Total Quality Management Through Defect Detection in Manufacturing Processes Using Machine Learning Algorithms

Almira S. Kahya; Selin Şişmanoğlu; Zeynep Erçin; Hatice Camgöz Akdağ

doi:10.1007/978-3-030-31343-2_44

Total Quality Management Through Defect Detection in Manufacturing Processes Using Machine Learning Algorithms

Almira S. Kahya^*, Selin Şişmanoğlu, Zeynep Erçin, Hatice Camgöz Akdağ

^*Bu çalışma için yazışmadan sorumlu yazar

İşletme Mühendisliği Bölümü

Istanbul Technical University

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

2 Atıf (Scopus)

Özet

Total Quality Management is the new raising value of all industries. The more it is revealed that TQM is one of the key success factors for the companies, the more it is being absorbed by the industries. This study aims to analyze TQM approaches considering its history and development worldwide while observing manufacturing industry with machine learning applications in order to identify the defects in the process before completed which contributes continuous improvement to the system. Also, different descriptions of quality according to the customer satisfaction will be examined.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Proceedings of the International Symposium for Production Research 2019
Editörler	Numan M. Durakbasa, Muhammed Nafis Osman Zahid, Radhiyah Abd. Aziz, Ahmad Razlan Yusoff, Nafrizuan Mat Yahya, Fazilah Abdul Aziz, Mohd Yazid Abu, M. Günes Gençyilmaz
Yayınlayan	Springer Science and Business Media Deutschland GmbH
Sayfalar	508-516
Sayfa sayısı	9
ISBN (Basılı)	9783030313425, 9789811509490
DOI'lar	https://doi.org/10.1007/978-3-030-31343-2_44
Yayın durumu	Yayınlandı - 2020
Etkinlik	19th International Symposium for Production Research, ISPR 2019 - Vienna, Austria Süre: 28 Ağu 2019 → 30 Ağu 2019

Yayın serisi

Adı	Lecture Notes in Mechanical Engineering
ISSN (Basılı)	2195-4356
ISSN (Elektronik)	2195-4364

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	19th International Symposium for Production Research, ISPR 2019
Ülke/Bölge	Austria
Şehir	Vienna
Periyot	28/08/19 → 30/08/19

Bibliyografik not

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Belgeye Erişim

10.1007/978-3-030-31343-2_44

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Kahya, A. S., Şişmanoğlu, S., Erçin, Z., & Akdağ, H. C. (2020). Total Quality Management Through Defect Detection in Manufacturing Processes Using Machine Learning Algorithms. In N. M. Durakbasa, M. N. Osman Zahid, R. Abd. Aziz, A. R. Yusoff, N. Mat Yahya, F. Abdul Aziz, M. Yazid Abu, & M. G. Gençyilmaz (Eds.), Proceedings of the International Symposium for Production Research 2019 (pp. 508-516). (Lecture Notes in Mechanical Engineering). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-31343-2_44

Kahya, Almira S. ; Şişmanoğlu, Selin ; Erçin, Zeynep et al. / Total Quality Management Through Defect Detection in Manufacturing Processes Using Machine Learning Algorithms. Proceedings of the International Symposium for Production Research 2019. editör / Numan M. Durakbasa ; Muhammed Nafis Osman Zahid ; Radhiyah Abd. Aziz ; Ahmad Razlan Yusoff ; Nafrizuan Mat Yahya ; Fazilah Abdul Aziz ; Mohd Yazid Abu ; M. Günes Gençyilmaz. Springer Science and Business Media Deutschland GmbH, 2020. pp. 508-516 (Lecture Notes in Mechanical Engineering).

@inproceedings{ae3c5b54dbd945c3b661b9835ff2003f,

title = "Total Quality Management Through Defect Detection in Manufacturing Processes Using Machine Learning Algorithms",

abstract = "Total Quality Management is the new raising value of all industries. The more it is revealed that TQM is one of the key success factors for the companies, the more it is being absorbed by the industries. This study aims to analyze TQM approaches considering its history and development worldwide while observing manufacturing industry with machine learning applications in order to identify the defects in the process before completed which contributes continuous improvement to the system. Also, different descriptions of quality according to the customer satisfaction will be examined.",

keywords = "Continuous improvement, Customer satisfaction, Defect detection, Machine learning applications, Manufacturing, Problem solving, Quality, Total Quality Management",

author = "Kahya, {Almira S.} and Selin {\c S}i{\c s}manoğlu and Zeynep Er{\c c}in and Akdağ, {Hatice Camg{\"o}z}",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 19th International Symposium for Production Research, ISPR 2019 ; Conference date: 28-08-2019 Through 30-08-2019",

year = "2020",

doi = "10.1007/978-3-030-31343-2_44",

language = "English",

isbn = "9783030313425",

series = "Lecture Notes in Mechanical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "508--516",

editor = "Durakbasa, {Numan M.} and {Osman Zahid}, {Muhammed Nafis} and {Abd. Aziz}, Radhiyah and Yusoff, {Ahmad Razlan} and {Mat Yahya}, Nafrizuan and {Abdul Aziz}, Fazilah and {Yazid Abu}, Mohd and Gen{\c c}yilmaz, {M. G{\"u}nes}",

booktitle = "Proceedings of the International Symposium for Production Research 2019",

address = "Germany",

}

Kahya, AS, Şişmanoğlu, S, Erçin, Z & Akdağ, HC 2020, Total Quality Management Through Defect Detection in Manufacturing Processes Using Machine Learning Algorithms. in NM Durakbasa, MN Osman Zahid, R Abd. Aziz, AR Yusoff, N Mat Yahya, F Abdul Aziz, M Yazid Abu & MG Gençyilmaz (eds), Proceedings of the International Symposium for Production Research 2019. Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH, pp. 508-516, 19th International Symposium for Production Research, ISPR 2019, Vienna, Austria, 28/08/19. https://doi.org/10.1007/978-3-030-31343-2_44

Total Quality Management Through Defect Detection in Manufacturing Processes Using Machine Learning Algorithms. / Kahya, Almira S.; Şişmanoğlu, Selin; Erçin, Zeynep et al.
Proceedings of the International Symposium for Production Research 2019. ed. / Numan M. Durakbasa; Muhammed Nafis Osman Zahid; Radhiyah Abd. Aziz; Ahmad Razlan Yusoff; Nafrizuan Mat Yahya; Fazilah Abdul Aziz; Mohd Yazid Abu; M. Günes Gençyilmaz. Springer Science and Business Media Deutschland GmbH, 2020. p. 508-516 (Lecture Notes in Mechanical Engineering).

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

TY - GEN

T1 - Total Quality Management Through Defect Detection in Manufacturing Processes Using Machine Learning Algorithms

AU - Kahya, Almira S.

AU - Şişmanoğlu, Selin

AU - Erçin, Zeynep

AU - Akdağ, Hatice Camgöz

PY - 2020

Y1 - 2020

N2 - Total Quality Management is the new raising value of all industries. The more it is revealed that TQM is one of the key success factors for the companies, the more it is being absorbed by the industries. This study aims to analyze TQM approaches considering its history and development worldwide while observing manufacturing industry with machine learning applications in order to identify the defects in the process before completed which contributes continuous improvement to the system. Also, different descriptions of quality according to the customer satisfaction will be examined.

AB - Total Quality Management is the new raising value of all industries. The more it is revealed that TQM is one of the key success factors for the companies, the more it is being absorbed by the industries. This study aims to analyze TQM approaches considering its history and development worldwide while observing manufacturing industry with machine learning applications in order to identify the defects in the process before completed which contributes continuous improvement to the system. Also, different descriptions of quality according to the customer satisfaction will be examined.

KW - Continuous improvement

KW - Customer satisfaction

KW - Defect detection

KW - Machine learning applications

KW - Manufacturing

KW - Problem solving

KW - Quality

KW - Total Quality Management

UR - http://www.scopus.com/inward/record.url?scp=85076223813&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-31343-2_44

DO - 10.1007/978-3-030-31343-2_44

M3 - Conference contribution

AN - SCOPUS:85076223813

SN - 9783030313425

SN - 9789811509490

T3 - Lecture Notes in Mechanical Engineering

SP - 508

EP - 516

BT - Proceedings of the International Symposium for Production Research 2019

A2 - Durakbasa, Numan M.

A2 - Osman Zahid, Muhammed Nafis

A2 - Abd. Aziz, Radhiyah

A2 - Yusoff, Ahmad Razlan

A2 - Mat Yahya, Nafrizuan

A2 - Abdul Aziz, Fazilah

A2 - Yazid Abu, Mohd

A2 - Gençyilmaz, M. Günes

PB - Springer Science and Business Media Deutschland GmbH

T2 - 19th International Symposium for Production Research, ISPR 2019

Y2 - 28 August 2019 through 30 August 2019

ER -

Kahya AS, Şişmanoğlu S, Erçin Z, Akdağ HC. Total Quality Management Through Defect Detection in Manufacturing Processes Using Machine Learning Algorithms. In Durakbasa NM, Osman Zahid MN, Abd. Aziz R, Yusoff AR, Mat Yahya N, Abdul Aziz F, Yazid Abu M, Gençyilmaz MG, editörler, Proceedings of the International Symposium for Production Research 2019. Springer Science and Business Media Deutschland GmbH. 2020. p. 508-516. (Lecture Notes in Mechanical Engineering). doi: 10.1007/978-3-030-31343-2_44

Total Quality Management Through Defect Detection in Manufacturing Processes Using Machine Learning Algorithms

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