An innovative application of CNN-based real-time temperature monitoring in electronics using thermochromic PLA

Öz

The growing demand for more powerful electronic equipment forces the operator to use active control systems such as temperature monitoring. Real-time temperature monitoring is critical for ensuring the longevity and operational reliability of electronic components. This study presents a contactless, cost-effective, and user-independent monitoring system. In an innovative approach, thermochromic Polylactic Acid (PLA) is utilized as a passive temperature sensor. The proposed methodology employs a Convolutional Neural Network (CNN) to determine the temperature gradients of a Gigabyte GA-EQ45M-S2 motherboard and an Intel Core 2 Duo E8500 processor. Experimental results demonstrate that the system achieves 96% accuracy in image-based temperature detection. Measurements were compared with data obtained from a thermal camera and an infrared (IR) thermometer to validate the proposed method, under the same conditions. The findings suggest that this CNN-based system provides a viable, low-cost alternative to expensive thermal imaging equipment and traditional sensors for electronic component real-time temperature monitoring.

Anahtar Kelimeler

• Real-time temperature monitoring
• Electronic component
• Deep learning
• CNN

Elektronik bileşenlerde gerçek zamanlı sıcaklık takibi için termokromik PLA ve CNN tabanlı yenilikçi bir uygulama

Öz

Daha güçlü elektronik ekipmanlara yönelik artan talep, operatörü sıcaklık izleme gibi aktif kontrol sistemlerini kullanmaya zorlamaktadır. Gerçek zamanlı sıcaklık izleme, elektronik bileşenlerin uzun ömürlülüğünü ve çalışma güvenilirliğini sağlamak için kritik öneme sahiptir. Bu çalışma, temassız, uygun maliyetli ve kullanıcıdan bağımsız bir izleme sistemi sunmaktadır. Yenilikçi bir yaklaşımla, termokromik Polilaktik Asit (PLA) pasif bir sıcaklık sensörü olarak kullanılmaktadır. Önerilen metodoloji, bir Gigabyte GA-EQ45M-S2 anakartının ve bir Intel Core 2 Duo E8500 işlemcisinin sıcaklık gradyanlarını belirlemek için Evrişimsel Sinir Ağı (CNN) kullanmaktadır. Deneysel sonuçlar, sistemin görüntü tabanlı sıcaklık tespitinde %96 doğruluk elde ettiğini göstermektedir. Önerilen yöntemi doğrulamak için, aynı koşullar altında, ölçümler termal kamera ve kızılötesi (IR) termometreden elde edilen verilerle karşılaştırılmıştır. Bulgular, CNN tabanlı bu sistemin, elektronik bileşenlerin gerçek zamanlı sıcaklık izlemesi için pahalı termal görüntüleme ekipmanına ve geleneksel sensörlere uygulanabilir, düşük maliyetli bir alternatif sağladığını göstermektedir.

Anahtar Kelimeler

• Gerçek zamanlı sıcaklık takibi
• Elektronik bileşen
• Derin öğrenme
• CNN
• PLA

Kaynakça

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