Deep Learning Approach for Controlling Additive Manufacturing Process
نام عام مواد
[Thesis]
نام نخستين پديدآور
Razavi Arab, Nariman
نام ساير پديدآوران
Banadaki, Yaser
وضعیت نشر و پخش و غیره
نام ناشر، پخش کننده و غيره
Southern University and Agricultural and Mechanical College
تاریخ نشرو بخش و غیره
2019
مشخصات ظاهری
نام خاص و کميت اثر
106
یادداشتهای مربوط به پایان نامه ها
جزئيات پايان نامه و نوع درجه آن
M.S.
کسي که مدرک را اعطا کرده
Southern University and Agricultural and Mechanical College
امتياز متن
2019
یادداشتهای مربوط به خلاصه یا چکیده
متن يادداشت
Additive manufacturing (AM) is a crucial component of smart manufacturing systems that disrupts traditional supply chains. However, the parts built using the state-of-the-art powder-bed 3D printers have noticeable unpredictable mechanical properties. The attempts to improve the geometry and mechanical properties of final products in the 3D printing process were usually limited to the development of a typical control system using feedback from sensor measurement. However, a smarter control system is required to learn and adapt as the AM machine is operating. In this thesis, I propose a machine learning (ML) algorithm as a promising way of improving the underlying failure phenomena in 3D printing. I employ a Deep Convolutional Neural Network (DCNN) to automatically detect the defects in printing the layers, thereby turning 3D printers into essentially their own inspectors. We expect that the proposed DCNN model generates a precise feedback signal for a smart 3D printer to recognize any issues with the build itself to make proper adjustments and corrections without operator intervention. This can enhance the quality of the AM process, leading to manufacturing better parts with fewer quality hiccups, limiting waste of time and materials.
موضوع (اسم عام یاعبارت اسمی عام)
موضوع مستند نشده
Artificial intelligence
موضوع مستند نشده
Computer science
نام شخص به منزله سر شناسه - (مسئولیت معنوی درجه اول )