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MODELING OF IOT-BASED ADDITIVE MANUFACTURING MACHINE’S DIGITAL TWIN FOR ERROR DETECTION

Yıl 2023, Cilt: 11 Sayı: 2, 486 - 497, 28.06.2023

Burhan Duman Ahmet Ali Süzen

https://doi.org/10.21923/jesd.1251972

Öz

Additive Manufacturing technology is one of the technologies that is changing the manufacturing industry. It has revealed some advantages over traditional manufacturing methods with this technology. With the advancement of information technologies, new approaches focusing on cost and improvement have begun to be adopted in the manufacturing industry. One such method is digital twin technology. A digital twin is frequently referred to as a digital replication of a physical system. Digital twins provide data and models to support the operation of design and manufacturing processes, as well as troubleshooting, diagnostics, and problem-solving. Various sensors are required to monitor the status of physical systems and transfer data to digital systems. Some of these Internet of Things-compatible sensors are already in production machines, but others can be added later. In the study, an Internet of Things-based system was proposed for the creation of digital twins using a virtual environment, and a digital twin simulation was created in order to bring the benefits of digitalization to production systems. The digital twin is modeled in the Matlab Simulink environment to perform binary classification to detect abnormal physical conditions that have the potential to disrupt the operation of the additive manufacturing machine and affect the quality of the manufacturing part. By generating a digital twin from real machine data, the proposed system will be able to detect errors.

Anahtar Kelimeler

Digital Twin Internet of Things Modeling Additive Manufacturing

Kaynakça

  • Anonim. (2021). Nesnelerin internetinde dijital ikizlerin yükselişi. https://www.endustri40.com/nesnelerin-internetinde-dijital-ikizlerin-yukselisi/
  • Asghari, P., Rahmani, A. M., & Javadi, H. H. S. (2019). Internet of Things applications: A systematic review. Computer Networks, 148, 241–261.
  • Ashton, K. (2009). That ‘internet of things’ thing. RFID Journal, 22(7), 97–114.
  • ASTM. (2020). Standard Terminology for Additive Manufacturing Technologies,. Retrieved September 3, 2020, from https://www.astm.org/f2792-12.html
  • Aynacı, İ. (2020). Dijital İkiz Ve Sağlık Uygulamaları. İzmir Kâtip Çelebi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 3(1), 70–79.
  • Babayiğit, B., & Büyükpatpat, B. (2019). Design and implementation of IoT-based irrigation system. 2019 4th International Conference on Computer Science and Engineering (UBMK), 38–41.
  • Bibow, P., Dalibor, M., Hopmann, C., Mainz, B., Rumpe, B., Schmalzing, D., Schmitz, M., & Wortmann, A. (2020). Model-driven development of a digital twin for injection molding. Advanced Information Systems Engineering: 32nd International Conference, CAiSE 2020, Grenoble, France, June 8–12, 2020, Proceedings, 85–100.
  • Burhan, M., Rehman, R. A., Khan, B., & Kim, B.-S. (2018). IoT elements, layered architectures, and security issues: A comprehensive survey. Sensors, 18(9), 2796.
  • Çavdar, T., & Öztürk, E. (2018). A novel architecture design for the internet of things. Sakarya University Journal of Science, 22(1), 39–48.
  • Çelebi, A. (2019). Investigation of fused deposition modeling processing parameters of 3D PLA specimens by an experimental design methodology. Materials Testing, 61(5), 405–410.
  • Chen, H. (2016). A process modelling and parameters optimization and recommendation system for binder jetting additive manufacturing process. McGill University (Canada).
  • Chhetri, S. R., Faezi, S., Canedo, A., & Faruque, M. A. Al. (2019). QUILT: Quality inference from living digital twins in IoT-enabled manufacturing systems. Proceedings of the International Conference on Internet of Things Design and Implementation, 237–248.
  • Cruz, M., Parés, C., & Quintela, P. (2021). Progress in Industrial Mathematics: Success Stories: The Industry and the Academia Points of View. Springer.
  • Corradini, F., & Silvestri, M. (2022). Design and testing of a digital twin for monitoring and quality assessment of material extrusion process. Additive Manufacturing, 51, 102633.
  • Di Angelo, L., Di Stefano, P., Dolatnezhadsomarin, A., Guardiani, E., & Khorram, E. (2020). A reliable build orientation optimization method in additive manufacturing: The application to FDM technology. The International Journal of Advanced Manufacturing Technology, 108, 263–276.
  • Duman, B., & Özsoy, K. (2019). Endüstri 4.0 perspektifinde akıllı tarım. 4th International Congress on 3d Printing (Additive Manufacturing) Technologies and Digital Industry, 540–555.
  • Duman, B., & Kayacan, M. C. (2016). Seçmeli Lazer Sinterleme Tezgâhı İçin İmalat Yazılımı Geliştirilmesi. Uluslararası Teknolojik Bilimler Dergisi, 8(3), 27–45.
  • Entes. (2021). Dijital İkiz (Digital Twin) Nedir? Endüstri 4.0 ve Dijital İkizlerin Önemi. Retrieved September 1, 2021, from https://www.entes.com.tr/dijital-ikiz-digital-twin-nedir-endustri-4-0-ve-dijital-ikizlerin-onemi/
  • Fernandes, E. (2020). Internet of Things (IoT) Market Size And Forecast. Retrieved July 14, 2020, from https://www.verifiedmarketresearch.com/product/global-internet-of-things-iot-market-size-and-forecast-to-2026
  • GE Company. (2021). GE Digital Twin: Analytic engine for the digital power plant. https://www.ge.com/digital/sites/default/files/download_assets/Digital-Twin-for-the-digital-power-plant-.pdf
  • Glaessgen, E., & Stargel, D. (2012). The digital twin paradigm for future NASA and US Air Force vehicles. 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA, 1818.
  • Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches, 85–113.
  • He, R., Chen, G., Dong, C., Sun, S., & Shen, X. (2019). Data-driven digital twin technology for optimized control in process systems. ISA Transactions, 95, 221–234.
  • Huang, H., & Baddour, N. (2018). Bearing vibration data collected under time-varying rotational speed conditions. Data in Brief, 21, 1745–1749.
  • Ioturkiye. (2020). IoT ve Bulut Bilişim, Verilerin Geleceği Mi? Retrieved April 2, 2020, from https://ioturkiye.com/2020/04/iot-ve-bulut-bilisim-verilerin-gelecegi-mi
  • Karakılınç, U., Yalçın, B., & Ergene, B. (2019). Toz Yataklı/Beslemeli Eklemeli İmalatta Kullanılan Partiküllerin Uygunluk Araştırması ve Partikül İmalat Yöntemleri. Politeknik Dergisi. 22(4), 801-810.
  • Li, Z., Wang, Y., & Wang, K.-S. (2017). Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario. Advances in Manufacturing, 5, 377–387.
  • Liu, X., Kan, C., & Ye, Z. (2022). Real-time multiscale prediction of structural performance in material extrusion additive manufacturing. Additive Manufacturing, 49, 102503.
  • Mathworks. (2021). Mathworks. https://www.mathworks.com/
  • Medvedofsky, D., Mor-Avi, V., Amzulescu, M., Fernandez-Golfin, C., Hinojar, R., Monaghan, M. J., Otani, K., Reiken, J., Takeuchi, M., & Tsang, W. (2018). Three-dimensional echocardiographic quantification of the left-heart chambers using an automated adaptive analytics algorithm: multicentre validation study. European Heart Journal-Cardiovascular Imaging, 19(1), 47–58.
  • Mehmood, F. (2021). BME280-Sensor-Data. Retrieved September 2, 2021, from https://www.kaggle.com/faisalawan/bme280sensordata
  • Miljanovic, D., Seyedmahmoudian, M., Stojcevski, A., & Horan, B. (2020). Design and fabrication of implants for mandibular and craniofacial defects using different medical-additive manufacturing technologies: a review. Annals of Biomedical Engineering, 48, 2285–2300.
  • Mohammed, A., Elshaer, A., Sareh, P., Elsayed, M., & Hassanin, H. (2020). Additive manufacturing technologies for drug delivery applications. International Journal of Pharmaceutics, 580, 119245.
  • Osho, J., Hyre, A., Pantelidakis, M., Ledford, A., Harris, G., Liu, J., & Mykoniatis, K. (2022). Four Rs Framework for the development of a digital twin: The implementation of Representation with a FDM manufacturing machine. Journal of Manufacturing Systems, 63, 370-380.
  • Pamuk, N. S., & Soysal, M. (2018). Yeni sanayi devrimi endüstri 4.0 üzerine bir inceleme. Verimlilik Dergisi, 1, 41–66.
  • Qin, J., Liu, Y., & Grosvenor, R. (2017). A framework of energy consumption modelling for additive manufacturing using internet of things. Procedia CIRP, 63, 307–312.
  • Rao, P. K., Liu, J., Roberson, D., Kong, Z., & Williams, C. (2015). Online real-time quality monitoring in additive manufacturing processes using heterogeneous sensors. Journal of Manufacturing Science and Engineering, 137(6), 061007.
  • Rafiee, M., Farahani, R. D., & Therriault, D. (2020). Multi‐material 3D and 4D printing: a survey. Advanced Science, 7(12), 1902307.
  • Rengier, F., Mehndiratta, A., Von Tengg-Kobligk, H., Zechmann, C. M., Unterhinninghofen, R., Kauczor, H.-U., & Giesel, F. L. (2010). 3D printing based on imaging data: review of medical applications. International Journal of Computer Assisted Radiology and Surgery, 5, 335–341.
  • Siemens Healthineers. (2018). Exploring the possibilities offered by digital twins in medical technology. Retrieved April 24, 2018, from https://static.healthcare.siemens.com/siemens_hwemhwem_ssxa_websitescontextroot/wcm/idc/groups/public/@global/@press/documents/download/mda4/nzm4/~edisp/exploring-the-possibilities-offered-by-digital-twins-in-medical-technology-05899262.pdf
  • Shi, Z., Mamun, A. A., Kan, C., Tian, W., & Liu, C. (2022). An LSTM-autoencoder based online side channel monitoring approach for cyber-physical attack detection in additive manufacturing. Journal of Intelligent Manufacturing, 1-17.
  • Sparkmeasure. (2020). Nesnelerin İnterneti`nin Temelleri. Retrieved July 9, 2020, from https://www.sparkmeasure.com/b-136-nesnelerin-interneti%60nin-temel.html
  • Standardization, I. O. for. (2015). Additive Manufacturing: General: Principles: Terminology. ISO.
  • Statista. (2016). IoT number of connected devices worldwide. Retrieved November 27, 2016, from https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/#:~:text=The total installed base of,fivefold increase in ten years.
  • Türkay, B. (2018). Nesnelerin İnterneti (IoT) Uygulamalarının Günümüzdeki Yeri. Retrieved June 22, 2018, from https://medium.com/@Barturkay/nesnelerin-i̇nterneti-iot-uygulamalarının-günümüzdeki-yeri-736cd99e37d9
  • Ventura, K., Kabasakal, İ., Keskin, F. D., & Soyuer, H. (2019). Pazar ve Müşteri Yönlü IoT (Internet of Things-Nesnelerin İnterneti) Uygulamalarının İş Yazılımları Kapsamında Analizi. Yaşar Üniversitesi E-Dergisi, 14(56), 507–521.
  • Wiki. (2020). Nesnelerin İnterneti. https://tr.wikipedia.org/wiki/Nesnelerin_interneti
  • Yap, Y. L., Wang, C., Sing, S. L., Dikshit, V., Yeong, W. Y., & Wei, J. (2017). Material jetting additive manufacturing: An experimental study using designed metrological benchmarks. Precision Engineering, 50, 275–285.
  • Yin, J., Lu, C., Fu, J., Huang, Y., & Zheng, Y. (2018). Interfacial bonding during multi-material fused deposition modeling (FDM) process due to inter-molecular diffusion. Materials & Design, 150, 104-112.

NESNELERIN İNTERNETI TABANLI EKLEMELI İMALAT MAKINESININ HATA TESPITINE YÖNELIK DIJITAL İKIZININ MODELLENMESI

Yıl 2023, Cilt: 11 Sayı: 2, 486 - 497, 28.06.2023

Burhan Duman Ahmet Ali Süzen

https://doi.org/10.21923/jesd.1251972

Öz

Eklemeli İmalat teknolojisi, imalat sanayine farklı bir yön veren teknolojilerdendir. Bu teknoloji ile geleneksel imalat yöntemlerine göre bazı avantajlar ortaya koymuştur. Bilişim teknolojilerinin imkanlarının artmasıyla birlikte imalat sanayinde iyileştirme ve maliyet odaklı yeni yaklaşımlar benimsenmeye başlanmıştır. Dijital ikiz teknolojisi de böyle bir yaklaşımdır. Dijital ikiz, genellikle fiziksel bir sistemin dijital kopyası olarak adlandırılır. Dijital ikizler, tasarım ve üretim süreçlerinin işleyişi, sorun giderme, teşhis ve problem çözme için bilgi ve modeller sağlar. Fiziksel sistemlerdeki durumların izlenerek dijital sistemlere veri aktarımı için çeşitli sensörlere ihtiyaç duyulmaktadır. Nesnelerin internetine uygun bu sensörlerden bazıları imalat makinelerinde olmakla birlikte bazıları da sonradan ilave edilebilmektedir. Çalışmada, dijitalleşmenin avantajlarını üretim sistemlerine kazandırmak amacıyla, sanal ortam kullanılarak dijital ikizin oluşturulması için Nesnelerin İnterneti tabanlı bir sistem önerilmiş ve dijital ikiz simülasyonu yapılmıştır. Dijital ikiz Matlab Simulink ortamında, eklemeli imalat makinesinin işleyişini aksatacak ve imalat parçasının kalitesini etkileyebilecek potansiyele sahip normal dışı fiziksel şartları tespit etmek için ikili sınıflandırma yapacak şekilde modellenmiştir. Önerilen sistem, gerçek makine verilerinden bir dijital ikiz oluşturarak hataları tespit edebilecektir.

Anahtar Kelimeler

Dijital İkiz Nesnelerin İnterneti Modelleme Eklemeli İmalat

Kaynakça

  • Anonim. (2021). Nesnelerin internetinde dijital ikizlerin yükselişi. https://www.endustri40.com/nesnelerin-internetinde-dijital-ikizlerin-yukselisi/
  • Asghari, P., Rahmani, A. M., & Javadi, H. H. S. (2019). Internet of Things applications: A systematic review. Computer Networks, 148, 241–261.
  • Ashton, K. (2009). That ‘internet of things’ thing. RFID Journal, 22(7), 97–114.
  • ASTM. (2020). Standard Terminology for Additive Manufacturing Technologies,. Retrieved September 3, 2020, from https://www.astm.org/f2792-12.html
  • Aynacı, İ. (2020). Dijital İkiz Ve Sağlık Uygulamaları. İzmir Kâtip Çelebi Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 3(1), 70–79.
  • Babayiğit, B., & Büyükpatpat, B. (2019). Design and implementation of IoT-based irrigation system. 2019 4th International Conference on Computer Science and Engineering (UBMK), 38–41.
  • Bibow, P., Dalibor, M., Hopmann, C., Mainz, B., Rumpe, B., Schmalzing, D., Schmitz, M., & Wortmann, A. (2020). Model-driven development of a digital twin for injection molding. Advanced Information Systems Engineering: 32nd International Conference, CAiSE 2020, Grenoble, France, June 8–12, 2020, Proceedings, 85–100.
  • Burhan, M., Rehman, R. A., Khan, B., & Kim, B.-S. (2018). IoT elements, layered architectures, and security issues: A comprehensive survey. Sensors, 18(9), 2796.
  • Çavdar, T., & Öztürk, E. (2018). A novel architecture design for the internet of things. Sakarya University Journal of Science, 22(1), 39–48.
  • Çelebi, A. (2019). Investigation of fused deposition modeling processing parameters of 3D PLA specimens by an experimental design methodology. Materials Testing, 61(5), 405–410.
  • Chen, H. (2016). A process modelling and parameters optimization and recommendation system for binder jetting additive manufacturing process. McGill University (Canada).
  • Chhetri, S. R., Faezi, S., Canedo, A., & Faruque, M. A. Al. (2019). QUILT: Quality inference from living digital twins in IoT-enabled manufacturing systems. Proceedings of the International Conference on Internet of Things Design and Implementation, 237–248.
  • Cruz, M., Parés, C., & Quintela, P. (2021). Progress in Industrial Mathematics: Success Stories: The Industry and the Academia Points of View. Springer.
  • Corradini, F., & Silvestri, M. (2022). Design and testing of a digital twin for monitoring and quality assessment of material extrusion process. Additive Manufacturing, 51, 102633.
  • Di Angelo, L., Di Stefano, P., Dolatnezhadsomarin, A., Guardiani, E., & Khorram, E. (2020). A reliable build orientation optimization method in additive manufacturing: The application to FDM technology. The International Journal of Advanced Manufacturing Technology, 108, 263–276.
  • Duman, B., & Özsoy, K. (2019). Endüstri 4.0 perspektifinde akıllı tarım. 4th International Congress on 3d Printing (Additive Manufacturing) Technologies and Digital Industry, 540–555.
  • Duman, B., & Kayacan, M. C. (2016). Seçmeli Lazer Sinterleme Tezgâhı İçin İmalat Yazılımı Geliştirilmesi. Uluslararası Teknolojik Bilimler Dergisi, 8(3), 27–45.
  • Entes. (2021). Dijital İkiz (Digital Twin) Nedir? Endüstri 4.0 ve Dijital İkizlerin Önemi. Retrieved September 1, 2021, from https://www.entes.com.tr/dijital-ikiz-digital-twin-nedir-endustri-4-0-ve-dijital-ikizlerin-onemi/
  • Fernandes, E. (2020). Internet of Things (IoT) Market Size And Forecast. Retrieved July 14, 2020, from https://www.verifiedmarketresearch.com/product/global-internet-of-things-iot-market-size-and-forecast-to-2026
  • GE Company. (2021). GE Digital Twin: Analytic engine for the digital power plant. https://www.ge.com/digital/sites/default/files/download_assets/Digital-Twin-for-the-digital-power-plant-.pdf
  • Glaessgen, E., & Stargel, D. (2012). The digital twin paradigm for future NASA and US Air Force vehicles. 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA, 1818.
  • Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches, 85–113.
  • He, R., Chen, G., Dong, C., Sun, S., & Shen, X. (2019). Data-driven digital twin technology for optimized control in process systems. ISA Transactions, 95, 221–234.
  • Huang, H., & Baddour, N. (2018). Bearing vibration data collected under time-varying rotational speed conditions. Data in Brief, 21, 1745–1749.
  • Ioturkiye. (2020). IoT ve Bulut Bilişim, Verilerin Geleceği Mi? Retrieved April 2, 2020, from https://ioturkiye.com/2020/04/iot-ve-bulut-bilisim-verilerin-gelecegi-mi
  • Karakılınç, U., Yalçın, B., & Ergene, B. (2019). Toz Yataklı/Beslemeli Eklemeli İmalatta Kullanılan Partiküllerin Uygunluk Araştırması ve Partikül İmalat Yöntemleri. Politeknik Dergisi. 22(4), 801-810.
  • Li, Z., Wang, Y., & Wang, K.-S. (2017). Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario. Advances in Manufacturing, 5, 377–387.
  • Liu, X., Kan, C., & Ye, Z. (2022). Real-time multiscale prediction of structural performance in material extrusion additive manufacturing. Additive Manufacturing, 49, 102503.
  • Mathworks. (2021). Mathworks. https://www.mathworks.com/
  • Medvedofsky, D., Mor-Avi, V., Amzulescu, M., Fernandez-Golfin, C., Hinojar, R., Monaghan, M. J., Otani, K., Reiken, J., Takeuchi, M., & Tsang, W. (2018). Three-dimensional echocardiographic quantification of the left-heart chambers using an automated adaptive analytics algorithm: multicentre validation study. European Heart Journal-Cardiovascular Imaging, 19(1), 47–58.
  • Mehmood, F. (2021). BME280-Sensor-Data. Retrieved September 2, 2021, from https://www.kaggle.com/faisalawan/bme280sensordata
  • Miljanovic, D., Seyedmahmoudian, M., Stojcevski, A., & Horan, B. (2020). Design and fabrication of implants for mandibular and craniofacial defects using different medical-additive manufacturing technologies: a review. Annals of Biomedical Engineering, 48, 2285–2300.
  • Mohammed, A., Elshaer, A., Sareh, P., Elsayed, M., & Hassanin, H. (2020). Additive manufacturing technologies for drug delivery applications. International Journal of Pharmaceutics, 580, 119245.
  • Osho, J., Hyre, A., Pantelidakis, M., Ledford, A., Harris, G., Liu, J., & Mykoniatis, K. (2022). Four Rs Framework for the development of a digital twin: The implementation of Representation with a FDM manufacturing machine. Journal of Manufacturing Systems, 63, 370-380.
  • Pamuk, N. S., & Soysal, M. (2018). Yeni sanayi devrimi endüstri 4.0 üzerine bir inceleme. Verimlilik Dergisi, 1, 41–66.
  • Qin, J., Liu, Y., & Grosvenor, R. (2017). A framework of energy consumption modelling for additive manufacturing using internet of things. Procedia CIRP, 63, 307–312.
  • Rao, P. K., Liu, J., Roberson, D., Kong, Z., & Williams, C. (2015). Online real-time quality monitoring in additive manufacturing processes using heterogeneous sensors. Journal of Manufacturing Science and Engineering, 137(6), 061007.
  • Rafiee, M., Farahani, R. D., & Therriault, D. (2020). Multi‐material 3D and 4D printing: a survey. Advanced Science, 7(12), 1902307.
  • Rengier, F., Mehndiratta, A., Von Tengg-Kobligk, H., Zechmann, C. M., Unterhinninghofen, R., Kauczor, H.-U., & Giesel, F. L. (2010). 3D printing based on imaging data: review of medical applications. International Journal of Computer Assisted Radiology and Surgery, 5, 335–341.
  • Siemens Healthineers. (2018). Exploring the possibilities offered by digital twins in medical technology. Retrieved April 24, 2018, from https://static.healthcare.siemens.com/siemens_hwemhwem_ssxa_websitescontextroot/wcm/idc/groups/public/@global/@press/documents/download/mda4/nzm4/~edisp/exploring-the-possibilities-offered-by-digital-twins-in-medical-technology-05899262.pdf
  • Shi, Z., Mamun, A. A., Kan, C., Tian, W., & Liu, C. (2022). An LSTM-autoencoder based online side channel monitoring approach for cyber-physical attack detection in additive manufacturing. Journal of Intelligent Manufacturing, 1-17.
  • Sparkmeasure. (2020). Nesnelerin İnterneti`nin Temelleri. Retrieved July 9, 2020, from https://www.sparkmeasure.com/b-136-nesnelerin-interneti%60nin-temel.html
  • Standardization, I. O. for. (2015). Additive Manufacturing: General: Principles: Terminology. ISO.
  • Statista. (2016). IoT number of connected devices worldwide. Retrieved November 27, 2016, from https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/#:~:text=The total installed base of,fivefold increase in ten years.
  • Türkay, B. (2018). Nesnelerin İnterneti (IoT) Uygulamalarının Günümüzdeki Yeri. Retrieved June 22, 2018, from https://medium.com/@Barturkay/nesnelerin-i̇nterneti-iot-uygulamalarının-günümüzdeki-yeri-736cd99e37d9
  • Ventura, K., Kabasakal, İ., Keskin, F. D., & Soyuer, H. (2019). Pazar ve Müşteri Yönlü IoT (Internet of Things-Nesnelerin İnterneti) Uygulamalarının İş Yazılımları Kapsamında Analizi. Yaşar Üniversitesi E-Dergisi, 14(56), 507–521.
  • Wiki. (2020). Nesnelerin İnterneti. https://tr.wikipedia.org/wiki/Nesnelerin_interneti
  • Yap, Y. L., Wang, C., Sing, S. L., Dikshit, V., Yeong, W. Y., & Wei, J. (2017). Material jetting additive manufacturing: An experimental study using designed metrological benchmarks. Precision Engineering, 50, 275–285.
  • Yin, J., Lu, C., Fu, J., Huang, Y., & Zheng, Y. (2018). Interfacial bonding during multi-material fused deposition modeling (FDM) process due to inter-molecular diffusion. Materials & Design, 150, 104-112.
Toplam 49 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Yazılımı
Bölüm Araştırma Makaleleri \ Research Articles
Yazarlar

Burhan Duman 0000-0001-5614-1556

Ahmet Ali Süzen 0000-0002-5871-1652

Yayımlanma Tarihi 28 Haziran 2023
Gönderilme Tarihi 16 Şubat 2023
Kabul Tarihi 10 Nisan 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 11 Sayı: 2

Kaynak Göster

APA Duman, B., & Süzen, A. A. (2023). MODELING OF IOT-BASED ADDITIVE MANUFACTURING MACHINE’S DIGITAL TWIN FOR ERROR DETECTION. Mühendislik Bilimleri Ve Tasarım Dergisi, 11(2), 486-497. https://doi.org/10.21923/jesd.1251972
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