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

^*Corresponding author for this work

Department of Management Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the International Symposium for Production Research 2019
Editors	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
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	508-516
Number of pages	9
ISBN (Print)	9783030313425, 9789811509490
DOIs	https://doi.org/10.1007/978-3-030-31343-2_44
Publication status	Published - 2020
Event	19th International Symposium for Production Research, ISPR 2019 - Vienna, Austria Duration: 28 Aug 2019 → 30 Aug 2019

Publication series

Name	Lecture Notes in Mechanical Engineering
ISSN (Print)	2195-4356
ISSN (Electronic)	2195-4364

Conference

Conference	19th International Symposium for Production Research, ISPR 2019
Country/Territory	Austria
City	Vienna
Period	28/08/19 → 30/08/19

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Continuous improvement
Customer satisfaction
Defect detection
Machine learning applications
Manufacturing
Problem solving
Quality
Total Quality Management

Access to Document

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

Cite this

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. editor / 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).

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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",

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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",

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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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 - https://www.scopus.com/pages/publications/85076223813

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, editors, 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

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

Access to Document

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