A LITERATURE REVIEW OF LASER ENGINEERED NET SHAPING IN ADDITIVE MANUFACTURING USING ARTIFICIAL NEURAL NETWORKS

Filiz Karaömerlioğlu; Mustafa Ucar

doi:10.17780/ksujes.1594930

EN TR

A LITERATURE REVIEW OF LASER ENGINEERED NET SHAPING IN ADDITIVE MANUFACTURING USING ARTIFICIAL NEURAL NETWORKS

Abstract

This review explores the integration of machine learning (ML) and artificial neural networks (ANNs) in optimizing alloy production modeling and print control within Laser Engineered Net Shaping (LENS), a key additive manufacturing process. It investigates theoretical foundations, methodologies, case studies, and emerging trends to enhance process efficiency, improve product quality, and accelerate production cycles. A comprehensive literature review was conducted across academic databases and industry reports using keywords such as “machine learning,” “artificial neural networks,” and “Laser Engineered Net Shaping.” Both theoretical and experimental perspectives were analyzed to provide a well-rounded discussion. Findings indicate that ML and ANN models enhance understanding of alloy production, optimizing configurations and reducing defects. Real-time ML-driven optimization enables adaptive adjustments to process parameters, ensuring improved quality and accuracy. ANNs effectively predict key alloy microstructure properties, supporting informed decision-making and process refinement. Integrating ML and ANNs into LENS facilitates adaptive manufacturing, dynamically responding to changing conditions and alloy compositions.

Keywords

YAPAY SİNİR AĞLARI KULLANILARAK KATMANLI ÜRETİMDE LAZERLE TASARLANMIŞ AĞ ŞEKİLLENDİRME ÜZERİNE BİR LİTERATÜR İNCELEMESİ

Öz

Bu derleme, makine öğrenimi (ML) ve yapay sinir ağlarının (YSA), önemli bir eklemeli üretim süreci olan Laser Engineered Net Shaping (LENS) içinde alaşım üretim modellemesi ve baskı kontrolünü optimize etmek amacıyla entegrasyonunu incelemektedir. Süreç verimliliğini artırmak, ürün kalitesini iyileştirmek ve üretim döngülerini hızlandırmak için teorik temeller, metodolojiler, vaka çalışmaları ve yeni ortaya çıkan trendler araştırılmıştır. Akademik veri tabanları ve endüstri raporları üzerinde kapsamlı bir literatür taraması gerçekleştirilmiş, “makine öğrenimi”, “yapay sinir ağları” ve “Laser Engineered Net Shaping” gibi anahtar kelimeler kullanılmıştır. Konuya dengeli bir bakış açısı sunmak amacıyla hem teorik hem de deneysel çalışmalar analiz edilmiştir. Bulgular, ML ve YSA modellerinin alaşım üretim süreçlerini daha iyi anlamayı sağladığını, konfigürasyonları optimize ettiğini ve kusurları azalttığını göstermektedir. Gerçek zamanlı ML tabanlı optimizasyon, işlem parametrelerinin adaptif olarak ayarlanmasını sağlayarak kaliteyi ve doğruluğu artırır. YSA'lar, alaşım mikro yapısına ilişkin temel özellikleri başarılı bir şekilde tahmin ederek bilinçli karar alma ve süreç iyileştirmeye katkıda bulunur. ML ve YSA'ların LENS'e entegrasyonu, değişen koşullara ve alaşım bileşimlerine dinamik olarak uyum sağlayan adaptif üretimi mümkün kılar.

Anahtar Kelimeler

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Nöral Ağlar , Katmanlı Üretim

Bölüm

Derleme

Yazarlar

Filiz Karaömerlioğlu ^*
0000-0002-4677-4365
Türkiye

Mustafa Ucar
0000-0002-1851-2317
Türkiye

Yayımlanma Tarihi

3 Mart 2025

Gönderilme Tarihi

3 Aralık 2024

Kabul Tarihi

27 Aralık 2024

Yayımlandığı Sayı

Yıl 2025 Cilt: 28 Sayı: 1

DOI

https://doi.org/10.17780/ksujes.1594930

IZ

https://izlik.org/JA29UY97CX

Kaynak Göster

RIS / Bibtex

APA

Karaömerlioğlu, F., & Ucar, M. (2025). A LITERATURE REVIEW OF LASER ENGINEERED NET SHAPING IN ADDITIVE MANUFACTURING USING ARTIFICIAL NEURAL NETWORKS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 551-582. https://doi.org/10.17780/ksujes.1594930

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