Regresyon Analizi ve Diferansiyel Evrim Algoritması Kullanarak Düşük Karbon Çelik İçin Lazer Kesme Parametrelerinin Optimizasyonu

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

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

Abstract

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.

Keywords

• Laser Cutting
• Differential Evolution
• Roughness
• Regression Analysis
• Stochastic Optimization

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