Araştırma Makalesi
BibTex RIS Kaynak Göster

Metal Sektöründe üretim sürelerine etki eden faktörlerin veri madenciliği yöntemleriyle tespit edilmesi

Yıl 2021, Cilt: 36 Sayı: 4, 1949 - 1962, 02.09.2021
https://doi.org/10.17341/gazimmfd.736659

Öz

Günümüzün küresel rekabet koşullarında hayatta kalabilmek için işletmeler üretimlerinde düşük teslim zamanı, düşük maliyet, yüksek kalite ve yüksek esnekliği hedeflemek zorundadırlar. Proje bazlı üretim yapan firmaların bu hedeflere ulaşabilmesi için siparişe dayalı üretim yöntemini tercih etmeleri gerekmektedir. Siparişe dayalı üretimde ürünün teslim tarihinde hazır olması büyük önem taşımaktadır. Teslim tarihlerinin azaltılması için üretim süresini etkileyen faktörlerin tespit edilmesi gerekmektedir. Üretim süresine etki eden faktörlerin tespit edilmesi, bu faktörler üzerinde yapılabilecek iyileştirmeleri öngörmeyi sağlayacaktır. Bu çalışmada üretim süresine etki eden faktörlerin veri madenciliği yöntemleri ile belirlenebileceği metal sektöründe üretim yapan bir firmaya uygulanarak gösterilmiştir. Bu faktörler araştırılırken veri madenciliğinden çeşitli sınıflandırma algoritmaları kullanılmıştır. Uygulama sonucunda en iyi sonuçlar random tree algoritması ile elde edilmiştir. Üretim süresine etki eden faktörler parça adı, makine adı, üretim ayı, ortalama sıcaklık, operatör adı, tezgâh boyutu, ürün miktarı olarak bulunmuştur. Uygulama sonucunda üretilen bilgiler ile işletmeye üretim süreçleri için iyileştirme tavsiyeleri verilmiştir. Ham veri kümesi ek dosyada verilmiştir.

Teşekkür

Güven Mühendislik Makine Ltd. Şti. ’ye çalışmada kullanılan verilere ulaşmada sağladıkları destekten dolayı teşekkür ederiz.

Kaynakça

  • Harding J. A., Shahbaz M., Kusiak, A., Data mining in manufacturing: a review, Asme The Journal of Manufacturing Science and Engineering, 128(4), 969–976, 2006.
  • Choudhary A. K., Harding, J. A., Tiwari, M. K., Data mining in manufacturing: a review based on the kind of knowledge, Journal of Intellıgent Manufacturing, 20(5), 501-521, 2009.
  • Usuga Cadavid J. P., Lamouri S., Grabot B., Pellerin R., Fortin A,. Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0., Journal of Intelligent Manufacturing, 31, 1531–1558, 2020.
  • Köksal G., Batmaz I., Testik M. C., A review of data mining applications for quality improvement in manufacturing industry, Expert Systems with Applications, 38(10), 13448-13467, 2011.
  • Sim S. K., Chan Y. W., A knowledge-based expert system for rolling element bearing selection in mechanical engineering design, Artificial Intelligence Engineering, 6(3), 125–135, 1992.
  • Romanowski C. J., Nagi R., Data mining for design and manufacture: methods and applications, Editör: Braha D., Kluwer Academic, Cilt 3, Dordrecht, 161–178, 2001.
  • Giess, M. D., Culley, S. J., Shepherd, A., Informing design using data mining methods., Asme International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 207-215, 2002.
  • Gardner M., Bieker J., Data mining solves tough semi conductor problems, Proceedings of The Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston-ABD, 376–383, Ağustos 2000.
  • Park K. S., Kim S. H., Artificial intelligence approaches to determination of cnc machining parameters in manufacturing: a review, Artificial Intelligence Engineering, 12, 127–134, 1998.
  • Liao T. W., Chen J. H., Triantaphyllou E., Data mining applications in industrial engineering: a perspective, International Conference on Computers and Industrial Engineering, New Orleans-LA, 265–276, 1999.
  • Skormin V. A., Gorodetski V. I., PopYack I. J., Data mining technology for failure of prognostic of avionics, IEEE Transactions on Aerospace and Electronic Systems, 38(2), 388–403, 2002.
  • Chen W. C., Tseng S. S., Wang, C. Y., A novel manufacturing defect detection method using data mining approach, Lecture Notes in Artificial Intelligence, 3029, 77–86, 2004.
  • Batanov D., Nagarur N., Nitikhumkasem P., Expert-mm: a knowledge based system for maintenance management, Artificial Intelligence Engineering, 8, 283–291, 1993.
  • Çetin M., An application of data mining in a manufacturing industry, Yüksek Lisans tezi, Sakarya Üniversitesi, Fen Bilimleri Enstitüsü, Sakarya, 2009.
  • Tapkan P. Z., Özmen T., Determining the yarn quality by feature selection and classification in a yarn production facility, Pamukkale University Journal of Engineering Sciences, 24(4), 713-719, 2018.
  • Bilekdemir G., Manufacturing lead time estimation using data mining techniques, Yüksek Lisans Tezi, Dokuz Eylül Üniversitesi, Sosyal Bilimler Enstitüsü, İzmir, 2010.
  • Türkoğlu B., Komesli M., Ünlütürk M. S., An industrial case study on data mining, International management information systems conference, Ankara-Türkiye, 107-110, 26-28 Ekim 2018.
  • Turker A. K., GÖLEÇ A., Aktepe A., Ersoz S., Ipek M., Cagil G., A real-time system design using data mining for estimation of delayed orders an application, Journal of the Faculty of Engineering and Architecture of Gazı University, 35(2), 709-724, 2020.
  • Chen S., Li X., Liu R., Zeng S., Extension data mining method for improving product manufacturing quality, Procedia Computer Science, 162, 146-155, 2019.
  • Nkonyana T., Sun Y., Twala B., Dogo E., Performance evaluation of data mining techniques in steel manufacturing industry, Procedia Manufacturing, 35, 623–628, 2019.
  • Cheng Y., Chen K., Sun H., Zhang Y., Tao F., Data and knowledge mining with big data towards smart production, Journal of Industrial Information Integration, 9, 1-13, 2018.
  • Emre İ. E., Selçukhan Erol Ç., Statistics or data mining for data analysis, International Journal of Informatics Technologies, 10(2), 161-167, 2017.
  • Gürbüz F., Özbakır L., Yapıcı H., Data mining application on component reports of an airline company in turkey, Journal of the Faculty of Engineering and Architecture of Gazi University, 24(1), 73-78, 2009.
  • Koyuncugil A., Özgülbaş N., Data mining: using and applications in medicine and healthcare, International Journal of Informatics Technologies, 2(2), 2009.
  • Fayyad U., Piatetsky-Shapiro G., Smyth P., From data mining to knowledge discovery in databases, AI Magazine, 17(3), 37-37, 1996.
  • Çalış A., Kayapınar S., Çetinyokuş T., An application on computer and internet security with decision tree algorithms in data mining, Journal of Industrial Engineering, 253, 2-19, 2014.
  • Kaya H., Köymen K., Data mining concept and application areas, Eastern Anatolia Region Researches, 159-164, 2008.
  • Han J., M. Kamber, Data mining: concepts and techniques, Morgan kaufmann Publishers, Cilt: 3, USA, 2001.
  • Budak H., Feature selection methods and a new approach, Süleyman Demirel University Journal of Natural and Applied Sciences, 221, 21-31, 2018.
  • Şeker S. E., http://bilgisayarkavramlari.sadievrenseker.com/2012/11/13/information-gain-bilgi-kazanimi/, Yayın tarihi Kasım 13 2012, Erişim tarihi Nisan 3, 2020.
  • Karegowdal A. G., Manjunath A. S., Jayaram M. A., Comparative study of attribute selection using gain ratio and correlation based feature selection, International Journal of Information Technology and Knowledge Management, 2(2), 271-277, July-December 2010.
  • Hall M., https://Weka.sourceforge.io/doc.dev/Weka/attributeSelection/ClassifierAttributeEval.html, Erişim tarihi Nisan 3, 2020.
  • Miles J., Banyard P., Understanding and using statistics in psychology: a practical introduction, Sage, 2007.
  • Brownlee J., http://spssistatistik.net/spss-korelasyon-analizi/, Yayın tarihi Temmuz 13 2016, Erişim tarihi Nisan 3, 2020.
  • Max B., Principles of Data Mining, Cilt 1, Springer, London-UK, 2001.
  • Nisbet R., Elder J., Miner G., Handbook of statistical analysis and data mining applications, Elsevier, Burlington, 2009.
  • Yang D. P., Jin-Lin L., Ran L., Zhou C., Applications of data mining methods in the evaluation of client credibility, Applications of Data Mining in E-Business and Finance, Amsterdam, 35-43, 2008.
  • Frank E, Witten I.H., Generating accurate rule sets without global optimization, 15th International Conference on Machine Learning, Wisconsin-USA, 24-27 July 1998.
  • Holte R., Very simple classification rules perform well on most commonly used datasets, Machine Learning, 11, 63-91, 1993.
  • Novakovic J., Strbac P., Bulatovic D., Toward optimal feature selection using ranking methods and classification algorithms, Yugoslav Journal of Operations Research, 211, 119-135, 2011.
  • Kalmegh S., Comparative analysis of the Weka classifiers rules conjunctive rule and decision table on indian news dataset by using different test mode, International Journal of Engineering Science Invention, 73, 1-9, 2018.
  • Shahzad W., Asad S., Khan M. A., International Journal of Physical Sciences 818, 885-896, 2019.
  • Kalmegh S., Analysis of weka data mining algorithm reptree, Simple Cart and RandomTree for Classification of Indian News, International Journal of Innovative Science, Engineering & Technology, 22, 2015.
  • Landwehr N., Hall M., Frank E., Logistic model trees, Machine Learning, 591(2), 161–205, 2005.
  • Provost F., Domingos P., Tree induction for probability based ranking, Machine Learning, 52(3), 199–215, 2003.
  • Nizam H., Akın S.S., Sosyal medyada makine öğrenmesi ile duygu analizinde dengeli ve dengesiz veri setlerinin performanslarının karşılaştırılması, Türkiye'de İnternet Konferansı, İzmir-Türkiye, 2014.
  • Hossin M., Sulaiman M.N., A review on evaluation metrics for data classification evaluations, International Journal of Data Mining & Knowledge Management Process, 5(2), 1-11, 2015.
  • Kumar G., Evaluation metrics for intrusion detection systems-a study, International Journal of Computer Science and Mobile Applications, 2(11), 11-17, 2014.
  • Ferri C., Hernández-Orallo, J., Modroiu R., An experimental comparison of performance measures for classification, Pattern Recognition Letters, 30, 27-38, 2009.
  • Handelman G.S., Kuan Kok H., Chandra R.V., Razavi A.H., Huang S., Brooks M., Lee M.J., Asadi H., Evaluation metrics of machine learning methods, American Journal of Roentgenology, 212, 38-43, 2019.
  • Sebastiani F., Machine learning inautomated text categorization, ACM Computing Surveys CSUR, 341, 1-47, 2002.
  • Göktepe A.B., Agar E., Lav A.H., Comparison of multilayer perceptron and adaptive neuro-fuzzy system on backcalculating the mechanical properties of flexible pavements, ARI The Bulletin of the Istanbul Technical University, 543, 65-77, 2004.
  • Çavuşoğlu Ü., Kaçar S., The performance analysis of data mining algorithms for anomaly detection, Academic Platform Journal of Engineering and Science, 72, 205-216, 2019.
  • Bilgin M., Performance analysis of classical machine learning methods in real data sets, Breast, 29, 683-688, 2017.
  • Kumar R., Indrayan A., Receiver operating characteristics roc curve for medical researchers, Indian Pediatrics, 48, 277-287,2011.
  • Gnanambal S., Thangaraj M., Meenatchi V.T., Gayathri V., Classification algorithms with attribute selection: an evaluation study using Weka, Intelligent Journal Advanced Networking and Applications, 9(6), 3640-3644, 2018.
  • Ang J., C., Mirzal A., Haron H., Nuzly H., Hamed A., Supervised unsupervised and semi-supervised feature selection: a review on gene selection, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol 13(5), 971-989, 2016.

Determining the factors that affect the production time in the metal industry utilizing data mining methods

Yıl 2021, Cilt: 36 Sayı: 4, 1949 - 1962, 02.09.2021
https://doi.org/10.17341/gazimmfd.736659

Öz

In order to survive in today's global competitive environment, companies must aim for low delivery time, low cost, high quality, and high flexibility in their production. Companies engaged in project-based production should prefer the order-based production method to achieve these goals. For order-based production method, it is very important that the product is ready at the delivery date. To reduce delivery dates, factors affecting production time should be determined. Determining the factors affecting the production time enables companies to plan the improvements that can be made on these factors. On an application, it is shown that data mining can be used to identify the factors affecting production times in metal industry. While investigating these factors, various classification algorithms were used. In the result best evaluation metrics were obtained with random tree algorithm. The features that best express the model used are part name, machine name, month of production, average temperature, operator name, machine size and product quantity. With the information produced, improvement recommendations that can be applied to the production processes are given to the company. The raw data set can be accessed as a supplementary file.

Kaynakça

  • Harding J. A., Shahbaz M., Kusiak, A., Data mining in manufacturing: a review, Asme The Journal of Manufacturing Science and Engineering, 128(4), 969–976, 2006.
  • Choudhary A. K., Harding, J. A., Tiwari, M. K., Data mining in manufacturing: a review based on the kind of knowledge, Journal of Intellıgent Manufacturing, 20(5), 501-521, 2009.
  • Usuga Cadavid J. P., Lamouri S., Grabot B., Pellerin R., Fortin A,. Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0., Journal of Intelligent Manufacturing, 31, 1531–1558, 2020.
  • Köksal G., Batmaz I., Testik M. C., A review of data mining applications for quality improvement in manufacturing industry, Expert Systems with Applications, 38(10), 13448-13467, 2011.
  • Sim S. K., Chan Y. W., A knowledge-based expert system for rolling element bearing selection in mechanical engineering design, Artificial Intelligence Engineering, 6(3), 125–135, 1992.
  • Romanowski C. J., Nagi R., Data mining for design and manufacture: methods and applications, Editör: Braha D., Kluwer Academic, Cilt 3, Dordrecht, 161–178, 2001.
  • Giess, M. D., Culley, S. J., Shepherd, A., Informing design using data mining methods., Asme International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 207-215, 2002.
  • Gardner M., Bieker J., Data mining solves tough semi conductor problems, Proceedings of The Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston-ABD, 376–383, Ağustos 2000.
  • Park K. S., Kim S. H., Artificial intelligence approaches to determination of cnc machining parameters in manufacturing: a review, Artificial Intelligence Engineering, 12, 127–134, 1998.
  • Liao T. W., Chen J. H., Triantaphyllou E., Data mining applications in industrial engineering: a perspective, International Conference on Computers and Industrial Engineering, New Orleans-LA, 265–276, 1999.
  • Skormin V. A., Gorodetski V. I., PopYack I. J., Data mining technology for failure of prognostic of avionics, IEEE Transactions on Aerospace and Electronic Systems, 38(2), 388–403, 2002.
  • Chen W. C., Tseng S. S., Wang, C. Y., A novel manufacturing defect detection method using data mining approach, Lecture Notes in Artificial Intelligence, 3029, 77–86, 2004.
  • Batanov D., Nagarur N., Nitikhumkasem P., Expert-mm: a knowledge based system for maintenance management, Artificial Intelligence Engineering, 8, 283–291, 1993.
  • Çetin M., An application of data mining in a manufacturing industry, Yüksek Lisans tezi, Sakarya Üniversitesi, Fen Bilimleri Enstitüsü, Sakarya, 2009.
  • Tapkan P. Z., Özmen T., Determining the yarn quality by feature selection and classification in a yarn production facility, Pamukkale University Journal of Engineering Sciences, 24(4), 713-719, 2018.
  • Bilekdemir G., Manufacturing lead time estimation using data mining techniques, Yüksek Lisans Tezi, Dokuz Eylül Üniversitesi, Sosyal Bilimler Enstitüsü, İzmir, 2010.
  • Türkoğlu B., Komesli M., Ünlütürk M. S., An industrial case study on data mining, International management information systems conference, Ankara-Türkiye, 107-110, 26-28 Ekim 2018.
  • Turker A. K., GÖLEÇ A., Aktepe A., Ersoz S., Ipek M., Cagil G., A real-time system design using data mining for estimation of delayed orders an application, Journal of the Faculty of Engineering and Architecture of Gazı University, 35(2), 709-724, 2020.
  • Chen S., Li X., Liu R., Zeng S., Extension data mining method for improving product manufacturing quality, Procedia Computer Science, 162, 146-155, 2019.
  • Nkonyana T., Sun Y., Twala B., Dogo E., Performance evaluation of data mining techniques in steel manufacturing industry, Procedia Manufacturing, 35, 623–628, 2019.
  • Cheng Y., Chen K., Sun H., Zhang Y., Tao F., Data and knowledge mining with big data towards smart production, Journal of Industrial Information Integration, 9, 1-13, 2018.
  • Emre İ. E., Selçukhan Erol Ç., Statistics or data mining for data analysis, International Journal of Informatics Technologies, 10(2), 161-167, 2017.
  • Gürbüz F., Özbakır L., Yapıcı H., Data mining application on component reports of an airline company in turkey, Journal of the Faculty of Engineering and Architecture of Gazi University, 24(1), 73-78, 2009.
  • Koyuncugil A., Özgülbaş N., Data mining: using and applications in medicine and healthcare, International Journal of Informatics Technologies, 2(2), 2009.
  • Fayyad U., Piatetsky-Shapiro G., Smyth P., From data mining to knowledge discovery in databases, AI Magazine, 17(3), 37-37, 1996.
  • Çalış A., Kayapınar S., Çetinyokuş T., An application on computer and internet security with decision tree algorithms in data mining, Journal of Industrial Engineering, 253, 2-19, 2014.
  • Kaya H., Köymen K., Data mining concept and application areas, Eastern Anatolia Region Researches, 159-164, 2008.
  • Han J., M. Kamber, Data mining: concepts and techniques, Morgan kaufmann Publishers, Cilt: 3, USA, 2001.
  • Budak H., Feature selection methods and a new approach, Süleyman Demirel University Journal of Natural and Applied Sciences, 221, 21-31, 2018.
  • Şeker S. E., http://bilgisayarkavramlari.sadievrenseker.com/2012/11/13/information-gain-bilgi-kazanimi/, Yayın tarihi Kasım 13 2012, Erişim tarihi Nisan 3, 2020.
  • Karegowdal A. G., Manjunath A. S., Jayaram M. A., Comparative study of attribute selection using gain ratio and correlation based feature selection, International Journal of Information Technology and Knowledge Management, 2(2), 271-277, July-December 2010.
  • Hall M., https://Weka.sourceforge.io/doc.dev/Weka/attributeSelection/ClassifierAttributeEval.html, Erişim tarihi Nisan 3, 2020.
  • Miles J., Banyard P., Understanding and using statistics in psychology: a practical introduction, Sage, 2007.
  • Brownlee J., http://spssistatistik.net/spss-korelasyon-analizi/, Yayın tarihi Temmuz 13 2016, Erişim tarihi Nisan 3, 2020.
  • Max B., Principles of Data Mining, Cilt 1, Springer, London-UK, 2001.
  • Nisbet R., Elder J., Miner G., Handbook of statistical analysis and data mining applications, Elsevier, Burlington, 2009.
  • Yang D. P., Jin-Lin L., Ran L., Zhou C., Applications of data mining methods in the evaluation of client credibility, Applications of Data Mining in E-Business and Finance, Amsterdam, 35-43, 2008.
  • Frank E, Witten I.H., Generating accurate rule sets without global optimization, 15th International Conference on Machine Learning, Wisconsin-USA, 24-27 July 1998.
  • Holte R., Very simple classification rules perform well on most commonly used datasets, Machine Learning, 11, 63-91, 1993.
  • Novakovic J., Strbac P., Bulatovic D., Toward optimal feature selection using ranking methods and classification algorithms, Yugoslav Journal of Operations Research, 211, 119-135, 2011.
  • Kalmegh S., Comparative analysis of the Weka classifiers rules conjunctive rule and decision table on indian news dataset by using different test mode, International Journal of Engineering Science Invention, 73, 1-9, 2018.
  • Shahzad W., Asad S., Khan M. A., International Journal of Physical Sciences 818, 885-896, 2019.
  • Kalmegh S., Analysis of weka data mining algorithm reptree, Simple Cart and RandomTree for Classification of Indian News, International Journal of Innovative Science, Engineering & Technology, 22, 2015.
  • Landwehr N., Hall M., Frank E., Logistic model trees, Machine Learning, 591(2), 161–205, 2005.
  • Provost F., Domingos P., Tree induction for probability based ranking, Machine Learning, 52(3), 199–215, 2003.
  • Nizam H., Akın S.S., Sosyal medyada makine öğrenmesi ile duygu analizinde dengeli ve dengesiz veri setlerinin performanslarının karşılaştırılması, Türkiye'de İnternet Konferansı, İzmir-Türkiye, 2014.
  • Hossin M., Sulaiman M.N., A review on evaluation metrics for data classification evaluations, International Journal of Data Mining & Knowledge Management Process, 5(2), 1-11, 2015.
  • Kumar G., Evaluation metrics for intrusion detection systems-a study, International Journal of Computer Science and Mobile Applications, 2(11), 11-17, 2014.
  • Ferri C., Hernández-Orallo, J., Modroiu R., An experimental comparison of performance measures for classification, Pattern Recognition Letters, 30, 27-38, 2009.
  • Handelman G.S., Kuan Kok H., Chandra R.V., Razavi A.H., Huang S., Brooks M., Lee M.J., Asadi H., Evaluation metrics of machine learning methods, American Journal of Roentgenology, 212, 38-43, 2019.
  • Sebastiani F., Machine learning inautomated text categorization, ACM Computing Surveys CSUR, 341, 1-47, 2002.
  • Göktepe A.B., Agar E., Lav A.H., Comparison of multilayer perceptron and adaptive neuro-fuzzy system on backcalculating the mechanical properties of flexible pavements, ARI The Bulletin of the Istanbul Technical University, 543, 65-77, 2004.
  • Çavuşoğlu Ü., Kaçar S., The performance analysis of data mining algorithms for anomaly detection, Academic Platform Journal of Engineering and Science, 72, 205-216, 2019.
  • Bilgin M., Performance analysis of classical machine learning methods in real data sets, Breast, 29, 683-688, 2017.
  • Kumar R., Indrayan A., Receiver operating characteristics roc curve for medical researchers, Indian Pediatrics, 48, 277-287,2011.
  • Gnanambal S., Thangaraj M., Meenatchi V.T., Gayathri V., Classification algorithms with attribute selection: an evaluation study using Weka, Intelligent Journal Advanced Networking and Applications, 9(6), 3640-3644, 2018.
  • Ang J., C., Mirzal A., Haron H., Nuzly H., Hamed A., Supervised unsupervised and semi-supervised feature selection: a review on gene selection, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol 13(5), 971-989, 2016.
Toplam 57 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Kübra Işık 0000-0003-3491-4233

Selda Kapan Ulusoy 0000-0001-5604-0448

Yayımlanma Tarihi 2 Eylül 2021
Gönderilme Tarihi 13 Mayıs 2020
Kabul Tarihi 21 Mart 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 36 Sayı: 4

Kaynak Göster

APA Işık, K., & Kapan Ulusoy, S. (2021). Metal Sektöründe üretim sürelerine etki eden faktörlerin veri madenciliği yöntemleriyle tespit edilmesi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 36(4), 1949-1962. https://doi.org/10.17341/gazimmfd.736659
AMA Işık K, Kapan Ulusoy S. Metal Sektöründe üretim sürelerine etki eden faktörlerin veri madenciliği yöntemleriyle tespit edilmesi. GUMMFD. Eylül 2021;36(4):1949-1962. doi:10.17341/gazimmfd.736659
Chicago Işık, Kübra, ve Selda Kapan Ulusoy. “Metal Sektöründe üretim sürelerine Etki Eden faktörlerin Veri madenciliği yöntemleriyle Tespit Edilmesi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 36, sy. 4 (Eylül 2021): 1949-62. https://doi.org/10.17341/gazimmfd.736659.
EndNote Işık K, Kapan Ulusoy S (01 Eylül 2021) Metal Sektöründe üretim sürelerine etki eden faktörlerin veri madenciliği yöntemleriyle tespit edilmesi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 36 4 1949–1962.
IEEE K. Işık ve S. Kapan Ulusoy, “Metal Sektöründe üretim sürelerine etki eden faktörlerin veri madenciliği yöntemleriyle tespit edilmesi”, GUMMFD, c. 36, sy. 4, ss. 1949–1962, 2021, doi: 10.17341/gazimmfd.736659.
ISNAD Işık, Kübra - Kapan Ulusoy, Selda. “Metal Sektöründe üretim sürelerine Etki Eden faktörlerin Veri madenciliği yöntemleriyle Tespit Edilmesi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 36/4 (Eylül 2021), 1949-1962. https://doi.org/10.17341/gazimmfd.736659.
JAMA Işık K, Kapan Ulusoy S. Metal Sektöründe üretim sürelerine etki eden faktörlerin veri madenciliği yöntemleriyle tespit edilmesi. GUMMFD. 2021;36:1949–1962.
MLA Işık, Kübra ve Selda Kapan Ulusoy. “Metal Sektöründe üretim sürelerine Etki Eden faktörlerin Veri madenciliği yöntemleriyle Tespit Edilmesi”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, c. 36, sy. 4, 2021, ss. 1949-62, doi:10.17341/gazimmfd.736659.
Vancouver Işık K, Kapan Ulusoy S. Metal Sektöründe üretim sürelerine etki eden faktörlerin veri madenciliği yöntemleriyle tespit edilmesi. GUMMFD. 2021;36(4):1949-62.