An Application on Analysis of Mask Production Factors with Clustering Approach

Semra Tebrizcik; Süleyman Ersöz; Adnan Aktepe

Makale detayları

An Application on Analysis of Mask Production Factors with Clustering Approach

Yazarlar:

Semra Tebrizcik | Süleyman Ersöz | Adnan Aktepe

Yayıncı:

Kirikkale University

Ana Başlıklar:

Industrial Engineering

Dergi:

International Journal of Engineering Research and Development Vol.15 Issue 2, pp.626-635

Belge Tipi:

Makale

Yayın Tarihi:

7/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001284751

pISSN:

1308-5506

Anahtar kelimeler:

Özet:

 With the development of technology, large databases are becoming more accessible. Today, it is possible to use large databases in many fields. By using data kept in databases and data mining approaches, meaningful information and rules are discovered. Thus, information discoveries are made that will be useful for businesses. In this study, the production data of a factory producing surgical medical masks was used. Clustering analyzes of the variables that are effective in the production of faulty or error-free masks in the quality control stage were made with the Two Step method and the properties of the resulting clusters were compared. The most effective variables in cluster partitioning are; It has been observed that the amount of speed used in the creation of the mask body, the type of fabric of the middle layer of the mask body consisting of 3 layers, and the amount of ultrasonic heat used to create the pleats on the body. The information obtained to improve the performance of production activities will be used to improve the process.

 With the development of technology, large databases are becoming more accessible. Today, it is possible to use large databases in many fields. By using data kept in databases and data mining approaches, meaningful information and rules are discovered. Thus, information discoveries are made that will be useful for businesses. In this study, the production data of a factory producing surgical medical masks was used. Clustering analyzes of the variables that are effective in the production of faulty or error-free masks in the quality control stage were made with the Two Step method and the properties of the resulting clusters were compared. The most effective variables in cluster partitioning are; It has been observed that the amount of speed used in the creation of the mask body, the type of fabric of the middle layer of the mask body consisting of 3 layers, and the amount of ultrasonic heat used to create the pleats on the body. The information obtained to improve the performance of production activities will be used to improve the process.

 With the development of technology, large databases are becoming more accessible. Today, it is possible to use large databases in many fields. By using data kept in databases and data mining approaches, meaningful information and rules are discovered. Thus, information discoveries are made that will be useful for businesses. In this study, the production data of a factory producing surgical medical masks was used. Clustering analyzes of the variables that are effective in the production of faulty or error-free masks in the quality control stage were made with the Two Step method and the properties of the resulting clusters were compared. The most effective variables in cluster partitioning are; It has been observed that the amount of speed used in the creation of the mask body, the type of fabric of the middle layer of the mask body consisting of 3 layers, and the amount of ultrasonic heat used to create the pleats on the body. The information obtained to improve the performance of production activities will be used to improve the process.

 Teknolojinin gelişmesi ile birlikte büyük veri tabanları daha ulaşılabilir hale gelmektedir. Günümüzde de birçok alanda büyük veri tabanlarının kullanımına imkân verilmektedir. Veri tabanlarında tutulan veriler ile veri madenciliği yaklaşımlarından faydalanarak anlamlı bilgiler ve kurallar keşfedilmektedir. Böylelikle işletmeler için yararlı olacak bilgi keşifleri yapılmaktadır. Bu çalışmada cerrahi tıbbi maske üretimi yapmakta olan bir fabrikanın üretim verileri kullanılmıştır. Kalite kontrol aşamasında hatalı veya hatasız maskelerin üretilmesinde etkili olan değişkenlerin kümeleme analizleri Two Step yöntemi ile yapılarak ortaya çıkan kümelerin özellikleri karşılaştırılmıştır. Küme bölümlenmesinde en çok etkili olan değişkenlerin; maske gövdesinin oluşturulmasında kullanılan hız miktarı, 3 katmandan oluşan maske gövdesinin orta katmanına ait kumaşın türü ve gövde üzerinde bulunan pilelerin oluşturulması için kullanılan ultrasonik ısı miktarının olduğu gözlemlenmiştir. Üretim faaliyetlerinin performansını artırmak için elde edilen bilgiler süreci iyileştirmek için kullanılacaktır.

Makale detayları

An Application on Analysis of Mask Production Factors with Clustering Approach

Yazarlar:

Semra Tebrizcik | Süleyman Ersöz | Adnan Aktepe

Yayıncı:

Kirikkale University

Ana Başlıklar:

Industrial Engineering

Dergi:

International Journal of Engineering Research and Development Vol.15 Issue 2, pp.626-635

Belge Tipi:

Makale

Yayın Tarihi:

7/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001284751

pISSN:

1308-5506

Cluster analysis

Two-step method

data mining

SPSS clementine

Kümeleme analizi

Two-step yöntemi

veri madenciliği

SPSS clementine

Sayı

Makale detayları

Yazarlar: Semra Tebrizcik | Süleyman Ersöz | Adnan Aktepe

Yayıncı: Kirikkale University

Ana Başlıklar: Industrial Engineering

Dergi: International Journal of Engineering Research and Development Vol.15 Issue 2, pp.626-635

Belge Tipi: Makale

Yayın Tarihi: 7/2023

Belge Dili: Türkçe

Document ID (Org No.): 2001284751

pISSN: 1308-5506

Sayı

Yazarlar:

Semra Tebrizcik | Süleyman Ersöz | Adnan Aktepe

Yayıncı:

Kirikkale University

Ana Başlıklar:

Industrial Engineering

Dergi:

International Journal of Engineering Research and Development Vol.15 Issue 2, pp.626-635

Belge Tipi:

Makale

Yayın Tarihi:

7/2023

Belge Dili:

Türkçe

Document ID (Org No.):

2001284751

pISSN:

1308-5506