Scholar 

  32. Sathish K, Kumar SS, Magal RT, Selvaraj V, Narasimharaj V, Karthikeyan R, Sabarinathan G, Tiwari M, Kassa AE (2022) A comparative study on subtractive manufacturing and additive manufacturing. Adv Mater Sci Eng 2022

    Google Scholar 

  33. Stankovic M, Jovanovic L, Bacanin N, Zivkovic M, Antonijevic M, Bisevac P (2023) Tuned long short-term memory model for ethereum price forecasting through an arithmetic optimization algorithm. In: Abraham A, Bajaj A, Gandhi N, Madureira AM, Kahraman C (eds) Innovations in bio-inspired computing and applications. Springer Nature Switzerland, Cham, pp 327–337

    Google Scholar 

  34. Trung D (2022) Effect of cutting parameters on the surface roughness and roundness error when turning the interrupted surface of 40x steel using HSS-TiN insert. Appl Eng Lett J Eng Appl Sci 7(1):1–9

    Google Scholar 

  35. Umapathi K, Vanitha V, Anbarasu L, Zivkovic M, Bacanin N, Antonijevic M (2021) Predictive data regression technique based carbon nanotube biosensor for efficient patient health monitoring system. J Ambient Intell Hum Comput. https://doi.org/10.1007/s12652-021-03063-6

  36. Wang D, Tan D, Liu L (2018) Particle swarm optimization algorithm: an overview. Soft Comput 22:387–408

    Google Scholar 

  37. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Google Scholar 

  38. Yang XS (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-inspired Comput 2(2):78–84

    Google Scholar 

  39. Yang XS, He X (2013) Firefly algorithm: recent advances and applications. Int J Swarm Intell 1(1):36–50

    Google Scholar 

  40. Yang XS, Slowik A (2020) Firefly algorithm. In: Swarm intelligence algorithms. CRC Press, pp 163–174

    Google Scholar 

  41. Zhang S, Gong M, Zeng X, Gao M (2021) Residual stress and tensile anisotropy of hybrid wire arc additive-milling subtractive manufacturing. J Mater Process Technol 293:117077

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Technical Faculty “Mihajlo Pupin”, University of Novi Sad, Dure Dakovića bb, 23000, Zrenjanin, Serbia

    Aleksandra Bozovic & Eleonora Desnica

  2. Singidunum University, Danijelova 32, 11000, Belgrade, Serbia

    Luka Jovanovic, Nebojsa Bacanin, Miodrag Zivkovic & Milos Antonijevic

  3. Modern College of Business and Science, Muscat, Sultanate of Oman

    Joseph P. Mani

Authors
  1. Aleksandra Bozovic
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luka Jovanovic
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Eleonora Desnica
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nebojsa Bacanin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Miodrag Zivkovic
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Milos Antonijevic
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Joseph P. Mani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Luka Jovanovic .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Christ (Deemed to be University), Bengaluru, India

    Sandeep Kumar

  2. Department of Computer Science and Engineering, Christ (Deemed to be University), Bengaluru, India

    Balachandran K.

  3. School of Civil, Environmental and Architectural Engineering, Anam-ro, Anam-dong, Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

  4. South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bozovic, A. et al. (2024). Metaheuristic Optimized Extreme Gradient Boosting Milling Maintenance Prediction. In: Kumar, S., K., B., Kim, J.H., Bansal, J.C. (eds) Fourth Congress on Intelligent Systems. CIS 2023. Lecture Notes in Networks and Systems, vol 868. Springer, Singapore. https://doi.org/10.1007/978-981-99-9037-5_28

Download citation

Publish with us

Policies and ethics