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020 ▼a 9780438134683
035 ▼a (MiAaPQ)AAI10903643
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 004
1001 ▼a Dering, Matthew L.
24510 ▼a Generation and Evaluation of Designs Using Deep Neural Networks.
260 ▼a [S.l.] : ▼b The Pennsylvania State University., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 135 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
5021 ▼a Thesis (Ph.D.)--The Pennsylvania State University, 2018.
520 ▼a The objective of this dissertation is to reduce inefficiencies and manual tasks that are part of the product design lifecycle, using proposed deep neural network based methods. Computer Aided Design (CAD) and CAD software are an integral part of
520 ▼a First, a method is proposed which uses a Generative Adversarial Network to automatically generate sketches of objects. Enabling fast and recognizable ideation in a simplified medium such as sketching, aids designers in getting a design started q
520 ▼a Second, a method is described which generates mesh data in a native and usable format. Once a sketched object is selected, the designer will model it in a higher fidelity manner, using 3D modeling software. This method demonstrates the ability o
520 ▼a Next, a new neural network design is proposed which is used to predict an object's intended function. After a 3D object is created, sophisticated simulation software is typically employed to validate that an object's behavior will provide the de
520 ▼a After a design is created, refined and validated, it must be prototyped. Toward this end, a method is described which uses body data to determine if a user is performing a task correctly. This method uses a specialized sensor to monitor a user p
590 ▼a School code: 0176.
650 4 ▼a Computer science.
690 ▼a 0984
71020 ▼a The Pennsylvania State University. ▼b Computer Science and Engineering.
7730 ▼t Dissertation Abstracts International ▼g 79-12B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0176
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000642 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자