Abstract
Günümüz dünyasının en gelişmiş bilim ve teknolojilerinden biri olan yapay zeka (AI) teknolojisi, üretimde ve yaşamda, özellikle imalat alanında giderek daha fazla kullanılmaktadır. Yapay zeka teknolojisinin imalat, mekanik kusur tespiti, kalite denetimi, iş yeri güvenliğini artırma ve diğer alanlarda kullanılmaktadır. Yapay zeka teknolojisi, yapay zeka ile mekanik imalat sanayinin füzyonunun ürünleri olan akıllı bulaşık makineleri ve akıllı süpürücülerin yaygınlaşması gibi, insanların günlük yaşamlarında daha yaygın bir şekilde kullanılmaya başlanmasıyla birlikte, insanların yaşamlarında giderek daha fazla önem kazanmaktadır. Gerçekten de yapay zeka teknolojisi, yalnızca üretim hassasiyetini sağlamakla kalmayan, aynı zamanda iş üretkenliğini ve iş yeri güvenliğini de artıran mekanik imalat işlemlerinde yaygın olarak kullanılmaktadır. Yapay zekanın yükselişi, imalat endüstrisinde bir bütün olarak önemli değişikliklere neden olmuştur. İstisnasız imalat endüstrisi, otomasyonu ve akıllı geliştirmeyi gerçekleştirmenin yanı sıra üretkenliği artırmak için yapay zeka teknolojisine güvenmelidir. Mekanik bileşenleri kategorize etmek için yapay zekayı kullanarak, yalnızca bir görüntüye veya CAD modeline dayalı olarak parçalar üretebiliriz. Bir makinede gerekli bir bileşeni bulmak için şu anda bir kataloğa göz atmamız ve mevcut olanaklara ve katalog anlayışınıza bağlı olarak hangi parçayı istediğinizi ayırt edebilmemiz gerekir. Tek bir rakam veya karakter değişikliği farklı türde bir parçayı gösterebileceğinden, ezberlenmesi gereken seri numaraları vardır. Algoritma hangi bölümlerin en iyi olduğunu seçecek ve aramamızı önemli ölçüde kolaylaştıracaktır
Supporting Institution
Yok.
Project Number
Yok.
Thanks
Yok.
References
- Andrew Ng, 2022, Convolutional Neural Networks of the Deep Learning Specialization by deeplearning.ai. (n.d.). Retrieved from Coursera.
- Atalay M.,, Çelik E., Büyük Veri Analizinde Yapay Zekâ Ve Makine Öğrenmesi Uygulamaları Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi Cilt.9 Sayı.22 2017 - Aralık (s.155-172).
- Balaban, M. E. ve Kartal, E. (2015). Veri Madenciliği ve Makine Öğrenmesi. İstanbul: Çağlayan Kitabevi.
- Bao, C.W., Jiang, W. (2019) Exploration and Practice of the Cultivation Scheme of Mechanical Engineering Specialty under the Background of New Engineering Strategy. Sci-Tech Innovation & Productivity, 4: 83-85.
- Charniak, E. ve McDermott, D. (1985). Introduction to Artificial Intelligence. Boston, MA, USA: Addison-Wesley Series in Computer Science. Conor McDonald, Machine learning fundamentals (I): Cost functions and gradient descent (2017), Towards data science.
- Elmas, Ç., (2011), Yapay Zeka uygulamaları. Ankara: Seçkin Yayıncılık.
- Jason B., Difference Between a Batch and an Epoch in a Neural Network (2018), machinelearningmastery.com.
- Khan, M. A., Uddin, M. F. ve Gupta, N. (2014). Seven V's of Big Data understanding Big Data to extract value. Conference of the American Society for Engineering Education, IEEE, DOI: 10.1109/ASEEZone1.2014.6820689, Bridgeport, CT, USA.
- Liu, J.N. (2018) Discussion on Relation between Mechanical Electronic Engineering and Artificial Intelligence. Journal of Tianjin Vocational Institutes, 20: 76-79.
- Shirkhorshidi, A. S., Aghabozorgi, S., Wah, T. Y. ve Herawan, T. (2014). Big Data Clustering: A Review. B. Murgante vd. (Ed.) Computational Science and Its Applications – ICCSA 2014, Lecture Notes in Computer Science, Switzerland: Springer International Publishing.
- Valarmathi G., S.U. Suganthi, V. Subashini, R. Janaki, R. Sivasankari, S. Dhanasekar, CNN algorithm for plant classification in deep learning, Materials Today: Proceedings, 46, (2021). doi.or/10.1016/j.matpr.2021.01.847
- Wu, A.H., Yang, Q.B., Hao, J. (2019) The Innovation and Reform of Higher Education under the Leadership of Emerging Engineering Education. Research in Higher Education of Engineering, 1:1-7.
- Yang, J.R. (2019) Study on the Present Status in the Interfusion of AI and Manufacturing Industry. Journal of Shanghai Electric Technology, 2: 1-5.
Abstract
Artificial intelligence (AI) technology, as one of the most sophisticated science and technology in today's world, is increasingly being used to production and life, particularly in the manufacturing business. it demonstrates how artificial intelligence technology is used in mechanical manufacturing, namely in defect detection, quality inspection, enhancing workplace safety, and other areas. Artificial intelligence technology is becoming increasingly important in people's lives as it becomes more widely used in people's daily lives, such as the widespread use of smart dishwashers and smart sweepers, which are the products of the fusion of artificial intelligence and the mechanical manufacturing industry. Indeed, artificial intelligence technology has been widely utilized in the mechanical manufacturing business, which not only ensures production precision, but also enhances job productivity and workplace safety. The rise of artificial intelligence has caused significant changes in the manufacturing industry as a whole. Without exception, the manufacturing industry must rely on AI technology to accomplish automation and intelligent development, as well as to improve productivity. Using artificial intelligence to categorize mechanical components, we may propose parts from a based solely on an image or CAD model. To find a necessary component in a machines we must currently browse through a catalogue and be able to discern which part you want based on the available possibilities and your understanding of the catalogue. There are serial numbers to memorize since a single digit or character change might indicate a different sort of part. The algorithm will choose which sections are the best and will significantly facilitate our search.
Keywords
Machine Learning Artificial Intelligence (AI) Machine Learning Deep Learning Manufacturing Technologies, Mechanical Engineering
Project Number
Yok.
References
- Andrew Ng, 2022, Convolutional Neural Networks of the Deep Learning Specialization by deeplearning.ai. (n.d.). Retrieved from Coursera.
- Atalay M.,, Çelik E., Büyük Veri Analizinde Yapay Zekâ Ve Makine Öğrenmesi Uygulamaları Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi Cilt.9 Sayı.22 2017 - Aralık (s.155-172).
- Balaban, M. E. ve Kartal, E. (2015). Veri Madenciliği ve Makine Öğrenmesi. İstanbul: Çağlayan Kitabevi.
- Bao, C.W., Jiang, W. (2019) Exploration and Practice of the Cultivation Scheme of Mechanical Engineering Specialty under the Background of New Engineering Strategy. Sci-Tech Innovation & Productivity, 4: 83-85.
- Charniak, E. ve McDermott, D. (1985). Introduction to Artificial Intelligence. Boston, MA, USA: Addison-Wesley Series in Computer Science. Conor McDonald, Machine learning fundamentals (I): Cost functions and gradient descent (2017), Towards data science.
- Elmas, Ç., (2011), Yapay Zeka uygulamaları. Ankara: Seçkin Yayıncılık.
- Jason B., Difference Between a Batch and an Epoch in a Neural Network (2018), machinelearningmastery.com.
- Khan, M. A., Uddin, M. F. ve Gupta, N. (2014). Seven V's of Big Data understanding Big Data to extract value. Conference of the American Society for Engineering Education, IEEE, DOI: 10.1109/ASEEZone1.2014.6820689, Bridgeport, CT, USA.
- Liu, J.N. (2018) Discussion on Relation between Mechanical Electronic Engineering and Artificial Intelligence. Journal of Tianjin Vocational Institutes, 20: 76-79.
- Shirkhorshidi, A. S., Aghabozorgi, S., Wah, T. Y. ve Herawan, T. (2014). Big Data Clustering: A Review. B. Murgante vd. (Ed.) Computational Science and Its Applications – ICCSA 2014, Lecture Notes in Computer Science, Switzerland: Springer International Publishing.
- Valarmathi G., S.U. Suganthi, V. Subashini, R. Janaki, R. Sivasankari, S. Dhanasekar, CNN algorithm for plant classification in deep learning, Materials Today: Proceedings, 46, (2021). doi.or/10.1016/j.matpr.2021.01.847
- Wu, A.H., Yang, Q.B., Hao, J. (2019) The Innovation and Reform of Higher Education under the Leadership of Emerging Engineering Education. Research in Higher Education of Engineering, 1:1-7.
- Yang, J.R. (2019) Study on the Present Status in the Interfusion of AI and Manufacturing Industry. Journal of Shanghai Electric Technology, 2: 1-5.
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Project Number | Yok. |
| Early Pub Date | December 31, 2022 |
| Publication Date | December 31, 2022 |
| Published in Issue | Year 2022 Issue: 45 |
