Graph Representation of 3D CAD Models for Machining Feature Recognition With Deep Learning

2020 ◽  
Author(s):  
Weijuan Cao ◽  
Trevor Robinson ◽  
Yang Hua ◽  
Flavien Boussuge ◽  
Andrew R. Colligan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Feature Recognition ◽  
Graph Representation ◽  
3D Cad ◽  
Machining Feature ◽  
Cad Models ◽  
Efficient Representation ◽  
Graph Neural Networks ◽  
Interacting Features

Abstract In this paper, the application of deep learning methods to the task of machining feature recognition in CAD models is studied. Four contributions are made: 1. An automatic method to generate large datasets of 3D CAD models is proposed, where each model contains multiple machining features with face labels. 2. A concise and informative graph representation for 3D CAD models is presented. This is shown to be applicable to graph neural networks. 3. The graph representation is compared with voxels on their performance of training deep neural networks to segment 3D CAD models. 4. Experiments are also conducted to evaluate the effectiveness of graph-based deep learning for interacting feature recognition. Results show that the proposed graph representation is a more efficient representation of 3D CAD models than voxels for deep learning. It is also shown that graph neural networks can be used to recognize individual features on the model and also identify complex interacting features.

Graph Representation of 3D CAD Models for Machining Feature Recognition With Deep Learning

2021 ◽  
Author(s):  
Weijuan Cao ◽  
Trevor Robinson ◽  
Hua Yang ◽  
Flavien Boussuge ◽  
Andrew Colligan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Graph Representation ◽  
3D Cad ◽  
Machining Feature ◽  
Cad Models

scholarly journals Machining feature recognition based on deep neural networks to support tight integration with 3D CAD systems

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Changmo Yeo ◽  
Byung Chul Kim ◽  
Sanguk Cheon ◽  
Jinwon Lee ◽  
Duhwan Mun
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Network ◽  
3D Models ◽  
Boundary Representation ◽  
Machining Features ◽  
3D Cad ◽  
Machining Feature ◽  
Tight Integration

AbstractRecently, studies applying deep learning technology to recognize the machining feature of three-dimensional (3D) computer-aided design (CAD) models are increasing. Since the direct utilization of boundary representation (B-rep) models as input data for neural networks in terms of data structure is difficult, B-rep models are generally converted into a voxel, mesh, or point cloud model and used as inputs for neural networks for the application of 3D models to deep learning. However, the model’s resolution decreases during the format conversion of 3D models, causing the loss of some features or difficulties in identifying areas of the converted model corresponding to a specific face of the B-rep model. To solve these problems, this study proposes a method enabling tight integration of a 3D CAD system with a deep neural network using feature descriptors as inputs to neural networks for recognizing machining features. Feature descriptor denotes an explicit representation of the main property items of a face. We constructed 2236 data to train and evaluate the deep neural network. Of these, 1430 were used for training the deep neural network, and 358 were used for validation. And 448 were used to evaluate the performance of the trained deep neural network. In addition, we conducted an experiment to recognize a total of 17 types (16 types of machining features and a non-feature) from the B-rep model, and the types for all 75 test cases were successfully recognized.

Graph Neural Networks for Prediction of Fuel Ignition Quality

2020 ◽  
Author(s):  
Artur Schweidtmann ◽  
Jan Rittig ◽  
Andrea König ◽  
Martin Grohe ◽  
Alexander Mitsos ◽  
...  
Keyword(s):  
Neural Networks ◽  
Octane Number ◽  
Molecular Graph ◽  
Chemical Properties ◽  
Graph Representation ◽  
Structure Property ◽  
Oxygenated Hydrocarbons ◽  
Physico Chemical ◽  
Ignition Quality ◽  
Graph Neural Networks

<div>Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure-property relationship (QSPR) models are frequently employed. Recently, a machine learning method, graph neural networks (GNNs), has shown promising results for the prediction of structure-property relationships. GNNs utilize a graph representation of molecules, where atoms correspond to nodes and bonds to edges containing information about the molecular structure. More specifically, GNNs learn physico-chemical properties as a function of the molecular graph in a supervised learning setup using a backpropagation algorithm. This end-to-end learning approach eliminates the need for selection of molecular descriptors or structural groups, as it learns optimal fingerprints through graph convolutions and maps the fingerprints to the physico-chemical properties by deep learning. We develop GNN models for predicting three fuel ignition quality indicators, i.e., the derived cetane number (DCN), the research octane number (RON), and the motor octane number (MON), of oxygenated and non-oxygenated hydrocarbons. In light of limited experimental data in the order of hundreds, we propose a combination of multi-task learning, transfer learning, and ensemble learning. The results show competitive performance of the proposed GNN approach compared to state-of-the-art QSPR models making it a promising field for future research. The prediction tool is available via a web front-end at www.avt.rwth-aachen.de/gnn.</div>

scholarly journals Deep Learning Strategies for ProtoDUNE Raw Data Denoising

2022 ◽  
Vol 6 (1) ◽  
Author(s):  
Marco Rossi ◽  
Sofia Vallecorsa
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Learning Strategies ◽  
Simulation Data ◽  
Raw Data ◽  
Digital Detector ◽  
Speed Up ◽  
Neural Network Hardware ◽  
Graph Neural Networks ◽  
High Level

AbstractIn this work, we investigate different machine learning-based strategies for denoising raw simulation data from the ProtoDUNE experiment. The ProtoDUNE detector is hosted by CERN and it aims to test and calibrate the technologies for DUNE, a forthcoming experiment in neutrino physics. The reconstruction workchain consists of converting digital detector signals into physical high-level quantities. We address the first step in reconstruction, namely raw data denoising, leveraging deep learning algorithms. We design two architectures based on graph neural networks, aiming to enhance the receptive field of basic convolutional neural networks. We benchmark this approach against traditional algorithms implemented by the DUNE collaboration. We test the capabilities of graph neural network hardware accelerator setups to speed up training and inference processes.

scholarly journals Online Planner Selection with Graph Neural Networks and Adaptive Scheduling

2020 ◽  
Vol 34 (04) ◽  
pp. 5077-5084
Author(s):  
Tengfei Ma ◽  
Patrick Ferber ◽  
Siyu Huo ◽  
Jie Chen ◽  
Michael Katz
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Automated Planning ◽  
Adaptive Scheduling ◽  
Optimal Planning ◽  
Graph Representations ◽  
Structural Graph ◽  
Scheduling Method ◽  
Graph Neural Networks ◽  
Node Labels

Automated planning is one of the foundational areas of AI. Since no single planner can work well for all tasks and domains, portfolio-based techniques have become increasingly popular in recent years. In particular, deep learning emerges as a promising methodology for online planner selection. Owing to the recent development of structural graph representations of planning tasks, we propose a graph neural network (GNN) approach to selecting candidate planners. GNNs are advantageous over a straightforward alternative, the convolutional neural networks, in that they are invariant to node permutations and that they incorporate node labels for better inference.Additionally, for cost-optimal planning, we propose a two-stage adaptive scheduling method to further improve the likelihood that a given task is solved in time. The scheduler may switch at halftime to a different planner, conditioned on the observed performance of the first one. Experimental results validate the effectiveness of the proposed method against strong baselines, both deep learning and non-deep learning based.The code is available at https://github.com/matenure/GNN_planner.

scholarly journals Biological network analysis with deep learning

2020 ◽  
Author(s):  
Giulia Muzio ◽  
Leslie O’Bray ◽  
Karsten Borgwardt
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Biological Networks ◽  
Protein Function ◽  
Regulatory Networks ◽  
Biological Network ◽  
Gene Interaction ◽  
Disease Diagnosis ◽  
Interaction Prediction ◽  
Graph Neural Networks

Abstract Recent advancements in experimental high-throughput technologies have expanded the availability and quantity of molecular data in biology. Given the importance of interactions in biological processes, such as the interactions between proteins or the bonds within a chemical compound, this data is often represented in the form of a biological network. The rise of this data has created a need for new computational tools to analyze networks. One major trend in the field is to use deep learning for this goal and, more specifically, to use methods that work with networks, the so-called graph neural networks (GNNs). In this article, we describe biological networks and review the principles and underlying algorithms of GNNs. We then discuss domains in bioinformatics in which graph neural networks are frequently being applied at the moment, such as protein function prediction, protein–protein interaction prediction and in silico drug discovery and development. Finally, we highlight application areas such as gene regulatory networks and disease diagnosis where deep learning is emerging as a new tool to answer classic questions like gene interaction prediction and automatic disease prediction from data.

Deep learning-based framework for Shape Instance Registration on 3D CAD models

2021 ◽  
Author(s):  
Lucas Figueiredo ◽  
Paulo Ivson ◽  
Waldemar Celes
Keyword(s):  
Deep Learning ◽  
3D Cad ◽  
Cad Models

A deep learning approach to the classification of 3D CAD models

2014 ◽  
Vol 15 (2) ◽  
pp. 91-106 ◽  
Author(s):  
Fei-wei Qin ◽  
Lu-ye Li ◽  
Shu-ming Gao ◽  
Xiao-ling Yang ◽  
Xiang Chen
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
3D Cad ◽  
Cad Models

Partial Matching Algorithm of 3D CAD Models Based on the Constraints of Transition Features

2010 ◽  
Vol 97-101 ◽  
pp. 3371-3375
Author(s):  
Kai Xing Zhang ◽  
Shu Sheng Zhang ◽  
Xiao Liang Bai
Keyword(s):  
Feature Recognition ◽  
Graph Isomorphism ◽  
Topological Information ◽  
Machining Features ◽  
New Approach ◽  
Partial Matching ◽  
Mechanical Parts ◽  
3D Cad ◽  
Matching Efficiency ◽  
Cad Models

The CAD models of mechanical parts usually have many blends and chamfers, and the existence of these machining features can greatly change the geometric and topological patterns of the CAD models, but the existing partial matching algorithms cannot match the CAD models which contain machining features such as blends and chamfers. In this paper, a new approach to partial matching based on the constraints of transition features is proposed. Firstly, the transition features are identified by feature recognition, and then these machining features are removed to eliminate the impacts to the geometric and topological information of the CAD models, and the attribute adjacent graph is reconstructed, finally, the sub-graph isomorphism approach is used to achieve the partial matching. Experimental results show that this method can achieve partial matching of CAD models which contain machining features such as blends and chamfers, and the matching efficiency can satisfy the requirement of the engineering retrieval.

scholarly journals Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks

2021 ◽  
Author(s):  
Pengyong Li ◽  
Jun Wang ◽  
Ziliang Li ◽  
Yixuan Qiao ◽  
Xianggen Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Supervised Learning ◽  
Learning Strategy ◽  
Binary Classification ◽  
Representation Learning ◽  
Graph Representation ◽  
Superior Performance ◽  
Graph Classification ◽  
Training Strategy ◽  
Graph Neural Networks

Self-supervised learning has gradually emerged as a powerful technique for graph representation learning. However, transferable, generalizable, and robust representation learning on graph data still remains a challenge for pre-training graph neural networks. In this paper, we propose a simple and effective self-supervised pre-training strategy, named Pairwise Half-graph Discrimination (PHD), that explicitly pre-trains a graph neural network at graph-level. PHD is designed as a simple binary classification task to discriminate whether two half-graphs come from the same source. Experiments demonstrate that the PHD is an effective pre-training strategy that offers comparable or superior performance on 13 graph classification tasks compared with state-of-the-art strategies, and achieves notable improvements when combined with node-level strategies. Moreover, the visualization of learned representation revealed that PHD strategy indeed empowers the model to learn graph-level knowledge like the molecular scaffold. These results have established PHD as a powerful and effective self-supervised learning strategy in graph-level representation learning.

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