عنوان

Deep Learning Approach for Controlling Additive Manufacturing Process

Number

TL52488

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

Deep Learning Approach for Controlling Additive Manufacturing Process

General Material Designation

[Thesis]

First Statement of Responsibility

Razavi Arab, Nariman

Subsequent Statement of Responsibility

Banadaki, Yaser

Name of Publisher, Distributor, etc.

Southern University and Agricultural and Mechanical College

Date of Publication, Distribution, etc.

2019

Text of Note

106 p.

Dissertation or thesis details and type of degree

M.S.

Body granting the degree

Southern University and Agricultural and Mechanical College

Text preceding or following the note

2019

Text of Note

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.

Subject Term

Artificial intelligence

Subject Term

Computer science

Razavi Arab, Nariman

Banadaki, Yaser

Southern University and Agricultural and Mechanical College

Electronic name

p

[Thesis]

276903

a

Y