Implemantation of Non-Sensor Based Fuzzy Logic Control for G-Code Parameter Optimization: Advanced Efficiency in Titanium Alloy CNC Processing

I Made Aditya; Bryant Josua Runturambi; Jedithjah Naapia Tamedi Papia; Firmansyah Reskal Motulo; Jerry Heisye Purnama; Meike Negawati Kesek

doi:10.59535/jece.v2i2.363

Authors

I Made Aditya Department of Mechanical Engineering, Politeknik Negeri Manado, Indonesia
Bryant Josua Runturambi Department of Mechanical Engineering, Politeknik Negeri Manado, Indonesia
Jedithjah Naapia Tamedi Papia Departement of Mechanical Engineering, Politeknik Negeri Manado, Indonesia https://orcid.org/0009-0004-6108-7581
Firmansyah Reskal Motulo Department of Mechanical Engineering, Politeknik Negeri Manado, Indonesia
Jerry Heisye Purnama Department of Mechanical Engineering, Politeknik Negeri Manado, Indonesia
Meike Negawati Kesek Department of Mechanical Engineering, Politeknik Negeri Manado, Indonesia https://orcid.org/0009-0002-1053-5722

DOI:

https://doi.org/10.59535/jece.v2i2.363

Keywords:

Fuzzy Logic Control, G-code optimization, machining parameters, parameter optimization, titanium alloy

Abstract

This research introduces an innovative algorithm for G-code modification using Fuzzy Logic Control (FLC) to optimize Computer Numerical Control (CNC) machining parameters without relying on additional hardware or sensors. The study develops a computational framework that processes G-code blocks with an average speed of 0.3ms while maintaining a minimal memory footprint of 1.2MB. Implementation results demonstrate an 18% reduction in total machining time, with the feed rate optimized from 1000 mm/min to 1180 mm/min for linear cutting and spindle speed enhanced from 3000 RPM to 3450 RPM, while maintaining conservative parameters for critical plunge cutting operations. The system achieved a 23% increase in tool life through intelligent parameter modulation. Testing on titanium alloy workpieces showed consistent performance with zero machining interruptions during parameter modification, marking a five-fold improvement in processing speed compared to existing sensor-based systems. This hardware-independent approach enables rapid deployment in existing CNC systems through simple software updates, offering a cost-effective solution for machining optimization. The research establishes a foundation for intelligent G-code generation that adapts to material properties and cutting conditions while maintaining operational safety and efficiency.

Downloads

Download data is not yet available.

References

J. C. Chen and M. Savage, ‘A Fuzzy-Net-Based Multilevel In-Process Surface Roughness Recognition System in Milling Operations’, Int J Adv Manuf Technol, vol. 17, no. 9, pp. 670–676, May 2001, doi: 10.1007/s001700170132.

M. Davidson, ‘Classical Charged Particle Models Derived from Complex Shift Methods’, Int J Theor Phys, vol. 62, no. 7, p. 154, Jul. 2023, doi: 10.1007/s10773-023-05411-y.

P. Suthahar, K. Palanikumar, A. Ponshanmugakumar, and G. Anbuchezhiyan, ‘Machine Learning Advancements in Machining Processes: A Comprehensive Review for Manufacturing Optimization’, J. Phys.: Conf. Ser., vol. 2837, no. 1, p. 012102, Oct. 2024, doi: 10.1088/1742-6596/2837/1/012102.

X. Yang and C. Zhu, ‘Industrial Expert Systems Review: A Comprehensive Analysis of Typical Applications’, IEEE Access, vol. 12, pp. 88558–88584, 2024, doi: 10.1109/ACCESS.2024.3419047.

X. Hao, Y. Li, Z. Zhao, and C. Liu, ‘Dynamic machining process planning incorporating in-process workpiece deformation data for large-size aircraft structural parts’, International Journal of Computer Integrated Manufacturing, vol. 32, no. 2, pp. 136–147, Feb. 2019, doi: 10.1080/0951192X.2018.1529431.

T. Aziz, A. Ullah, A. Ali, M. Shabeer, and ..., ‘Manufactures of bio‐degradable and bio‐based polymers for bio‐materials in the pharmaceutical field’, Journal of Applied …, no. Query date: 2024-10-31 14:43:50, 2022, doi: 10.1002/app.52624.

E. D. Kirby and J. C. Chen, ‘Development of a fuzzy-nets-based surface roughness prediction system in turning operations’, Computers & Industrial Engineering, vol. 53, no. 1, pp. 30–42, Aug. 2007, doi: 10.1016/j.cie.2006.06.018.

T. Kıvak, ‘Optimization of surface roughness and flank wear using the Taguchi method in milling of Hadfield steel with PVD and CVD coated inserts’, Measurement, vol. 50, pp. 19–28, Apr. 2014, doi: 10.1016/j.measurement.2013.12.017.

R. Kumar, S. Sharma, R. Kumar, S. Verma, and M. Rafighi, ‘Review of Lubrication and Cooling in Computer Numerical Control (CNC) Machine Tools: A Content and Visualization Analysis, Research Hotspots and Gaps’, Sustainability, vol. 15, no. 6, Art. no. 6, Jan. 2023, doi: 10.3390/su15064970.

M. Ntemi, S. Paraschos, A. Karakostas, I. Gialampoukidis, S. Vrochidis, and I. Kompatsiaris, ‘Infrastructure monitoring and quality diagnosis in CNC machining: A review’, CIRP Journal of Manufacturing Science and Technology, vol. 38, pp. 631–649, Aug. 2022, doi: 10.1016/j.cirpj.2022.06.001.

Y. Liu, M. Chen, and Y. Sun, ‘Global toolpath modulation–based contour error pre-compensation for multi-axis CNC machining’, Int J Adv Manuf Technol, vol. 125, no. 7, pp. 3171–3189, Apr. 2023, doi: 10.1007/s00170-023-10857-9.

S. P. Leo Kumar, ‘State of The Art-Intense Review on Artificial Intelligence Systems Application in Process Planning and Manufacturing’, Engineering Applications of Artificial Intelligence, vol. 65, pp. 294–329, Oct. 2017, doi: 10.1016/j.engappai.2017.08.005.

M. Yucesan and M. Gul, ‘Hospital service quality evaluation: an integrated model based on Pythagorean fuzzy AHP and fuzzy TOPSIS’, Soft Comput, vol. 24, no. 5, pp. 3237–3255, Mar. 2020, doi: 10.1007/s00500-019-04084-2.

P. Chotikunnan, R. Chotikunnan, A. Nirapai, A. Wongkamhang, P. Imura, and M. Sangworasil, ‘Optimizing Membership Function Tuning for Fuzzy Control of Robotic Manipulators Using PID-Driven Data Techniques’, Journal of Robotics and Control (JRC), vol. 4, no. 2, Art. no. 2, Mar. 2023, doi: 10.18196/jrc.v4i2.18108.

S. Prvulovic, P. Mosorinski, D. Radosav, J. Tolmac, M. Josimovic, and V. Sinik, ‘Determination of the temperature in the cutting zone while processing machine plastic using fuzzy-logic controller (FLC)’, Ain Shams Engineering Journal, vol. 13, no. 3, p. 101624, May 2022, doi: 10.1016/j.asej.2021.10.019.

B. Liu, H. Zhang, Y. Liu, and M. Lu, ‘A Feedrate Planning Method in CNC System Based on Servo Response Error Model’, Electronics, vol. 12, no. 14, Art. no. 14, Jan. 2023, doi: 10.3390/electronics12143150.

Z. Wang, Z. Wang, S. He, X. Gu, and Z. F. Yan, ‘Fault detection and diagnosis of chillers using Bayesian network merged distance rejection and multi-source non-sensor information’, Applied Energy, vol. 188, pp. 200–214, Feb. 2017, doi: 10.1016/j.apenergy.2016.11.130.

A. Ikram and F. Pušavec, ‘1 Control, monitoring, modeling, and optimization of manufacturing and machining technologies’, in Soft Computing in Smart Manufacturing: Solutions toward Industry 5.0, T. Sibalija and J. P. Davim, Eds., De Gruyter, 2021, pp. 1–54. doi: 10.1515/9783110693225-001.

T. Truong, J.-S. Kim, and J. Kim, ‘Development of Embroidery-Type Pressure Sensor Dependent on Interdigitated Capacitive Method’, Polymers, vol. 14, no. 17, Art. no. 17, Jan. 2022, doi: 10.3390/polym14173446.

E. A. Al-Suhaimi, ‘Nanogenerator-Based Sensors for Energy Harvesting From Cardiac Contraction’, Frontiers in Energy Research, vol. 10, no. Query date: 2024-10-31 15:10:01, 2022, doi: 10.3389/fenrg.2022.900534.

T. Kroger and F. M. Wahl, ‘Multi-sensor integration and sensor fusion in industrial manipulation: Hybrid switched control, trajectory generation, and software development’, in 2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, Aug. 2008, pp. 411–418. doi: 10.1109/MFI.2008.4648030.