MAKİNE ÖĞRENMESİ İLE ÜRETİM PERFORMANSI TAHMİNLEMESİ

Semih Göksu; Bülent Sezen; Yavuz Selim Balcıoğlu

doi:10.17780/ksujes.1517172

EN TR

PREDICT OF PRODUCTION PERFORMANCE WITH MACHINE LEARNING

Abstract

The aim of this study is to predict production performance using machine learning algorithms. Production systems operate based on various machines, parameters, and set values, with each production condition potentially exhibiting variability. Advances in technology have made it possible to control these variabilities, optimize production conditions, and derive insights from interconnected processes. In this context, machine learning serves as a valuable tool that enables the prediction of production performance through statistical methods applied to datasets. In this study, two years of data from the same product group were used to predict production performance through the application of Decision Tree, Linear Regression, Lasso Regression, XGBoost, Support Vector Regression, and LSTM algorithms. These algorithms were evaluated across three different scenarios, with the goal of identifying the model that can most accurately predict production performance. The results provide practical insights into improving production processes by comparing the performance of simple and complex models.

Keywords

MAKİNE ÖĞRENMESİ İLE ÜRETİM PERFORMANSI TAHMİNLEMESİ

Öz

Bu çalışmanın amacı, makine öğrenmesi algoritmaları kullanarak üretim performansının tahmin edilmesidir. Üretim sistemleri, çeşitli makineler, parametreler ve set değerler üzerinden çalışmakta olup, her bir üretim koşulu değişkenlik gösterebilmektedir. Gelişen teknolojiler sayesinde, bu değişkenliklerin kontrol altına alınması, üretim koşullarının optimize edilmesi ve birbirini etkileyen süreçlerden çıkarım yapılması mümkün hale gelmiştir. Bu bağlamda, makine öğrenmesi, istatistiksel metotlarla veri setleri üzerinden üretim performansının tahmin edilmesine olanak tanıyan önemli bir araçtır. Çalışmada, aynı ürün grubuna ait 2 yıllık veri kullanılarak üretim performansını tahmin etmek amacıyla Karar Ağacı, Lineer Regresyon, Lasso Regresyon, XGBoost, Destek Vektör Regresyonu ve LSTM algoritmaları uygulanmıştır. Bu algoritmalar, üç farklı senaryo üzerinden değerlendirilmiş ve üretim performansını en doğru şekilde tahmin edebilecek modelin belirlenmesi hedeflenmiştir. Elde edilen sonuçlar, basit ve karmaşık modellerin performanslarını karşılaştırarak üretim süreçlerinin iyileştirilmesine yönelik pratik öneriler sunmaktadır.

Anahtar Kelimeler

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Ayrıntılar

Birincil Dil

Türkçe

Konular

Yapay Zeka (Diğer)

Bölüm

Araştırma Makalesi

Yazarlar

Semih Göksu ^*
0009-0007-8158-6654
Türkiye

Bülent Sezen
0000-0001-7485-3194
Türkiye

Yavuz Selim Balcıoğlu
0000-0001-7138-2972
Türkiye

Yayımlanma Tarihi

3 Mart 2025

Gönderilme Tarihi

16 Temmuz 2024

Kabul Tarihi

30 Ekim 2024

Yayımlandığı Sayı

Yıl 2025 Cilt: 28 Sayı: 1

DOI

https://doi.org/10.17780/ksujes.1517172

IZ

https://izlik.org/JA84EH87EZ

Kaynak Göster

RIS / Bibtex

APA

Göksu, S., Sezen, B., & Balcıoğlu, Y. S. (2025). MAKİNE ÖĞRENMESİ İLE ÜRETİM PERFORMANSI TAHMİNLEMESİ. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 65-79. https://doi.org/10.17780/ksujes.1517172

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