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Optimization of Laser Cutting Parameters for Mild Steel using Regression Analysis and Differential Evolution Algorithm

Yıl 2025, Cilt: 3 Sayı: 1, 1 - 13, 22.06.2025

Sayit Ozbey , İsmet Tıkız , Aysen Şimşek Kandemir

https://doi.org/10.70081/duted.1641355

Öz

The primary objective in the production of parts is to optimize the manufacturing process. As the industry recognizes the roughness of the cut product as one of the key criteria, it becomes critical to select the correct laser settings with minimum trial, error and at the lowest possible cost while using reliable techniques to achieve the desired surface finish. Due to the nonlinear nature of laser cutting, statistical analysis is necessary to obtain a satisfactory surface finish. In this study, experimental data sourced from literature were subjected to analytical processes. In the experimental design, L25 orthogonal array was used. The optimization process for the laser cutting parameters (laser power, cutting speed, and assist gas pressure) was implemented using regression analysis and a differential evolution algorithm. The regression model, with an R2 value of 83.21%, accurately predicted roughness based on these parameters. The model's effectiveness was further supported by the high correlation (R2 = 86.6%) between the experimental and predicted results. Using the differential evolution optimization method, the minimum surface roughness was calculated as 0.442 µm. This study provides a method for identifying optimal laser settings to achieve the desired surface roughness based on the obtained results.

Anahtar Kelimeler

Laser Cutting Differential Evolution Roughness Regression Analysis Stochastic Optimization

Kaynakça

  • Ahmad, M. F., Isa, N. A. M., Lim, W. H., & Ang, K. M. (2022). Differential evolution: A recent review based on state-of-the-art works. Alexandria Engineering Journal, 61(5), 3831–3872.
  • Ahmed, Z. E., Saeed, R. A., Mukherjee, A., & Ghorpade, S. N. (2020). Energy optimization in low-power wide area networks by using heuristic techniques. In LPWAN Technologies for IoT and M2M Applications (pp. 199–223). Elsevier. https://doi.org/10.1016/B978-0-12-818880-4.00011-9
  • Anuja Beatrice, B., Kirubakaran, E., Ranjit Jeba Thangaiah, P., & Leo Dev Wins, K. (2014). Surface roughness prediction using artificial neural network in hard turning of AISI H13 steel with minimal cutting fluid application. Procedia Engineering, 97, 205–211.
  • Arnab, R. (2017). Chapter 20 - Complex Survey Design: Regression Analysis. In R. Arnab (Ed.), Survey Sampling Theory and Applications (pp. 673–689). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-811848-1.00020-0
  • Aslan, E., Camuşcu, N., & Birgören, B. (2007). Design optimization of cutting parameters when turning hardened AISI 4140 steel (63 HRC) with Al2O3+TiCN mixed ceramic tool. Materials & Design, 28(5), 1618–1622.
  • Begic-Hajdarevic, D., Pasic, M., Cekic, A., & Mehmedovic, M. (2016). Optimization of process parameters for cut quality in CO2 laser cutting using taguchi method. Annals of DAAAM and Proceedings of the International DAAAM Symposium, 27(1), 157–164.
  • Çaydaş, U., & Hasçalik, A. (2008). Use of the grey relational analysis to determine optimum laser cutting parameters with multi-performance characteristics. Optics and Laser Technology, 40(7), 987–994.
  • Ceylan, A. B., Aydın, L., Nil, M., Mamur, H., Polatoğlu, İ., & Sözen, H. (2023). A new hybrid approach in selection of optimum establishment location of the biogas energy production plant. Biomass Conversion and Biorefinery, 13(7), 5771–5786.
  • Dash, R., Rautray, R., & Dash, R. (2023). Utility of a Shuffled Differential Evolution algorithm in designing of a Pi-Sigma Neural Network based predictor model. Applied Computing and Informatics, 19(1/2), 22–40. https://doi.org/10.1016/j.aci.2019.04.001
  • Erkan, Ö. (2023). Evaluation of the Relationship of Surface Roughness with Machining Parameters in Milling of AA 7075 Material with Experimental and Deform 3D Simulation. Celal Bayar University Journal of Science, 19(2), 175–182.
  • Fan, M., Zhou, X., Chen, S., Jiang, S., & Song, J. (2023). Study of the surface roughness and optimization of machining parameters during laser-assisted fast tool servo machining of glass-ceramic. Surface Topography: Metrology and Properties, 11(2). https://doi.org/10.1088/2051-672X/acd5ec
  • Ferré, J. (2009). 3.02 - Regression Diagnostics. In S. D. Brown, R. Tauler, & B. Walczak (Eds.), Comprehensive Chemometrics (pp. 33–89). Elsevier. https://doi.org/https://doi.org/10.1016/B978-044452701-1.00076-4
  • Freund, R. J., Wilson, W. J., & Mohr, D. L. (2010). CHAPTER 7 - Linear Regression. In R. J. Freund, W. J. Wilson, & D. L. Mohr (Eds.), Statistical Methods (Third Edition) (Third Edit, pp. 321–374). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-374970-3.00007-X
  • Genna, S., Menna, E., Rubino, G., & Tagliaferri, V. (2020). Experimental Investigation of Industrial Laser Cutting: The Effect of the Material Selection and the Process Parameters on the Kerf Quality. Applied Sciences, 10(14), 4956.
  • Ghalandarzadeh, A., Javadpour, J., Majidian, H., & Ganjali, M. (2023). The evaluation of prepared microstructure pattern by carbon-dioxide laser on zirconia-based ceramics for dental implant application: an in vitro study. Odontology, 111(3), 580–599.
  • Hashem, R. A., Samir, R., Essam, T. M., Ali, A. E., & Amin, M. A. (2018). Optimization and enhancement of textile reactive Remazol black B decolorization and detoxification by environmentally isolated pH tolerant Pseudomonas aeruginosa KY284155. AMB Express, 8(1), 83. https://doi.org/10.1186/s13568-018-0616-1
  • Islam, S. M., Das, S., Ghosh, S., Roy, S., & Suganthan, P. N. (2012). An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical Optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(2), 482–500. https://doi.org/10.1109/TSMCB.2011.2167966
  • Karci, A. (2017). Differential Evolution Algorithm and Its Variants. Anatolian Journal of Computer Sciences, 2(1), 10–14.
  • Kasman, S. (2023). Machining of Ti–6Al–4V alloy by fiber laser: Determining the effects of parameters on surface roughness. Sigma Journal of Engineering and Natural Sciences, 41(4), 770–780.
  • Khan, A., Ndaliman, M. B., Ali, M. Y., & Lawal, S. A. (2013). Effect of Electrical Parameters on Performance of Cu- TiC Mixed Ceramic Compact Electrode in EDM Process. 2nd International Conference on Mechanical, Automotive and Aerospace Engineering (ICMAAE 2013), 1–6.
  • Kocak, E., Ozsoy, V. S., & Orkcu, H. H. (2024). Modeling of wind speed using differential evolution: Istanbul case. Sigma Journal of Engineering and Natural Sciences, 42(3), 642–652.
  • Madhava, P. (2024). Empirical Modeling and Analysis of Process Parameters in Laser Beam Cutting Process. International Journal of Mechanical Engineering and Robotics Research, 13(1), 133–138.
  • Madić, M., & Radovanović, M. (2013). Application of RCGA-ANN approach for modeling kerf width and surface roughness in CO2 laser cutting of mild steel. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 35(2), 103–110.
  • Magdum, V. B., Kittur, J. K., & Kulkarni, S. C. (2022). Surface roughness optimization in laser machining of stainless steel 304 using response surface methodology. Materials Today: Proceedings, 59, 540–546.
  • Makableh, Y., Alzubi, H., & Tashtoush, G. (2021). Design and Optimization of the Antireflective Coating Properties of Silicon Solar Cells by Using Response Surface Methodology. Coatings, 11, 721.
  • Mallipeddi, R., & Suganthan, P. N. (2010). Differential Evolution Algorithm with Ensemble of Parameters and Mutation and Crossover Strategies. In Swarm, Evolutionary, and Memetic Computing (pp. 71–78). https://doi.org/10.1007/978-3-642-17563-3_9
  • Nas, E., & Özbek, N. (2020). Optimization of the Machining Parameters in Turning of Hardened Hot Work Tool Steel Using Cryogenically Treated Tools. Surface Review and Letters, 27(5), 1950177.
  • Ozbey, S., & Tıkız, I. (2024). Advanced Laser Material Processing Techniques. In Interdisciplinary Studies on Contemporary Research Practices in Engineering in the 21st Century- VI (pp. 103–120). Ozgur Publication. https://doi.org/10.58830/ozgur. pub426.c1849
  • Ozdemir, O., Bettemir, O. H., & Firat, M. (2017). Minimum-Cost Design of Water Distribution Line with Differential Evolution Algorithm. Sigma Journal of Engineering and Natural Sciences, 8(3), 189–198.
  • Price, K., Storn, R. M., & Lampinen, J. A. (2006). Differential evolution: a practical approach to global optimization. Springer Science \& Business Media.
  • Qin, A. K., Huang, V. L., & Suganthan, P. N. (2009). Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization. IEEE Transactions on Evolutionary Computation, 13(2), 398–417.
  • Ratnam, M. M. (2017). 1.1 Factors Affecting Surface Roughness in Finish Turning. In Comprehensive Materials Finishing (pp. 1–25). Elsevier. https://doi.org/10.1016/B978-0-12-803581-8.09147-5
  • Rouf, S., Raina, A., Irfan Ul Haq, M., Naveed, N., Jeganmohan, S., & Farzana Kichloo, A. (2022). 3D printed parts and mechanical properties: Influencing parameters, sustainability aspects, global market scenario, challenges and applications. Advanced Industrial and Engineering Polymer Research, 5(3), 143–158.
  • Rubal, & Kumar, D. (2018). Evolving Differential evolution method with random forest for prediction of Air Pollution. Procedia Computer Science, 132, 824–833.
  • Saad, A., Engelbrecht, A. P., & Khan, S. A. (2024). An Analysis of Differential Evolution Population Size. Applied Sciences, 14(21), 9976.
  • Salleh, M. N. M., Ishak, M., Aiman, M. H., Zaifuddin, Q., & Quazi, M. M. (2020). The effect of laser surface hardening on the surface hardness of mild steel. IOP Conference Series: Materials Science and Engineering, 788(1). https://doi.org/10.1088/1757-899X/788/1/012014
  • Samantaray, S. R., Pradhan, S., Dhupal, D., Padhan, S., & Das, S. R. (2024). Comparative performance investigation and sustainability evaluation between hot-AJM and AJM during machining of zirconia ceramic using Al2O3 abrasives. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 46(5), 263. https://doi.org/10.1007/s40430-024-04836-8
  • Samatham, M., Venkata, A., Vardhan, V., & Reddy, N. V. (2017). Experimental Investigation on Effect of Heat Treatment on Mechanical Properties of Steels and Titanium. International Journal of Current Engineering and Technology, 7(3), 845–850.
  • SHAH, A. F. M. S., & Karabulut, M. A. (2022). Optimization of drones communication by using meta-heuristic optimization algorithms. Sigma Journal of Engineering and Natural Sciences, 40(1), 108–117.
  • Sharma, A., Raju, P. S., Gopichand, A., & Subbaiah, K. V. (2012). Optimization of Cutting Parameters on Mild Steel With Hss & Cemented Carbide Tipped Tools Using Ann. International Journal of Research in Engineering and Technology, 1(3), 226–228.
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Regresyon Analizi ve Diferansiyel Evrim Algoritması Kullanarak Düşük Karbon Çelik İçin Lazer Kesme Parametrelerinin Optimizasyonu

Yıl 2025, Cilt: 3 Sayı: 1, 1 - 13, 22.06.2025

Sayit Ozbey , İsmet Tıkız , Aysen Şimşek Kandemir

https://doi.org/10.70081/duted.1641355

Öz

Parçaların üretimindeki temel hedef, üretim sürecini optimize etmektir. Endüstri, kesilen ürünün pürüzlülüğünü anahtar kriterlerden biri olarak kabul ettiğinden, istenilen yüzey kalitesine ulaşmak için güvenilir teknikler kullanarak doğru lazer ayarlarını minimum deneme ve hata ile ve en düşük maliyetle seçmek kritik hale gelmektedir. Lazer kesmenin doğrusal olmayan doğası nedeniyle, tatmin edici bir yüzey kalitesi elde etmek için istatistiksel analiz gereklidir. Bu çalışmada, literatürden alınan deneysel veriler analitik süreçlere tabi tutulmuştur. Deneysel tasarımda L25 ortogonal dizisi kullanılmıştır. Lazer kesme parametrelerinin (lazer gücü, kesme hızı ve yardımcı gaz basıncı) optimizasyon süreci, regresyon analizi ve diferansiyel evrim algoritması kullanılarak gerçekleştirilmiştir. Regresyon modeli, %83,21'lik bir R2 değeri ile bu parametreler temelinde pürüzlülüğü doğru bir şekilde tahmin etmiştir. Modelin etkinliği, deneysel ve tahmin edilen sonuçlar arasındaki yüksek korelasyon (R2 = 86.6%) ile daha da desteklenmiştir. Diferansiyel evrim optimizasyon yöntemi kullanılarak, minimum yüzey pürüzlülüğü 0,442 µm olarak hesaplanmıştır. Bu çalışma, elde edilen sonuçlara dayalı olarak istenilen yüzey pürüzlülüğünü elde etmek için optimal lazer ayarlarını belirleme yöntemi sunmaktadır.

Anahtar Kelimeler

Lazer Kesim Diferansiyel Gelişim Algoritması Pürüzlülük Regresyon Analizi Stokastik Optimizasyon

Kaynakça

  • Ahmad, M. F., Isa, N. A. M., Lim, W. H., & Ang, K. M. (2022). Differential evolution: A recent review based on state-of-the-art works. Alexandria Engineering Journal, 61(5), 3831–3872.
  • Ahmed, Z. E., Saeed, R. A., Mukherjee, A., & Ghorpade, S. N. (2020). Energy optimization in low-power wide area networks by using heuristic techniques. In LPWAN Technologies for IoT and M2M Applications (pp. 199–223). Elsevier. https://doi.org/10.1016/B978-0-12-818880-4.00011-9
  • Anuja Beatrice, B., Kirubakaran, E., Ranjit Jeba Thangaiah, P., & Leo Dev Wins, K. (2014). Surface roughness prediction using artificial neural network in hard turning of AISI H13 steel with minimal cutting fluid application. Procedia Engineering, 97, 205–211.
  • Arnab, R. (2017). Chapter 20 - Complex Survey Design: Regression Analysis. In R. Arnab (Ed.), Survey Sampling Theory and Applications (pp. 673–689). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-811848-1.00020-0
  • Aslan, E., Camuşcu, N., & Birgören, B. (2007). Design optimization of cutting parameters when turning hardened AISI 4140 steel (63 HRC) with Al2O3+TiCN mixed ceramic tool. Materials & Design, 28(5), 1618–1622.
  • Begic-Hajdarevic, D., Pasic, M., Cekic, A., & Mehmedovic, M. (2016). Optimization of process parameters for cut quality in CO2 laser cutting using taguchi method. Annals of DAAAM and Proceedings of the International DAAAM Symposium, 27(1), 157–164.
  • Çaydaş, U., & Hasçalik, A. (2008). Use of the grey relational analysis to determine optimum laser cutting parameters with multi-performance characteristics. Optics and Laser Technology, 40(7), 987–994.
  • Ceylan, A. B., Aydın, L., Nil, M., Mamur, H., Polatoğlu, İ., & Sözen, H. (2023). A new hybrid approach in selection of optimum establishment location of the biogas energy production plant. Biomass Conversion and Biorefinery, 13(7), 5771–5786.
  • Dash, R., Rautray, R., & Dash, R. (2023). Utility of a Shuffled Differential Evolution algorithm in designing of a Pi-Sigma Neural Network based predictor model. Applied Computing and Informatics, 19(1/2), 22–40. https://doi.org/10.1016/j.aci.2019.04.001
  • Erkan, Ö. (2023). Evaluation of the Relationship of Surface Roughness with Machining Parameters in Milling of AA 7075 Material with Experimental and Deform 3D Simulation. Celal Bayar University Journal of Science, 19(2), 175–182.
  • Fan, M., Zhou, X., Chen, S., Jiang, S., & Song, J. (2023). Study of the surface roughness and optimization of machining parameters during laser-assisted fast tool servo machining of glass-ceramic. Surface Topography: Metrology and Properties, 11(2). https://doi.org/10.1088/2051-672X/acd5ec
  • Ferré, J. (2009). 3.02 - Regression Diagnostics. In S. D. Brown, R. Tauler, & B. Walczak (Eds.), Comprehensive Chemometrics (pp. 33–89). Elsevier. https://doi.org/https://doi.org/10.1016/B978-044452701-1.00076-4
  • Freund, R. J., Wilson, W. J., & Mohr, D. L. (2010). CHAPTER 7 - Linear Regression. In R. J. Freund, W. J. Wilson, & D. L. Mohr (Eds.), Statistical Methods (Third Edition) (Third Edit, pp. 321–374). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-374970-3.00007-X
  • Genna, S., Menna, E., Rubino, G., & Tagliaferri, V. (2020). Experimental Investigation of Industrial Laser Cutting: The Effect of the Material Selection and the Process Parameters on the Kerf Quality. Applied Sciences, 10(14), 4956.
  • Ghalandarzadeh, A., Javadpour, J., Majidian, H., & Ganjali, M. (2023). The evaluation of prepared microstructure pattern by carbon-dioxide laser on zirconia-based ceramics for dental implant application: an in vitro study. Odontology, 111(3), 580–599.
  • Hashem, R. A., Samir, R., Essam, T. M., Ali, A. E., & Amin, M. A. (2018). Optimization and enhancement of textile reactive Remazol black B decolorization and detoxification by environmentally isolated pH tolerant Pseudomonas aeruginosa KY284155. AMB Express, 8(1), 83. https://doi.org/10.1186/s13568-018-0616-1
  • Islam, S. M., Das, S., Ghosh, S., Roy, S., & Suganthan, P. N. (2012). An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical Optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(2), 482–500. https://doi.org/10.1109/TSMCB.2011.2167966
  • Karci, A. (2017). Differential Evolution Algorithm and Its Variants. Anatolian Journal of Computer Sciences, 2(1), 10–14.
  • Kasman, S. (2023). Machining of Ti–6Al–4V alloy by fiber laser: Determining the effects of parameters on surface roughness. Sigma Journal of Engineering and Natural Sciences, 41(4), 770–780.
  • Khan, A., Ndaliman, M. B., Ali, M. Y., & Lawal, S. A. (2013). Effect of Electrical Parameters on Performance of Cu- TiC Mixed Ceramic Compact Electrode in EDM Process. 2nd International Conference on Mechanical, Automotive and Aerospace Engineering (ICMAAE 2013), 1–6.
  • Kocak, E., Ozsoy, V. S., & Orkcu, H. H. (2024). Modeling of wind speed using differential evolution: Istanbul case. Sigma Journal of Engineering and Natural Sciences, 42(3), 642–652.
  • Madhava, P. (2024). Empirical Modeling and Analysis of Process Parameters in Laser Beam Cutting Process. International Journal of Mechanical Engineering and Robotics Research, 13(1), 133–138.
  • Madić, M., & Radovanović, M. (2013). Application of RCGA-ANN approach for modeling kerf width and surface roughness in CO2 laser cutting of mild steel. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 35(2), 103–110.
  • Magdum, V. B., Kittur, J. K., & Kulkarni, S. C. (2022). Surface roughness optimization in laser machining of stainless steel 304 using response surface methodology. Materials Today: Proceedings, 59, 540–546.
  • Makableh, Y., Alzubi, H., & Tashtoush, G. (2021). Design and Optimization of the Antireflective Coating Properties of Silicon Solar Cells by Using Response Surface Methodology. Coatings, 11, 721.
  • Mallipeddi, R., & Suganthan, P. N. (2010). Differential Evolution Algorithm with Ensemble of Parameters and Mutation and Crossover Strategies. In Swarm, Evolutionary, and Memetic Computing (pp. 71–78). https://doi.org/10.1007/978-3-642-17563-3_9
  • Nas, E., & Özbek, N. (2020). Optimization of the Machining Parameters in Turning of Hardened Hot Work Tool Steel Using Cryogenically Treated Tools. Surface Review and Letters, 27(5), 1950177.
  • Ozbey, S., & Tıkız, I. (2024). Advanced Laser Material Processing Techniques. In Interdisciplinary Studies on Contemporary Research Practices in Engineering in the 21st Century- VI (pp. 103–120). Ozgur Publication. https://doi.org/10.58830/ozgur. pub426.c1849
  • Ozdemir, O., Bettemir, O. H., & Firat, M. (2017). Minimum-Cost Design of Water Distribution Line with Differential Evolution Algorithm. Sigma Journal of Engineering and Natural Sciences, 8(3), 189–198.
  • Price, K., Storn, R. M., & Lampinen, J. A. (2006). Differential evolution: a practical approach to global optimization. Springer Science \& Business Media.
  • Qin, A. K., Huang, V. L., & Suganthan, P. N. (2009). Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization. IEEE Transactions on Evolutionary Computation, 13(2), 398–417.
  • Ratnam, M. M. (2017). 1.1 Factors Affecting Surface Roughness in Finish Turning. In Comprehensive Materials Finishing (pp. 1–25). Elsevier. https://doi.org/10.1016/B978-0-12-803581-8.09147-5
  • Rouf, S., Raina, A., Irfan Ul Haq, M., Naveed, N., Jeganmohan, S., & Farzana Kichloo, A. (2022). 3D printed parts and mechanical properties: Influencing parameters, sustainability aspects, global market scenario, challenges and applications. Advanced Industrial and Engineering Polymer Research, 5(3), 143–158.
  • Rubal, & Kumar, D. (2018). Evolving Differential evolution method with random forest for prediction of Air Pollution. Procedia Computer Science, 132, 824–833.
  • Saad, A., Engelbrecht, A. P., & Khan, S. A. (2024). An Analysis of Differential Evolution Population Size. Applied Sciences, 14(21), 9976.
  • Salleh, M. N. M., Ishak, M., Aiman, M. H., Zaifuddin, Q., & Quazi, M. M. (2020). The effect of laser surface hardening on the surface hardness of mild steel. IOP Conference Series: Materials Science and Engineering, 788(1). https://doi.org/10.1088/1757-899X/788/1/012014
  • Samantaray, S. R., Pradhan, S., Dhupal, D., Padhan, S., & Das, S. R. (2024). Comparative performance investigation and sustainability evaluation between hot-AJM and AJM during machining of zirconia ceramic using Al2O3 abrasives. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 46(5), 263. https://doi.org/10.1007/s40430-024-04836-8
  • Samatham, M., Venkata, A., Vardhan, V., & Reddy, N. V. (2017). Experimental Investigation on Effect of Heat Treatment on Mechanical Properties of Steels and Titanium. International Journal of Current Engineering and Technology, 7(3), 845–850.
  • SHAH, A. F. M. S., & Karabulut, M. A. (2022). Optimization of drones communication by using meta-heuristic optimization algorithms. Sigma Journal of Engineering and Natural Sciences, 40(1), 108–117.
  • Sharma, A., Raju, P. S., Gopichand, A., & Subbaiah, K. V. (2012). Optimization of Cutting Parameters on Mild Steel With Hss & Cemented Carbide Tipped Tools Using Ann. International Journal of Research in Engineering and Technology, 1(3), 226–228.
  • Shugaev, M. V., He, M., Levy, Y., Mazzi, A., Miotello, A., Bulgakova, N. M., & Zhigilei, L. V. (2020). Laser-Induced Thermal Processes: Heat Transfer, Generation of Stresses, Melting and Solidification, Vaporization, and Phase Explosion. In Handbook of Laser Micro- and Nano-Engineering (pp. 1–81). Springer International Publishing. https://doi.org/10.1007/978-3-319-69537-2_11-1
  • Speidel, A., Lutey, A. H. A., Mitchell-Smith, J., Rance, G. A., Liverani, E., Ascari, A., Fortunato, A., & Clare, A. (2016). Surface modification of mild steel using a combination of laser and electrochemical processes. Surface and Coatings Technology, 307, 849–860.
  • Storn, R., & Price, K. (1997). Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces. Journal of Global Optimization, 11(4), 341–359.
  • Suhail, A. H., Ismail, N., Wong, S. V, & Abdul Jalil, N. A. (2010). Optimization of Cutting Parameters Based on Surface Roughness and Assistance of Workpiece Surface Temperature in Turning Process. American Journal of Engineering and Applied Sciences, 3(1), 102–108.
  • Tamanna, N., Crouch, R., & Naher, S. (2019). Progress in numerical simulation of the laser cladding process. Optics and Lasers in Engineering, 122(May), 151–163.
  • Tamrin, K. F., Moghadasi, K., Jalil, M. H., Sheikh, N. A., & Mohamaddan, S. (2021). Laser Discoloration in Acrylic Painting of Visual Art: Experiment and Modeling. Materials, 14(8), 2009. https://doi.org/10.3390/ma14082009
  • Thomasian, A. (2022). Chapter 9 - Structured, unstructured, and diverse databases. In A. Thomasian (Ed.), Storage Systems (pp. 493–563). Morgan Kaufmann. https://doi.org/https://doi.org/10.1016/B978-0-32-390796-5.00018-8
  • Tseng, T.-L. (Bill), Konada, U., & Kwon, Y. (James). (2016). A novel approach to predict surface roughness in machining operations using fuzzy set theory. Journal of Computational Design and Engineering, 3(1), 1–13.
  • Um, S.-H., Hwang, S.-W., Grigoropoulos, C. P., Jeon, H., & Ko, S. H. (2022). Recent advances in selective laser–material interaction for biomedical device applications. Applied Physics Reviews, 9(4). https://doi.org/10.1063/5.0101634
  • Ürgün, S., Yiğit, H., Fidan, S., & Sınmazçelik, T. (2024). Optimization of Laser Cutting Parameters for PMMA Using Metaheuristic Algorithms. Arabian Journal for Science and Engineering, 49(9), 12333–12355.
  • Villavicencio, R., & Guedes Soares, C. (2011). Numerical modelling of the boundary conditions on beams stuck transversely by a mass. International Journal of Impact Engineering, 38(5), 384–396.
  • Wang, G., Deng, J., Lei, J., Tang, W., Zhou, W., & Lei, Z. (2024). Multi-Objective Optimization of Laser Cleaning Quality of Q390 Steel Rust Layer Based on Response Surface Methodology and NSGA-II Algorithm. Materials, 17(13). https://doi.org/10.3390/ma17133109
  • Wang, G., Qin, Y., & Yang, S. (2021). Characterization of laser-powder interaction and particle transport phenomena during laser direct deposition of W‒Cu composite. Additive Manufacturing, 37, 101722.
  • Wang, Y., He, Z., Xie, S., Wang, R., Zhang, Z., Liu, S., Shang, S., Zheng, P., & Wang, C. (2024). Explainable prediction of surface roughness in multi-jet polishing based on ensemble regression and differential evolution method. Expert Systems with Applications, 249, 123578.
  • Xiao, G., Ni, Y., Liu, Z., He, Y., & Li, X. (2024). Research on material removal of Ti-6Al-4V by laser-belt machining. The International Journal of Advanced Manufacturing Technology, 130(11–12), 5533–5546.
  • Zdravković, V., Palija, T., & Karadolamovic, Z. (2020). Influence of thermal modification of ash wood (fraxinus excelsior l.) and machining parameters in cnc face milling on surface roughness using response surface methodology (RSM). Sigma Journal of Engineering and Natural Sciences, 2(11), 231–241.
  • Zeilmann, R. P., & Conrado, R. D. (2022). Effects of cutting power, speed and assist gas pressure parameters on the surface integrity cut by laser. Procedia CIRP, 108(C), 367–371.
Toplam 57 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Makine Mühendisliğinde Optimizasyon Teknikleri, Makine Mühendisliğinde Sayısal Yöntemler
Bölüm Araştırma Makalesi
Yazarlar

Sayit Ozbey 0000-0002-9782-6997

İsmet Tıkız 0000-0003-4477-799X

Aysen Şimşek Kandemir 0000-0001-5020-1183

Yayımlanma Tarihi 22 Haziran 2025
Gönderilme Tarihi 17 Şubat 2025
Kabul Tarihi 17 Mart 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 3 Sayı: 1

Kaynak Göster

APA Ozbey, S., Tıkız, İ., & Şimşek Kandemir, A. (2025). Optimization of Laser Cutting Parameters for Mild Steel using Regression Analysis and Differential Evolution Algorithm. Düzce Üniversitesi Teknik Bilimler Dergisi, 3(1), 1-13. https://doi.org/10.70081/duted.1641355
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