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

Sayit Ozbey; İsmet Tıkız; Aysen Şimşek Kandemir

Makale detayları

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

Yazarlar:

Sayit Ozbey | İsmet Tıkız | Aysen Şimşek Kandemir

Yayıncı:

Duzce University

Ana Başlıklar:

Optimization Techniques in Mechanical Engineering | Numerical Methods in Mechanical Engineering

Dergi:

Düzce University Journal of Technical Sciences Vol.3 Issue 1, pp.1-13

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001351245

pISSN:

3023-591X

Anahtar kelimeler:

Özet:

 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 models 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.

 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 models 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.

 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 models 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.

 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, 83,21lik 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.

Makale detayları

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

Yazarlar:

Sayit Ozbey | İsmet Tıkız | Aysen Şimşek Kandemir

Yayıncı:

Duzce University

Ana Başlıklar:

Optimization Techniques in Mechanical Engineering | Numerical Methods in Mechanical Engineering

Dergi:

Düzce University Journal of Technical Sciences Vol.3 Issue 1, pp.1-13

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001351245

pISSN:

3023-591X

Laser Cutting

Differential Evolution

Roughness

Regression Analysis

Stochastic OptimizationLazer Kesim

Diferansiyel Gelişim Algoritması

Pürüzlülük

Regresyon Analizi

Stokastik Optimizasyon

Sayı

Makale detayları

Yazarlar: Sayit Ozbey | İsmet Tıkız | Aysen Şimşek Kandemir

Yayıncı: Duzce University

Ana Başlıklar: Optimization Techniques in Mechanical Engineering | Numerical Methods in Mechanical Engineering

Dergi: Düzce University Journal of Technical Sciences Vol.3 Issue 1, pp.1-13

Belge Tipi: Makale

Yayın Tarihi: 6/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001351245

pISSN: 3023-591X

Sayı

Yazarlar:

Sayit Ozbey | İsmet Tıkız | Aysen Şimşek Kandemir

Yayıncı:

Duzce University

Ana Başlıklar:

Optimization Techniques in Mechanical Engineering | Numerical Methods in Mechanical Engineering

Dergi:

Düzce University Journal of Technical Sciences Vol.3 Issue 1, pp.1-13

Belge Tipi:

Makale

Yayın Tarihi:

6/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001351245

pISSN:

3023-591X