gpu-accelerated deep learning framework for cyber …€¦ · deep learning framework for...
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GPU-ACCELERATED
DEEP LEARNING
FRAMEWORK FOR
CYBER-ENABLED
MANUFACTURING
ADITYA BALU
SAMBIT GHADAI
SOUMIK SARKAR
ADARSH KRISHNAMURTHY
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Design for Manufacturing
Outline
Volumetric Representations for
CAD Models
Deep Learning based Design for
ManufacturingExplainable Deep
Learning
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Design
Manufacturing
INFLUENCE OVER PRODUCT COST
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TimeDesign Production
Reducing Product Cost
• Design has a large influence on final product cost
• DFM helps identify production issues early
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Source: David Stienstra (Rose-Hulman)
Design freedom to make change
Cost of change
Design knowledge
DFM
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Challenges
• Traditional DFM method involves rule based analysis
• Depends on the experience of the engineers
• Several rules for different processes• Example ISO/ASTM 52910:2017(E)
Standard Guidelines for Design for Additive Manufacturing
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Design
ManufacturingRedesign
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Artificial Intelligence for Design for Manufacturing
• Use deep learning to learn non-manufacturable features in a CAD model• Learn from examples of manufacturable
and non-manufacturable models
• Advantages• No explicit hand-crafting of rules
• Learn complicated rules that are difficult to codify
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Feasibility Demonstration – Drilling Holes
• Common manufacturing operations
• Fewer set of design rules• Can manually create ground-truth data
• Complex design rules• Depth to diameter ratio
• Blind vs. through holes
• Proximity of holes to object boundaries
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Boundary Representation (B-Rep) CAD Models
• De-facto representation for CAD models
• Can be easily tessellated into triangles for rendering
• Difficult to interpret volumetric information• Size of a feature
• Internal location of a feature
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Voxel Representation
• Binary occupancy information• Augmented with extra geometry
information
• Can be used as direct input to a convolutional neural network
• Require a fast method to voxelize a large number of CAD models
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Design for Manufacturing
Outline
Volumetric Representations for
CAD Models
Deep Learning based Design for
ManufacturingExplainable Deep
Learning
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Volumetric Voxelization
• Overlay a regular voxel grid on the object
• Test point membership of the voxel bounding-box center points, classify as in or out
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Point Membership Classification (PMC) Using GPU Slicing
• Use standard PMC using odd ray intersection test
• Slice the object perpendicular to an arbitrary axis
• Render the sliced object and count the number of pixels
• Extend to 3D; Each pixel corresponds to a grid point in a 2D slice
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View Direction
Pixels
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Design for Manufacturing
Outline
Volumetric Representations for
CAD Models
Deep Learning based Design for
ManufacturingExplainable Deep
Learning
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Learning Local Features
• Local Feature learning is different from object recognition
• Variation in features affect the object classification
• Learning the features by semi-supervised learning
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[1]. http://dfmpro.geometricglobal.com/files/2017/04/Whitepaper-A-new-approach-to-design-and-manufacturing-collaboration.pdf
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Need for 3D Convolutional Nets
• Hierarchical feature extraction from volumetric representation
• Capability to learn features with object classification
• Amenable to model interpretability due to learning of spatial location
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• Binary definition of manufacturability (binary cross-entropy loss)
• Choice of input resolution depending on the GPUs
• Architecture• 3D convolutional layer and 3D max. pooling layer
• ReLU activation with output layer having Sigmoid activation
Deep Learning Based Design for Manufacturing
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Deep Learning Based Design for Manufacturing
(a) (b) (c)
(d) (e) (f)
DLDFM
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Results
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0% 20% 40% 60% 80% 100%
DLDFM (binary + normals)
DLDFM (Orthogonal Distance Fields)
DLDFM (binary)
Representative Test Data Manufacturable
True Positive (Predicted Manufacturable, Actually Manufacturable)
False Negative (Predicted Non-Manufacturable, Actually Manufacturable)
0% 20% 40% 60% 80% 100%
DLDFM (binary + normals)
DLDFM (Orthogonal Distance Fields)
DLDFM (binary)
Representative Test Data Non-Manufacturable
True Negative (Predicted Non-Manufacturable, Actually Non-Manufacturable)
False Positive (Predicted Manufacturable, Actually Non-Manufacturable)
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0% 20% 40% 60% 80% 100%
DLDFM (binary + normals)
DLDFM (Orthogonal Distance Fields)
DLDFM (binary)
Non-Representative Test Data Manufacturable
True Positive (Predicted Manufacturable, Actually Manufacturable)
False Negative (Predicted Non-Manufacturable, Actually Manufacturable)
Results
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0% 20% 40% 60% 80% 100%
DLDFM (binary + normals)
DLDFM (Orthogonal Distance Fields)
DLDFM (binary)
Non-Representative Test Data Non-Manufacturable
True Negative (Predicted Non-Manufacturable, Actually Non-Manufacturable)
False Positive (Predicted Manufacturable, Actually Non-Manufacturable)
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Results
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0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
DLDFM (orthogonal distance fields)
DLDFM (binary + normals)
DLDFM (binary)
True Positive (Predicted Manufacturable, Actually Manufacturable)
True Negative (Predicted Non-Manufacturable, Actually Non-Manufacturable)
False Negative (Predicted Non-Manufacturable, Actually Manufacturable)
False Positive (Predicted Manufacturable, Actually Non-Manufacturable)
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Design for Manufacturing
Outline
Volumetric Representations for
CAD Models
Deep Learning based Design for
ManufacturingExplainable Deep
Learning
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Model Interpretability
• Possible methods• Back-propagation
• Guided back-propagation, saliency map, etc.Disadvantage: Not class discriminative
• Grad-CAM• Class discriminative
References:
[1]. Zeiler, Matthew D., and Rob Fergus. "Visualizing and understanding convolutional networks." European conference on computer vision. Springer International Publishing, 2014.
[2]. Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).
[3]. Zhou, Bolei, et al. "Learning deep features for discriminative localization." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.
[4]. Selvaraju, Ramprasaath R., et al. "Grad-CAM: Why did you say that?." arXiv preprint arXiv:1611.07450 (2016).
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3D Grad-CAM
• Perform global average pooling and back-propagate the activations
Input:Volumetric Representation
Convolutional Layer
Convolution Filters 1
Convolution Filters 2
Convolutional Layer
Pooling LayerFully Connected
Layer
(Yes/No)
InterpretationClass Activation Map
Output: Manufacturability
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Insights from GradCAM
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One HoleManufacturable
Insight:
3D Grad-CAM is class discriminative
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Two Holes Manufacturable (both)
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Insight:
DLDFM can predict manufacturability of multiple features simultaneously
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Insight:
DLDFM can predict manufacturability of individual features
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Two Holes Non-Manufacturable (due to one of them)
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Insight:
DLDFM can predict manufacturability of interacting features by generalizing the rules
Two Holes Non-Manufacturable (due to interaction between them)
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L-Shaped Block with Hole Non-Manufacturable (close to external face)
Insight:
DLDFM can predict manufacturability based on a local feature instead of external geometry
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Cylindrical-Shaped Block with HoleNon- Manufacturable (close to external cylindrical face)
Insight:
DLDFM can predict manufacturability based on a local feature even with complicated external geometry
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Demo
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Acknowledgements
• AI-based Design and Manufacturability Lab (ADAM Lab)• Gavin Young
• Kin Gwn Lore
• Funding Sources• National Science Foundation
• CMMI:1644441 – CM: Machine-Learning Driven Decision Support in Design for Manufacturability
• nVIDIA• Titan X GPU for Academic Research
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Thank You!
Questions?
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