Research Article
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Evaluation of hardness values in machine part surface hardening process by fuzzy quality control and process capability analysis

Year 2023, Volume: 29 Issue: 5, 440 - 450, 31.10.2023

Abstract

In the manufacture of machine parts, quenching is a method used in all areas of the industry in different environments and conditions. The obtained hardness values must be in the appropriate value range according to the requirements of the function of the relevant machine part. It is common to measure hardness values according to ASTM E10- 01 (Standard Test Method for Brinell Hardness of Metallic Materials), which is one of the hardness measurement methods. In the study, Shewart Average (𝑋̅) and Range (𝑅) quality control charts and process capability analysis were used to test the compliance of the hardness values obtained as a result of the quenching-tempering process according to the technical drawing of the relevant part in a company manufacturing machine parts. In addition, due to the approximate value of the observation values, the observation values were converted into fuzzy numbers and fuzzy quality control analyzes were performed with the "Fuzzy rules method for TFN case" method, and process adequacy was measured. According to both methods, as a result of the study, it was determined that the process variability was high and therefore the process was not sufficient.

References

  • [1] Callister WD, Rethwisch DG. Fundamentals of Materials Science and Engineering: An Integrated Approach, 4th ed. John Wiley & Sons, New York, USA, 2000.
  • [2] Ashby MF, Johnson K. Materials and Design: The Art and Science of Material Selection in Product Design. 2nd ed. Butterworth-Heinemann, Oxford, 2010.
  • [3] Işığıçok, E. Toplam Kalite Yönetimi Bakış Açısıyla İstatistiksel Kalite Kontrol. 2. Baskı. Bursa, Türkiye, Ezgi Kitapevi, 2012.
  • [4] Kaya İ, Kahraman C. “Process capability analyses based on fuzzy measurements and fuzzy control charts”. Expert Systems with Applications, 38, 3172-3184, 2011.
  • [5] Soysal H. Fuzzy Process Capability Analysis and An Application. MSc Thesis, Sakarya University, Sakarya, Turkey, 2014.
  • [6] Zadeh LA. “Fuzzy sets”. Information and Control, 8, 338-359, 1965.
  • [7] Bradddshaw CW. “A Fuzzy set theoretic interpretation of economic control limits”. European Journal of Operational Research, 13, 403-408, 1983.
  • [8] Wang JH, Raz T. “On the construction of control charts using linguistic variables”. International of Production Research, 28(3), 477-487,1990.
  • [9] Raz T, Wang JH. “Probabilistic and membership approaches in the construction of control chart for linguistic data”. Production Planning Control, 3, 147-157, 1990.
  • [10] Kanagawa A, Tamaki F, Ohta H. “Control charts for process average and variability based on linguistic data”. International Journal of Production Research, 31(4), 913-922, 1993.
  • [11] Wang CR, Chen CH. “Economic statistical 𝑛𝑝 control chart designs based on fuzzy optimization”. International Journal of Quality&Reliability Management, 12(1), 82-92, 1995.
  • [12] Kahraman C, Tolga E, Ulukan Z. "Using triangular fuzzy numbers in the tests of control charts for unnatural patterns". Proceedings 1995 INRIA/IEEE Symposium on Emerging Technologies and Factory Automation, ETFA'95, Paris, France, 10-13 October 1995.
  • [13] Taleb H, Limam M. “On fuzzy and probabilistic control Charts”. International Journal of Production Research, 40(12), 2849-2863, 2002.
  • [14] Gülbay M, Kahraman C, Ruan D. “Alpha-cut fuzzy control charts for linguistic data”. International Journal of Intelligent Systems, 19(12), 1173-1195, 2004.
  • [15] Aytaç E. Kalite Kontrolde Bulanık Mantık Yaklaşımı ve Bir Uygulama. Pamukkale Üniversitesi, Yüksek Lisans Tezi, Denizli, Türkiye, 2006.
  • [16] Gülbay M, Kahraman C. “Bulanık kontrol diyagramı modellerinin geliştirilmesi: Direkt bulanık yaklaşım”. İTÜ Dergisi, 7(2), 95-105, 2008.
  • [17] Senturk S, Erginel N. “Development of fuzzy and 𝑥 ̃ - 𝑅̃ and 𝑥 ̃ - 𝑆̃ control charts using α-cuts”. Information Sciences, 179, 1542-1551, 2009.
  • [18] Alizadeh HM, Ghomi F. “Fuzzy development of mean and range control charts using statistical properties of different representative values”. Journal of Intelligent & Fuzzy Systems, 22, 253-265, 2011.
  • [19] Aslangiray A. İstatistiksel Süreç Kontrolünde Bulanık Mantık Yaklaşımı ve Bir Uygulama. Yüksek Lisans Tezi, Akdeniz Üniversitesi, Antalya, Türkiye, 2011.
  • [20] Pekin Alakoç N. Bulanık Kalite Kontrol Grafiklerinde Yeni Bir Yaklaşım (Oran Yaklaşımı). Doktora Tezi. Ankara Üniversitesi, Ankara, Türkiye, 2012.
  • [21] Kaya İ, Erdoğan M, Yıldız C. “Analysis and control of variability by using fuzzy individual control charts”. Applies Soft Computing, 51, 370-381, 2017.
  • [22] Şentürk S, Antucheviciene J. “Interval Type-2 c-Control charts:An application in a food company”. Informatica, 28(2), 269-283, 2017.
  • [23] Teksen HE, Anagün AS. “Interval type-2 fuzzy c-control charts using likelihood and reduction methods”. Soft Computing, 22,4921-4934, 2018.
  • [24] Pekin Alakoç N, Apaydın A. “A fuzzy control chart approach for attributes and variables”. Engineering, Technology & Applies Science Research, 8(5), 3360-3365, 2018.
  • [25] Tekşen HE, Anagün AS. “Different methods to fuzzy 𝑋̅-𝑅 control charts used in production Interval type-2 fuzzy set example”. Journal of Enterprise Information Management, 31(6), 848-866, 2018.
  • [26] Zahir Khan MZ, Farid Khan M, Aslam M, Niaki STA, Mughal AZ. “A Fuzzy EWMA attribute control chart to monitor proces mean”. Information, 9(312), 1-13, 2018.
  • [27] Santos Mendes A, Machado MAG, Rocha Rizol PMS. “Fuzzy control chart for monitoring mean and range of univariate processes”. Pesquisa Operacional, 39(2), 339-357, 2019.
  • [28] Hesamian G, Akbari MG, Yaghoobpoor R. “Quality control process based on fuzzy random variables”. IEEE Transactions of Fuzzy Systems, 27(4), 671-685, 2019.
  • [29] Razali H, Abdullah L, Ghani TA, Aimran N. Application of Fuzzy control charts: A Review of its Analysis and findings. Editors: Awang M. Emamian S.S. Yusuf F. Advances in Material Sciences and Engineering. 483-490, Springer, 2020.
  • [30] Rodriguez-Alvarez JL, Lopez-Herrera R, VillalonTurrubiates IE, Molina-Arredondo RD, Garcia Alcaraz JL, Hernandez-Olvera OD. “Analysis and control of the paper moisture content variability by using fuzzy and traditional individual control charts”. Chemometrics and Intelligent Laboratory Systems, 208, 1-12, 2021.
  • [31] Teksen HE, Anagün AS. “Intuitionistic fuzzy c-control charts using defuzzification and likelihood methods”. Journal of Intelligent & Fuzzy Systems, 39(5), 6465-6473, 2020.
  • [32] Oktay E. Kalite Kontrol Grafikleri. 1. Baskı. Erzurum, Türkiye, Şafak Yayınevi, 1998.
  • [33] Montgomery DC. Introduction to Statistical Quality Control. 6th ed. New York, USA, Wiley, 2009.
  • [34] Soysal H, Boran S. “Bulanık 𝑋̅-𝑅 diyagramları kullanılarak bulanık süreç yeterlilik analizi”. Sakarya Üniversitesi Fen Bilimleri Dergisi, 19(1), 15-26, 2015.
  • [35] Tsai CC, Chen CC. “Making decision to evaluate process capability index 𝐶𝑝 with fuzzy numbers”. International Journal of Advanced Manufacturing Technology, 30, 334-339, 2006.
  • [36] Andaç A. Çağdaş Kalite Anlayışı İçerisinde ISO 9001 Kalite Güvencesi Sistemi Standardının Yorumu ve Uygulama Örnekleri. 1. Baskı. İstanbul, Türkiye, Çağlayan Kitapevi, 1996.

Makine parçası yüzey sertleştirme işleminde sertlik değerlerinin bulanık kalite kontrolü ve süreç yeterlilik analizi ile değerlendirilmesi

Year 2023, Volume: 29 Issue: 5, 440 - 450, 31.10.2023

Abstract

Makine parçası imalatında, su verme yoluyla sertleştirme, endüstrinin her alanında farklı ortam ve koşullarda kullanılan bir yöntemdir. Elde edilen sertlik değerlerinin, ilgili makine parçasının işlevinin gerekliliklerine göre uygun değer aralığında olması gerekmektedir. Sertlik değerlerinin, sertlik ölçme yöntemlerinden biri olan ASTM E10- 01 (Metalik Malzemelerin Brinell Sertliği için Standart Test Yöntemi)’e göre ölçümü yaygındır. Çalışmada makine parçası imal eden bir işletmede ilgili parçanın Teknik resmine göre yapılan su vermemenevişleme işlemi neticesinde elde edilen sertlik değerlerinin spesifikasyonlara uygunluğunu test etmek için, Shewart Ortalama (𝑋̅) ve Aralık (𝑅) kalite kontrol grafikleri ve süreç yeterlilik analizi kullanılmıştır. Ayrıca, gözlem değerlerinin yaklaşık değer içermesi nedeniyle, gözlem değerleri bulanık sayılara çevrilip “Fuzzy rules method for TFN case” yöntemi ile bulanık kalite kontrol analizleri yapılmış ve süreç yeterliliği ölçülmüştür. Her iki yönteme göre, çalışma sonucunda, proses değişkenliğinin fazla olduğu ve bundan dolayı da prosesin yeterli olmadığı tespit edilmiştir.

References

  • [1] Callister WD, Rethwisch DG. Fundamentals of Materials Science and Engineering: An Integrated Approach, 4th ed. John Wiley & Sons, New York, USA, 2000.
  • [2] Ashby MF, Johnson K. Materials and Design: The Art and Science of Material Selection in Product Design. 2nd ed. Butterworth-Heinemann, Oxford, 2010.
  • [3] Işığıçok, E. Toplam Kalite Yönetimi Bakış Açısıyla İstatistiksel Kalite Kontrol. 2. Baskı. Bursa, Türkiye, Ezgi Kitapevi, 2012.
  • [4] Kaya İ, Kahraman C. “Process capability analyses based on fuzzy measurements and fuzzy control charts”. Expert Systems with Applications, 38, 3172-3184, 2011.
  • [5] Soysal H. Fuzzy Process Capability Analysis and An Application. MSc Thesis, Sakarya University, Sakarya, Turkey, 2014.
  • [6] Zadeh LA. “Fuzzy sets”. Information and Control, 8, 338-359, 1965.
  • [7] Bradddshaw CW. “A Fuzzy set theoretic interpretation of economic control limits”. European Journal of Operational Research, 13, 403-408, 1983.
  • [8] Wang JH, Raz T. “On the construction of control charts using linguistic variables”. International of Production Research, 28(3), 477-487,1990.
  • [9] Raz T, Wang JH. “Probabilistic and membership approaches in the construction of control chart for linguistic data”. Production Planning Control, 3, 147-157, 1990.
  • [10] Kanagawa A, Tamaki F, Ohta H. “Control charts for process average and variability based on linguistic data”. International Journal of Production Research, 31(4), 913-922, 1993.
  • [11] Wang CR, Chen CH. “Economic statistical 𝑛𝑝 control chart designs based on fuzzy optimization”. International Journal of Quality&Reliability Management, 12(1), 82-92, 1995.
  • [12] Kahraman C, Tolga E, Ulukan Z. "Using triangular fuzzy numbers in the tests of control charts for unnatural patterns". Proceedings 1995 INRIA/IEEE Symposium on Emerging Technologies and Factory Automation, ETFA'95, Paris, France, 10-13 October 1995.
  • [13] Taleb H, Limam M. “On fuzzy and probabilistic control Charts”. International Journal of Production Research, 40(12), 2849-2863, 2002.
  • [14] Gülbay M, Kahraman C, Ruan D. “Alpha-cut fuzzy control charts for linguistic data”. International Journal of Intelligent Systems, 19(12), 1173-1195, 2004.
  • [15] Aytaç E. Kalite Kontrolde Bulanık Mantık Yaklaşımı ve Bir Uygulama. Pamukkale Üniversitesi, Yüksek Lisans Tezi, Denizli, Türkiye, 2006.
  • [16] Gülbay M, Kahraman C. “Bulanık kontrol diyagramı modellerinin geliştirilmesi: Direkt bulanık yaklaşım”. İTÜ Dergisi, 7(2), 95-105, 2008.
  • [17] Senturk S, Erginel N. “Development of fuzzy and 𝑥 ̃ - 𝑅̃ and 𝑥 ̃ - 𝑆̃ control charts using α-cuts”. Information Sciences, 179, 1542-1551, 2009.
  • [18] Alizadeh HM, Ghomi F. “Fuzzy development of mean and range control charts using statistical properties of different representative values”. Journal of Intelligent & Fuzzy Systems, 22, 253-265, 2011.
  • [19] Aslangiray A. İstatistiksel Süreç Kontrolünde Bulanık Mantık Yaklaşımı ve Bir Uygulama. Yüksek Lisans Tezi, Akdeniz Üniversitesi, Antalya, Türkiye, 2011.
  • [20] Pekin Alakoç N. Bulanık Kalite Kontrol Grafiklerinde Yeni Bir Yaklaşım (Oran Yaklaşımı). Doktora Tezi. Ankara Üniversitesi, Ankara, Türkiye, 2012.
  • [21] Kaya İ, Erdoğan M, Yıldız C. “Analysis and control of variability by using fuzzy individual control charts”. Applies Soft Computing, 51, 370-381, 2017.
  • [22] Şentürk S, Antucheviciene J. “Interval Type-2 c-Control charts:An application in a food company”. Informatica, 28(2), 269-283, 2017.
  • [23] Teksen HE, Anagün AS. “Interval type-2 fuzzy c-control charts using likelihood and reduction methods”. Soft Computing, 22,4921-4934, 2018.
  • [24] Pekin Alakoç N, Apaydın A. “A fuzzy control chart approach for attributes and variables”. Engineering, Technology & Applies Science Research, 8(5), 3360-3365, 2018.
  • [25] Tekşen HE, Anagün AS. “Different methods to fuzzy 𝑋̅-𝑅 control charts used in production Interval type-2 fuzzy set example”. Journal of Enterprise Information Management, 31(6), 848-866, 2018.
  • [26] Zahir Khan MZ, Farid Khan M, Aslam M, Niaki STA, Mughal AZ. “A Fuzzy EWMA attribute control chart to monitor proces mean”. Information, 9(312), 1-13, 2018.
  • [27] Santos Mendes A, Machado MAG, Rocha Rizol PMS. “Fuzzy control chart for monitoring mean and range of univariate processes”. Pesquisa Operacional, 39(2), 339-357, 2019.
  • [28] Hesamian G, Akbari MG, Yaghoobpoor R. “Quality control process based on fuzzy random variables”. IEEE Transactions of Fuzzy Systems, 27(4), 671-685, 2019.
  • [29] Razali H, Abdullah L, Ghani TA, Aimran N. Application of Fuzzy control charts: A Review of its Analysis and findings. Editors: Awang M. Emamian S.S. Yusuf F. Advances in Material Sciences and Engineering. 483-490, Springer, 2020.
  • [30] Rodriguez-Alvarez JL, Lopez-Herrera R, VillalonTurrubiates IE, Molina-Arredondo RD, Garcia Alcaraz JL, Hernandez-Olvera OD. “Analysis and control of the paper moisture content variability by using fuzzy and traditional individual control charts”. Chemometrics and Intelligent Laboratory Systems, 208, 1-12, 2021.
  • [31] Teksen HE, Anagün AS. “Intuitionistic fuzzy c-control charts using defuzzification and likelihood methods”. Journal of Intelligent & Fuzzy Systems, 39(5), 6465-6473, 2020.
  • [32] Oktay E. Kalite Kontrol Grafikleri. 1. Baskı. Erzurum, Türkiye, Şafak Yayınevi, 1998.
  • [33] Montgomery DC. Introduction to Statistical Quality Control. 6th ed. New York, USA, Wiley, 2009.
  • [34] Soysal H, Boran S. “Bulanık 𝑋̅-𝑅 diyagramları kullanılarak bulanık süreç yeterlilik analizi”. Sakarya Üniversitesi Fen Bilimleri Dergisi, 19(1), 15-26, 2015.
  • [35] Tsai CC, Chen CC. “Making decision to evaluate process capability index 𝐶𝑝 with fuzzy numbers”. International Journal of Advanced Manufacturing Technology, 30, 334-339, 2006.
  • [36] Andaç A. Çağdaş Kalite Anlayışı İçerisinde ISO 9001 Kalite Güvencesi Sistemi Standardının Yorumu ve Uygulama Örnekleri. 1. Baskı. İstanbul, Türkiye, Çağlayan Kitapevi, 1996.
There are 36 citations in total.

Details

Primary Language English
Subjects Mechanical Engineering (Other)
Journal Section Research Article
Authors

Ahmet Bilal Şengül

Ümran Şengül

Publication Date October 31, 2023
Published in Issue Year 2023 Volume: 29 Issue: 5

Cite

APA Şengül, A. B., & Şengül, Ü. (2023). Evaluation of hardness values in machine part surface hardening process by fuzzy quality control and process capability analysis. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 29(5), 440-450.
AMA Şengül AB, Şengül Ü. Evaluation of hardness values in machine part surface hardening process by fuzzy quality control and process capability analysis. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. October 2023;29(5):440-450.
Chicago Şengül, Ahmet Bilal, and Ümran Şengül. “Evaluation of Hardness Values in Machine Part Surface Hardening Process by Fuzzy Quality Control and Process Capability Analysis”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 29, no. 5 (October 2023): 440-50.
EndNote Şengül AB, Şengül Ü (October 1, 2023) Evaluation of hardness values in machine part surface hardening process by fuzzy quality control and process capability analysis. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 29 5 440–450.
IEEE A. B. Şengül and Ü. Şengül, “Evaluation of hardness values in machine part surface hardening process by fuzzy quality control and process capability analysis”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 29, no. 5, pp. 440–450, 2023.
ISNAD Şengül, Ahmet Bilal - Şengül, Ümran. “Evaluation of Hardness Values in Machine Part Surface Hardening Process by Fuzzy Quality Control and Process Capability Analysis”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi 29/5 (October 2023), 440-450.
JAMA Şengül AB, Şengül Ü. Evaluation of hardness values in machine part surface hardening process by fuzzy quality control and process capability analysis. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2023;29:440–450.
MLA Şengül, Ahmet Bilal and Ümran Şengül. “Evaluation of Hardness Values in Machine Part Surface Hardening Process by Fuzzy Quality Control and Process Capability Analysis”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol. 29, no. 5, 2023, pp. 440-5.
Vancouver Şengül AB, Şengül Ü. Evaluation of hardness values in machine part surface hardening process by fuzzy quality control and process capability analysis. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 2023;29(5):440-5.

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