A web-based decision support system for quality prediction in manufacturing using ensemble of regressor chains

Name: A web-based decision support system for quality prediction in manufacturing using ensemble of regressor chains
Author: Demirel, Kenan Cem, Şahin, Ahmet, Albey, Erinç

İsim	A web-based decision support system for quality prediction in manufacturing using ensemble of regressor chains
Yazar	Demirel, Kenan Cem, Şahin, Ahmet, Albey, Erinç
Basım Tarihi:	2020
Basım Yeri	- Springer
Konu	Industry 4.0, Quality prediction, Ensemble methods, Regressor chains, Decision support system
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-303054594-9
Kayıt Numarası	6933255a-55ba-4c75-b4e2-1104e540645e
Lokasyon	Industrial Engineering
Tarih	2020
Örnek Metin	In this study we construct a decision support system (DSS), which utilizes the production process parameters to predict the quality characteristics of final products in two different manufacturing plants. Using the idea of regressor chains, an ensemble method is developed to attain the highest prediction accuracy. Collected data is divided into two sets, namely “normal” and “unusual”, using local outlier factor method. The prediction performance is tested separately for each set. It is seen that the ensemble idea shows its competence especially in situations, where collected data is classified as “unusual”. We tested the proposed method in two different real-life cases: textile manufacturing process and plastic injection molding process. Proposed DSS supports online decisions through live process monitoring screens and provides real time quality predictions, which help to minimize the total number of nonconforming products.
DOI	10.1007/978-3-030-54595-6_6
Cilt	1255

Kaynağa git Özyeğin Üniversitesi

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Özyeğin Üniversitesi

Kaynağa git

A web-based decision support system for quality prediction in manufacturing using ensemble of regressor chains

Yazar Demirel, Kenan Cem, Şahin, Ahmet, Albey, Erinç

Basım Tarihi 2020

Basım Yeri - Springer

Konu Industry 4.0, Quality prediction, Ensemble methods, Regressor chains, Decision support system

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-303054594-9

Kayıt Numarası 6933255a-55ba-4c75-b4e2-1104e540645e

Lokasyon Industrial Engineering

Tarih 2020

Örnek Metin In this study we construct a decision support system (DSS), which utilizes the production process parameters to predict the quality characteristics of final products in two different manufacturing plants. Using the idea of regressor chains, an ensemble method is developed to attain the highest prediction accuracy. Collected data is divided into two sets, namely “normal” and “unusual”, using local outlier factor method. The prediction performance is tested separately for each set. It is seen that the ensemble idea shows its competence especially in situations, where collected data is classified as “unusual”. We tested the proposed method in two different real-life cases: textile manufacturing process and plastic injection molding process. Proposed DSS supports online decisions through live process monitoring screens and provides real time quality predictions, which help to minimize the total number of nonconforming products.

DOI 10.1007/978-3-030-54595-6_6

Cilt 1255