Araştırma Makalesi
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LABOR PLANNING AND ARTIFICIAL VISION SYSTEMS IN QUALITY MEASUREMENT

Yıl 2023, Cilt: 9 Sayı: 14, 29 - 42, 29.08.2023
https://doi.org/10.51947/yonbil.1341749

Öz

Today, computers, whose power is rapidly increasing and becoming cheaper in accordance with Moore's law, have paved the way for the widespread use of machine vision systems. However, the fact that machine vision systems require other components such as software, cameras, lighting systems, and the integration of these components and training studies make the subject a serious investment. Undoubtedly, the fact that machine vision systems are a very important tool that reduces the costs of poor quality in industry and internal logistics applications encourages investments. However, it is also indispensable that they comply with the principle of economy. Therefore, care should be taken to ensure that these systems provide returns in excess of their costs for the duration of their use. In this study, we introduce a model to predict the return on investment of machine vision systems that are intended to be used for quality improvements. Research conducted in Turkey and discussions with EMVA (European Machine Vision Association) have revealed that due to the lack of a similar model, some unprofitable investments are being made and some potentially profitable investment opportunities are being missed. The model takes as data the costs of poor quality, the estimated cost of the machine vision system, the amount of savings that can be achieved through the machine vision system in poor quality costs and develops a proposal in terms of labor costs.

Kaynakça

  • Acar, D. (2005). Küresel Rekabette Maliyet Yönetimi ve Yaklaşımları: Tekstil Sektörü İle İlgili Bir Araştırma, 1. Baskı, Asil Yayın Dağıtım, Isparta
  • Ahmad, S., Saleh, M., & Al-Ahmari, A.. (2022). The Impact of Industry 4.0 Technologies on Manufacturing Strategies: Proposition of Technology-Integrated Selection. Ieee Access, 10, 21574-21583. https://doi.org/10.1109/access.2022.3151898
  • Akgün, M. (2005). Kalite Maliyetlerinin Faaliyet Tabanlı Maliyetleme Sistemine Entegrasyonu. Muhasebe Ve Denetime Bakış, Yıl:4 Sayı:14, ss:31-47.
  • Alkan, H. (2002). Kalitesizliğin Önemli Bir Boyutu: Maliyet Artışı, Süleyman Demirel Üniversitesi Orman Fakültesi Dergisi, Seri: A, Sayı:2
  • Al-Shawi, H. F. J.. (2021). Solving the Problematic Relationship between Irregular Marketing Behaviours and the Principles of Social Responsibility. https://scite.ai/reports/10.11114/bms.v7i3.5331
  • Arroyo, E., Lima, J. ve Leitao, P. (2013). Adaptive Image Pre-processing for Quality Control in Production Lines. International Conference on Industrial Technology (ICIT) 2013; 2013; Cape Town: IEEE.
  • Bozkurt, R. (2003). Kalite Maliyetleri, Milli Prodüktivite Merkezi Yayınları, No: 641, Ankara
  • Dale, B. G. ve Plunkett, J. J. (1991), Quality Costing, Chapman & Hall, London
  • Davies, E. R., (1998). Automated Visual Inspection. Machine Vision, 2nd ed. Academic Press. 19.
  • Gornand, W. (1998), Combining Prevention and Appraisal Efforts to Minimize The Total Quality Costs, Journal of Cost Management, 12, 1, 20-32.
  • Harrington, H.J. (1987). Poor-Quality Cost (1st ed.). CRC Press. https://doi.org/10.1201/9780429259128 Heleno, P., Davies R., Correia B.A.B. ve Dinis J. (2002). A Machine Vision Quality Control System for Industrial Acrylic Fibre Production.
  • Jaffar, R. N., Hussain, A. A. A. M., & Chiad, W.. (2019). A new model for study of quality attributes to components based development approach. Periodicals of Engineering and Natural Sciences (Pen), 7(3), 1177. https://doi.org/10.21533/pen.v7i3.686
  • Judi, H., Jenal, R. ve Genasan, D. (2009). Some Experiences of Quality Control Implementation in Malaysian companies. European Journal of Scientific Research, 27(1), 34-45. Kopardekar, P., Mital A., ve Anand S. (1993). Manual, Hybrid and Automated Inspection Literature and Current Research. Integrated Manufacturing Systems, 4. 18-29.
  • Labudzki, R., ve Legutko, S. (2010). Applications of Machine Vision.27.
  • Lei, L.. (2022). Observation on the Effect of Intelligent Machine-Assisted Surgery and Perioperative Nursing. https://scite.ai/reports/10.1155/2022/6264441
  • Liu, H., Zhang, C., & Huang, D.. (2017). Extreme Learning Machine and Moving Least Square Regression Based Solar Panel Vision Inspection. https://scite.ai/reports/10.1155/2017/7406568
  • Malamas, E. N., Petrakis E. G., Zervakis M., Petit L. ve Legat J. D. (2003). A Survey on Industrial Vision Systems, Applications and Tools.
  • Madyaningarum, N., Berawi, M. A., & Miraj, P.. (2018). Relationship Between Leadership and Commitment with Quality Performance on U-Th-REE Processing Pilot Plant Construction in BATAN. https://scite.ai/reports/10.17146/eksplorium.2018.39.1.4161
  • Nagrale, S.K., ve Bagde S.T. (2013). Application of Image Processing For Development of Automated Inspection System. International Journal Of Computational Engineering Research. 3- 3.
  • Owusu, P. K., & Goh, M. A.. (2020). Assessment of Cost of Quality and its Effects on Manufacturing Performance: A Case Study of Special Ice Company Limited, Ghana. Asian Journal of Basic Science & Research, 02(03), 01-22. https://doi.org/10.38177/ajbsr.2020.2301
  • Prabuwono, A. S., Sulaiman, R.B., Hamdan, A. ve Hasniaty, A. (2006). Development of Intelligent Visual Inspection System (IVIS) for Bottling Machine. TENCON 2006 - 2006 IEEE Region 10 Conference, 1-4.
  • Roden S., ve Dale B. G. (2000). Undersanding The Language of Quality Costing. The TQM Magazine, 179-185.
  • Sabet, P. G. P., & Chong, H.. (2020). Pathways for the Improvement of Construction Productivity: A Perspective on the Adoption of Advanced Techniques. Advances in Civil Engineering, 2020, 1-17. https://doi.org/10.1155/2020/5170759
  • Shukla P. K. ve Jayswal C. C. (2013). Design & Development of an Image Processing Algorithm for Automated Visual Inspection System
  • Vasilev, Momchil et al. (2021, July 27). Sensor-Enabled Multi-Robot System for Automated Welding and In-Process Ultrasonic NDE. Sensors, 21(15), 5077. https://doi.org/10.3390/s21155077
  • Yaman, K., Sarucan, A., Atak, M ve Aktürk, N. (2001). Dinamik Çizelgeleme İçin Görüntü İşleme ve Arıma Modelleri Yardımıyla Veri Hazırlama, Gazi Üniv. Müh. Mim. Fak. Der., Cilt 16, No 1, 19-40,
  • Wang, W., & Li, H.. (2021). Deep Learning in Product Manufacturing Record System. https://scite.ai/reports/10.21307/ijanmc-2021-028.
  • Woo, T. M., ve Law, H.W. (2002). Modeling of a Quality Control Information System For Small to Medium-sized Enterprise. Integrated Manufacturing Systems, 13(4). 222-236.
  • Yıldırım, H. ve Saylık B. (2009). Kalitesizlik Maliyetleri Üzerine Bir İnceleme. Marmara Üniversitesi İ.İ.B.F. Dergisi, 16.
  • Yang, R., Yonglin Zhang, Zhenrong Deng, Wenming Huang, Rushi Lan, Xiaonan Luo. (2020). "SK-FMYOLOV3: A Novel Detection Method for Urine Test Strips", Wireless Communications and Mobile Computing, vol. 2020, Article ID 8847651, 14 pages. https://doi.org/10.1155/2020/8847651 Yang Zhang, Wei Liu, Zhiguang Lan, Zhiyuan Zhang, Fan Ye, Haiyang Zhao, Xiaodong Li, Zhenyuan Jia, "Global Measurement Method for Large-Scale Components Based on a Multiple Field of View Combination", Journal of Sensors, vol. 2017, Article ID 8765450, 12 pages, 2017. https://doi.org/10.1155/2017/8765450
  • Yuan-yuan, Si-yang ve Qing-chang. (2012). Application of Detecting Part’s Size Online Based on Machine Vision. International Conference on Future Energy, Environment, and Materials. Energy Procedia 16. 1948-1956.
  • Zhong, Y., Ling, J., & Shi, L.. (2022, October 10). Inspection Technology of Power Communication Network Based on Machine Vision Graphic Recognition. https://scite.ai/reports/10.1155/2022/1380679

KALİTE ÖLÇÜMÜNDE İŞGÜCÜ PLANLAMASI VE YAPAY GÖRME SİSTEMLERİ

Yıl 2023, Cilt: 9 Sayı: 14, 29 - 42, 29.08.2023
https://doi.org/10.51947/yonbil.1341749

Öz

Günümüzde Moore yasasına uygun şekilde gücü hızla artıp ucuzlayan bilgisayarlar yapay görme sistemlerinin yaygınlaşmasının önünü açmıştır. Bununla beraber yapay görme sistemlerinin yazılımlar, kameralar, ışık sistemleri gibi başka bileşenler gerektirmesi ve bu bileşenlerin entegrasyonu ile eğitim çalışmaları konuyu ciddi bir yatırım konumuna getirmektedir. Kuşkusuz yapay görme sistemlerinin endüstride ve iç lojistik uygulamalarda kalitesizlik maliyetlerini azaltan çok önemli bir araç olması yatırımları teşvik etmektedir. Ancak ekonomiklik ilkesine uyması da vazgeçilemez koşuldur. Dolayısı ile bu sistemlerin kullanıldıkları süre boyunca götürülerinin üstünde getiri sağlamalarına dikkat edilmelidir. Bu çalışmada kalite iyileştirmeleri sağlamak amaçlı kullanılması düşünülen yapay görme sistemlerinin yatırım karlılığının anlaşılmasını kestirecek bir model tanıtılmaktadır. Ülkemiz genelinde yürüttüğümüz araştırmalar ile EMVA (European Machine Vision Association) ile yaptığımız görüşmeler çerçevesinde temin edilen veriler benzer bir modelin bulunmaması nedeniyle kimi karsız yatırımların yapılmakta olduğu, kimi karlı olabilecek yatırım fırsatlarının kaçırıldığı gerçeklerini ortaya çıkartmıştır. Model, kalitesizlik maliyetlerini, yapay görme sisteminin tahmini bedelini, kalitesizlik maliyetlerinde yapay görme sistemi sayesinde sağlanabilecek tasarruf tutarını veri olarak almakta ve işgücü maliyetleri açısından da bir öneri geliştirmektedir.

Kaynakça

  • Acar, D. (2005). Küresel Rekabette Maliyet Yönetimi ve Yaklaşımları: Tekstil Sektörü İle İlgili Bir Araştırma, 1. Baskı, Asil Yayın Dağıtım, Isparta
  • Ahmad, S., Saleh, M., & Al-Ahmari, A.. (2022). The Impact of Industry 4.0 Technologies on Manufacturing Strategies: Proposition of Technology-Integrated Selection. Ieee Access, 10, 21574-21583. https://doi.org/10.1109/access.2022.3151898
  • Akgün, M. (2005). Kalite Maliyetlerinin Faaliyet Tabanlı Maliyetleme Sistemine Entegrasyonu. Muhasebe Ve Denetime Bakış, Yıl:4 Sayı:14, ss:31-47.
  • Alkan, H. (2002). Kalitesizliğin Önemli Bir Boyutu: Maliyet Artışı, Süleyman Demirel Üniversitesi Orman Fakültesi Dergisi, Seri: A, Sayı:2
  • Al-Shawi, H. F. J.. (2021). Solving the Problematic Relationship between Irregular Marketing Behaviours and the Principles of Social Responsibility. https://scite.ai/reports/10.11114/bms.v7i3.5331
  • Arroyo, E., Lima, J. ve Leitao, P. (2013). Adaptive Image Pre-processing for Quality Control in Production Lines. International Conference on Industrial Technology (ICIT) 2013; 2013; Cape Town: IEEE.
  • Bozkurt, R. (2003). Kalite Maliyetleri, Milli Prodüktivite Merkezi Yayınları, No: 641, Ankara
  • Dale, B. G. ve Plunkett, J. J. (1991), Quality Costing, Chapman & Hall, London
  • Davies, E. R., (1998). Automated Visual Inspection. Machine Vision, 2nd ed. Academic Press. 19.
  • Gornand, W. (1998), Combining Prevention and Appraisal Efforts to Minimize The Total Quality Costs, Journal of Cost Management, 12, 1, 20-32.
  • Harrington, H.J. (1987). Poor-Quality Cost (1st ed.). CRC Press. https://doi.org/10.1201/9780429259128 Heleno, P., Davies R., Correia B.A.B. ve Dinis J. (2002). A Machine Vision Quality Control System for Industrial Acrylic Fibre Production.
  • Jaffar, R. N., Hussain, A. A. A. M., & Chiad, W.. (2019). A new model for study of quality attributes to components based development approach. Periodicals of Engineering and Natural Sciences (Pen), 7(3), 1177. https://doi.org/10.21533/pen.v7i3.686
  • Judi, H., Jenal, R. ve Genasan, D. (2009). Some Experiences of Quality Control Implementation in Malaysian companies. European Journal of Scientific Research, 27(1), 34-45. Kopardekar, P., Mital A., ve Anand S. (1993). Manual, Hybrid and Automated Inspection Literature and Current Research. Integrated Manufacturing Systems, 4. 18-29.
  • Labudzki, R., ve Legutko, S. (2010). Applications of Machine Vision.27.
  • Lei, L.. (2022). Observation on the Effect of Intelligent Machine-Assisted Surgery and Perioperative Nursing. https://scite.ai/reports/10.1155/2022/6264441
  • Liu, H., Zhang, C., & Huang, D.. (2017). Extreme Learning Machine and Moving Least Square Regression Based Solar Panel Vision Inspection. https://scite.ai/reports/10.1155/2017/7406568
  • Malamas, E. N., Petrakis E. G., Zervakis M., Petit L. ve Legat J. D. (2003). A Survey on Industrial Vision Systems, Applications and Tools.
  • Madyaningarum, N., Berawi, M. A., & Miraj, P.. (2018). Relationship Between Leadership and Commitment with Quality Performance on U-Th-REE Processing Pilot Plant Construction in BATAN. https://scite.ai/reports/10.17146/eksplorium.2018.39.1.4161
  • Nagrale, S.K., ve Bagde S.T. (2013). Application of Image Processing For Development of Automated Inspection System. International Journal Of Computational Engineering Research. 3- 3.
  • Owusu, P. K., & Goh, M. A.. (2020). Assessment of Cost of Quality and its Effects on Manufacturing Performance: A Case Study of Special Ice Company Limited, Ghana. Asian Journal of Basic Science & Research, 02(03), 01-22. https://doi.org/10.38177/ajbsr.2020.2301
  • Prabuwono, A. S., Sulaiman, R.B., Hamdan, A. ve Hasniaty, A. (2006). Development of Intelligent Visual Inspection System (IVIS) for Bottling Machine. TENCON 2006 - 2006 IEEE Region 10 Conference, 1-4.
  • Roden S., ve Dale B. G. (2000). Undersanding The Language of Quality Costing. The TQM Magazine, 179-185.
  • Sabet, P. G. P., & Chong, H.. (2020). Pathways for the Improvement of Construction Productivity: A Perspective on the Adoption of Advanced Techniques. Advances in Civil Engineering, 2020, 1-17. https://doi.org/10.1155/2020/5170759
  • Shukla P. K. ve Jayswal C. C. (2013). Design & Development of an Image Processing Algorithm for Automated Visual Inspection System
  • Vasilev, Momchil et al. (2021, July 27). Sensor-Enabled Multi-Robot System for Automated Welding and In-Process Ultrasonic NDE. Sensors, 21(15), 5077. https://doi.org/10.3390/s21155077
  • Yaman, K., Sarucan, A., Atak, M ve Aktürk, N. (2001). Dinamik Çizelgeleme İçin Görüntü İşleme ve Arıma Modelleri Yardımıyla Veri Hazırlama, Gazi Üniv. Müh. Mim. Fak. Der., Cilt 16, No 1, 19-40,
  • Wang, W., & Li, H.. (2021). Deep Learning in Product Manufacturing Record System. https://scite.ai/reports/10.21307/ijanmc-2021-028.
  • Woo, T. M., ve Law, H.W. (2002). Modeling of a Quality Control Information System For Small to Medium-sized Enterprise. Integrated Manufacturing Systems, 13(4). 222-236.
  • Yıldırım, H. ve Saylık B. (2009). Kalitesizlik Maliyetleri Üzerine Bir İnceleme. Marmara Üniversitesi İ.İ.B.F. Dergisi, 16.
  • Yang, R., Yonglin Zhang, Zhenrong Deng, Wenming Huang, Rushi Lan, Xiaonan Luo. (2020). "SK-FMYOLOV3: A Novel Detection Method for Urine Test Strips", Wireless Communications and Mobile Computing, vol. 2020, Article ID 8847651, 14 pages. https://doi.org/10.1155/2020/8847651 Yang Zhang, Wei Liu, Zhiguang Lan, Zhiyuan Zhang, Fan Ye, Haiyang Zhao, Xiaodong Li, Zhenyuan Jia, "Global Measurement Method for Large-Scale Components Based on a Multiple Field of View Combination", Journal of Sensors, vol. 2017, Article ID 8765450, 12 pages, 2017. https://doi.org/10.1155/2017/8765450
  • Yuan-yuan, Si-yang ve Qing-chang. (2012). Application of Detecting Part’s Size Online Based on Machine Vision. International Conference on Future Energy, Environment, and Materials. Energy Procedia 16. 1948-1956.
  • Zhong, Y., Ling, J., & Shi, L.. (2022, October 10). Inspection Technology of Power Communication Network Based on Machine Vision Graphic Recognition. https://scite.ai/reports/10.1155/2022/1380679
Toplam 32 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İnsan Kaynakları ve Endüstriyel İlişkiler (Diğer)
Bölüm Makaleler
Yazarlar

Ali Özcan 0000-0003-3751-8148

Halil Halefşan Sümen 0000-0001-9627-2681

Ahmet Erkasap

Yayımlanma Tarihi 29 Ağustos 2023
Kabul Tarihi 28 Ağustos 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 9 Sayı: 14

Kaynak Göster

APA Özcan, A., Sümen, H. H., & Erkasap, A. (2023). KALİTE ÖLÇÜMÜNDE İŞGÜCÜ PLANLAMASI VE YAPAY GÖRME SİSTEMLERİ. Uluslararası Akademik Yönetim Bilimleri Dergisi, 9(14), 29-42. https://doi.org/10.51947/yonbil.1341749
Uluslararası Akademik Yönetim Bilimleri Dergisi
ISSN: 2149-1984
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