عنوان

Deep Learning Approach for Controlling Additive Manufacturing Process

شماره

TL52488

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Deep Learning Approach for Controlling Additive Manufacturing Process

نام عام مواد

[Thesis]

نام نخستين پديدآور

Razavi Arab, Nariman

نام ساير پديدآوران

Banadaki, Yaser

نام ناشر، پخش کننده و غيره

Southern University and Agricultural and Mechanical College

تاریخ نشرو بخش و غیره

2019

متن يادداشت

106 p.

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

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.

اصطلاح موضوعی

Artificial intelligence

اصطلاح موضوعی

Computer science

مستند نام اشخاص تاييد نشده

Razavi Arab, Nariman

مستند نام اشخاص تاييد نشده

Banadaki, Yaser

مستند نام تنالگان تاييد نشده

Southern University and Agricultural and Mechanical College

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y