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GTC ON-DEMAND

Intelligent Machines, IoT & Robotics
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GPU-Accelerated Deep Learning Framework for Cyber-Enabled Manufacturing
Abstract:
We'll present a GPU-accelerated deep-learning framework for cyber-manufacturing, which enables real-time feedback to designers regarding the manufacturability of a computer-aided design model. We'll talk about a 3D-convolutional neural network-based approach for learning the manufacturability of a mechanical component. The 3D-CNN can recognize the features in a CAD model and classify it to be manufacturable or non-manufacturable with a greater accuracy than traditional rule-based methods. We'll discuss a novel GPU-accelerated voxelization algorithm used to discretize the CAD model and prepare it for deep learning. We'll briefly outline the challenges in training a 3D-CNN using complex CAD models on a GPU (NVIDIA TITAN X) with limited memory. Finally, we'll touch upon different methods to extend the framework to other manufacturing processes, such as additive manufacturing and milling.
 
Topics:
Intelligent Machines, IoT & Robotics, Artificial Intelligence and Deep Learning, Computational Fluid Dynamics, Computer Aided Engineering, AEC & Manufacturing
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7397
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