Identifying manufacturability and machining processes using deep 3D convolutional networks

Peddireddy, Dheeraj; Fu, Xingyu; Shankar, Anirudh; Wang, Haobo; Joung, Byung Gun; Aggarwal, Vaneet; Sutherland, John W.; Jun, Martin B. G.

doi:10.1016/j.jmapro.2021.02.034

Cited by 19 publications

(4 citation statements)

References 8 publications

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“…Generating high-quality 3D CAD models can also help analyze machinability and manufacturing. Peddireddy et al [11] reported that being able to generate a triangularly tessellated surface (STL) from the 3D CAD model can be used to predict the manufacturability of the CAD design and machining processes. However, working with highly complex surfaces is a constant challenge for designers.…”

Section: State Of the Artmentioning

confidence: 99%

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Integrating Computer Vision and CAD for Precise Dimension Extraction and 3D Solid Model Regeneration for Enhanced Quality Assurance

Bhandari,

Manandhar

2023

Machines

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This paper focuses on the development of an integrated system that can rapidly and accurately extract the geometrical dimensions of a physical object assisted by a robotic hand and generate a 3D model of an object in a popular commercial Computer-Aided Design (CAD) software using computer vision. Two sets of experiments were performed: one with a simple cubical object and the other with a more complex geometry that needed photogrammetry to redraw it in the CAD system. For the accurate positioning of the object, a robotic hand was used. An Internet of Things (IoT) based camera unit was used for capturing the image and wirelessly transmitting it over the network. Computer vision algorithms such as GrabCut, Canny edge detector, and morphological operations were used for extracting border points of the input. The coordinates of the vertices of the solids were then transferred to the Computer-Aided Design (CAD) software via a macro to clean and generate the border curve. Finally, a 3D solid model is generated by linear extrusion based on the curve generated in CATIA. The results showed excellent regeneration of an object. This research makes two significant contributions. Firstly, it introduces an integrated system designed to achieve precise dimension extraction from solid objects. Secondly, it presents a method for regenerating intricate 3D solids with consistent cross-sections. The proposed system holds promise for a wide range of applications, including automatic 3D object reconstruction and quality assurance of 3D-printed objects, addressing potential defects arising from factors such as shrinkage and calibration, all with minimal user intervention.

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Section: State Of the Artmentioning

confidence: 99%

“…The blue box in the second image is a region of interest (ROI) and the black patch is a manually marked mask denoting the background color. Detailed operations and finer touch-ups of the GrabCut algorithm can be found in [11].…”

Section: Image Segmentationmentioning

confidence: 99%

Integrating Computer Vision and CAD for Precise Dimension Extraction and 3D Solid Model Regeneration for Enhanced Quality Assurance

Bhandari,

Manandhar

2023

Machines

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“…We summarized all the cantilever beam models provided in [4] and created a parameterized synthetic FEA simulation dataset. According to [26,27], we provide uniform random values to provide design parameters for cantilever beams to improve the repeatability and portability of the dataset. Details on dataset preparation are explained in this section.…”

Section: Dataset Preparationmentioning

confidence: 99%

“…Although the generated cantilevered structures cannot represent all the beam structures, the selected ones represent the most common structures in the mechanical areas [4]. Also, following the similar parametric design pattern, other features can be considered and added into the dataset in the future according to previous synthetic CAD design researches [26,27].…”

Section: Stress Field Simulationmentioning

confidence: 99%

An FEA surrogate model with Boundary Oriented Graph Embedding approach

Fu,

Zhou,

Peddireddy

et al. 2021

Preprint

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In this work, we present a Boundary Oriented Graph Embedding (BOGE) approach for the Graph Neural Network (GNN) to serve as a general surrogate model for regressing physical fields and solving boundary value problems. Providing shortcuts for both boundary elements and local neighbor elements, the BOGE approach can embed structured mesh elements into the graph and performs an efficient regression on large-scale triangular-mesh-based FEA results, which cannot be realized by other machine-learning-based surrogate methods. Focusing on the cantilever beam problem, our BOGE approach cannot only fit the distribution of stress fields but also regresses the topological optimization results, which show its potential of realizing abstract decision-making design process. The BOGE approach with 3-layer DeepGCN model achieves the regression with MSE of 0.011706 (2.41% MAPE) for stress field prediction and 0.002735 MSE (with 1.58% elements having error larger than 0.01) for topological optimization. The overall concept of the BOGE approach paves the way for a general and efficient deep-learning-based FEA simulator that will benefit both industry and design-related areas. KeywordsMachine learning • Graph neural network • Stress field • Solid mechanics • Topology optimization Recently, with the rapid development of Deep Learning (DL) techniques, some researchers employed Convolutional Neural Network (CNN) as the backbone for solving physical field prediction problems, and found out that CNN based surrogate models cannot only solve basic boundary value problems (e.g., solid mechanics [4-6], fluid dynamics [7-9],

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Two-stage filtering method to improve the performance of object detection trained by synthetic dataset in heavily cluttered industry scenes

et al. 2023

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Identifying manufacturability and machining processes using deep 3D convolutional networks

Cited by 19 publications

References 8 publications

Integrating Computer Vision and CAD for Precise Dimension Extraction and 3D Solid Model Regeneration for Enhanced Quality Assurance

Integrating Computer Vision and CAD for Precise Dimension Extraction and 3D Solid Model Regeneration for Enhanced Quality Assurance

An FEA surrogate model with Boundary Oriented Graph Embedding approach

Two-stage filtering method to improve the performance of object detection trained by synthetic dataset in heavily cluttered industry scenes

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