Akıllı Üretim Ortamlarında Endüstri 4.0 Teknolojilerinin Uygulanmasına Yönelik Bir Çerçeve

Öz

Bu çalışma, makine öğrenimi, evrimsel hesaplama ve siber-fiziksel sistemlerin (CPS) birleşik kullanımı yoluyla akıllı üretim ortamlarını optimize etmek için entegre bir Endüstri 4.0 çerçevesi sunmaktadır. Endüstriyel bir sprey kurutma işleminden elde edilen 1.510 gerçek zamanlı operasyonel gözlem kullanılarak, çerçeve, tahmine dayalı analitiği ve sürekli operasyonel iyileştirmeyi desteklemek için Rastgele Orman Regresyonunu (RFR) bir Genetik Algoritma (GA) optimizasyon motoruyla entegre etmektedir. RFR modeli yüksek tahmin doğruluğuna (R² = 0,9144) ulaşarak, proses koşullarının güvenilir gerçek zamanlı değerlendirilmesini sağlamıştır. GA, istikrarlı bir yakınsama göstermiş ve optimize edilmiş giriş sıcaklığında %5,91'lik bir iyileşmenin yanı sıra termal verimlilikte (%3,2) ve enerji tüketiminde (%1,8) ölçülebilir iyileştirmeler sağlamıştır. Duyarlılık analizi, Zaman, Dış Sıcaklık, Nem ve Kule'nin sistem davranışını yönlendiren en etkili parametreler olduğunu ortaya koymuştur. CPS mimarisi, gerçek zamanlı algılama, tahmine dayalı modelleme ve optimizasyonu etkili bir şekilde birleştirerek, endüstriyel alanlarda güçlü ölçeklenebilirlik ve aktarılabilirlik sergiledi. Genel olarak, bulgular, makine öğrenimi ve evrimsel optimizasyonun Endüstri 4.0 ekosistemine entegre edilmesinin, otonom, verimli ve son derece uyarlanabilir üretim operasyonlarını mümkün kıldığını göstermektedir.

Anahtar Kelimeler

• Endüstri 4.0
• Siber-Fiziksel Sistemler
• Genetik Algoritmalar
• Rastgele Orman Regresyonu
• Akıllı Üretim

Destekleyen Kurum

Bu araştırma, veri erişimi ve teknik kaynak desteği sağlayan tesis işletme biriminin kurumsal desteğiyle yürütülmüştür.

Etik Beyan

Bu çalışma insan katılımcılar veya hayvan denekler içermemektedir; bu nedenle etik kurul onayı gerekmemiştir. Araştırmanın tüm aşamalarında geçerli etik ilkelere uyulmuştur.

Teşekkür

Yazarlar, çalışma süresince değerli teknik katkılar sağlayan tesis işletme, kalite kontrol ve bakım ekiplerine teşekkür eder.

A Framework for Implementing Industry 4.0 Technologies in Smart Manufacturing Environments

Öz

This study presents an Industry 4.0–oriented framework for optimizing smart manufacturing environments through the integration of machine learning, evolutionary computation, and cyber–physical systems (CPS). Using 1,510 operational observations collected from an industrial spray-drying process, the framework combines Random Forest Regression (RFR) for predictive modeling with a Genetic Algorithm (GA) for process optimization. The RFR model achieved strong predictive performance (R² = 0.9144), demonstrating its capability to capture nonlinear relationships among process variables. The GA exhibited stable convergence behaviour and achieved a 5.91% improvement in the optimized inlet temperature, alongside measurable gains in thermal efficiency (3.2%) and energy consumption (1.8%). Sensitivity analysis identified Time, Outside Temperature, Humidity, and Tower settings as the most influential variables affecting system performance. The results indicate that the integration of predictive modeling and evolutionary optimization can enhance decision support and operational efficiency within a CPS-based manufacturing environment. While the framework demonstrates promising performance within the studied process, its applicability to other industrial domains and real-time deployment requires further validation.

Anahtar Kelimeler

• Industry 4.0
• Cyber–Physical Systems
• Genetic Algorithms
• Random Forest Regression
• Smart Manufacturing

Destekleyen Kurum

This work was conducted with institutional support from the plant operations unit, which provided access to data and technical resources used in the research.

Etik Beyan

This study did not involve human participants or animal subjects; therefore, ethics committee approval was not required. The authors declare that the research adhered to all applicable ethical standards.

Teşekkür

The authors express their appreciation to the plant operations, quality-control, and maintenance teams for their valuable technical assistance throughout the study.

Kaynakça

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