Learning localized features in 3D CAD models for manufacturability analysis of drilled holes

Ghadai, Sambit; Balu, Aditya; Sarkar, Soumik; Krishnamurthy, Adarsh

doi:10.1016/j.cagd.2018.03.024

Cited by 64 publications

(14 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ghadai et al proposed using 3D-CNN to identify difficult-to-manufacture drilled holes in CAD geometry. This detection can assist in the realization of intelligent manufacturability decisions [ 20 ]. Lin et al proposed a YOLO-based capacitance detection method for PCB assembly, however, the detection object only contains nine types of capacitance [ 21 ].…”

Section: Related Workmentioning

confidence: 99%

Research on Object Detection of PCB Assembly Scene Based on Effective Receptive Field Anchor Allocation

Chen

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Vision-based object detection of PCB (printed circuit board) assembly scenes is essential in accelerating the intelligent production of electronic products. In particular, it is necessary to improve the detection accuracy as much as possible to ensure the quality of assembly products. However, the lack of object detection datasets in PCB assembly scenes is the key to restricting intellectual PCB assembly research development. As an excellent representative of the one-stage object detection model, YOLOv3 (you only look once version 3) mainly relies on placing predefined anchors on the three feature pyramid layers and realizes recognition and positioning using regression. However, the number of anchors distributed in each grid cell of different scale feature layers is usually the same. The ERF (effective receptive field) corresponding to the grid cell at different locations varies. The contradiction between the uniform distribution of fixed-size anchors and the ERF size range in different feature layers will reduce the effectiveness of object detection. Few people use ERF as a standard for assigning anchors to improve detection accuracy. To address this issue, firstly, we constructed a PCB assembly scene object detection dataset, which includes 21 classes of detection objects in three scenes before assembly, during assembly, and after assembly. Secondly, we performed a refined ERF analysis on each grid of the three output layers of YOLOv3, determined the ERF range of each layer, and proposed an anchor allocation rule based on the ERF. Finally, for the small and difficult-to-detect TH (through-holes), we increased the context information and designed improved-ASPP (Atrous spatial pyramid pooling) and channel attention joint module. Through a series of experiments on the object detection dataset of the PCB assembly scene, we found that under the framework of YOLOv3, anchor allocation based on ERF can increase mAP (mean average precision) from 79.32% to 89.86%. At the same time, our proposed method is superior to Faster R-CNN (region convolution neural network), SSD (single shot multibox detector), and YOLOv4 (you only look once version 4) in the balance of high detection accuracy and low computational complexity.

show abstract

Section: Related Workmentioning

confidence: 99%

Research on Object Detection of PCB Assembly Scene Based on Effective Receptive Field Anchor Allocation

Chen

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…[24] extends Grad-CAM to 3D-CNN in solving the Alzheimer's disease classification. [9] extends the Grad-CAM to recognize difficult-to-manufacture drilled holes in a complex CAD geometry.…”

Section: Visual Explanationsmentioning

confidence: 99%

IMFNet: Interpretable Multimodal Fusion for Point Cloud Registration

Huang¹,

Qu²,

Zuo³

et al. 2021

Preprint

View full text Add to dashboard Cite

The existing state-of-the-art point descriptor relies on structure information only, which omit the texture information. However, texture information is crucial for our humans to distinguish a scene part. Moreover, the current learning-based point descriptors are all black boxes which are unclear how the original points contribute to the final descriptor. In this paper, we propose a new multimodal fusion method to generate a point cloud registration descriptor by considering both structure and texture information. Specifically, a novel attention-fusion module is designed to extract the weighted texture information for the descriptor extraction. In addition, we propose an interpretable module to explain the original points in contributing to the final descriptor. We use the descriptor element as the loss to backpropagate to the target layer and consider the gradient as the significance of this point to the final descriptor. This paper moves one step further to explainable deep learning in the registration task. Comprehensive experiments on 3DMatch, 3DLoMatch and KITTI demonstrate that the multimodal fusion descriptor achieves state-of-theart accuracy and improve the descriptor's distinctiveness. We also demonstrate that our interpretable module in explaining the registration descriptor extraction.

show abstract

“…Varying interaction parameters produce morphologies with different domain purities, while varying blend ratios produce domains of different sizes. Here, we choose to consider 2D morphologies, with extension to 3D morphologies being conceptually straightforward (but computationally non-trivial 11,34 ). This dataset of morphologies (i.e., 2D, amorphous, isotropic) chosen is a subset of the diversity of We construct a forward map from morphology to performance.…”

Section: Training and Validationmentioning

confidence: 99%

“…Due to this special ability of ML algorithms to be input agnostic, i.e., the ability to automatically evaluate features from input data, they have found utility in a wide variety of applications including recommendation systems 16 and self-driving cars. 17 These approaches are slowly gaining popularity in physics and engineered systems, [18][19][20] where modern sensor and computational developments have paved the way for structured data generation. 21,22 Here, we utilize the versatility of CNNs to map the active layer morphology of thin film OPVs to a performance metric, which is the short-circuit current J sc .…”

Section: Introductionmentioning

confidence: 99%

Interpretable deep learning for guided microstructure-property explorations in photovoltaics

et al. 2019

Self Cite

View full text Add to dashboard Cite

The microstructure determines the photovoltaic performance of a thin film organic semiconductor film. The relationship between microstructure and performance is usually highly non-linear and expensive to evaluate, thus making microstructure optimization challenging. Here, we show a data-driven approach for mapping the microstructure to photovoltaic performance using deep convolutional neural networks. We characterize this approach in terms of two critical metrics, its generalizability (has it learnt a reasonable map?), and its intepretability (can it produce meaningful microstructure characteristics that influence its prediction?). A surrogate model that exhibits these two features of generalizability and intepretability is particularly useful for subsequent design exploration. We illustrate this by using the surrogate model for both manual exploration (that verifies known domain insight) as well as automated microstructure optimization. We envision such approaches to be widely applicable to a wide variety of microstructure-sensitive design problems.

show abstract

Learning localized features in 3D CAD models for manufacturability analysis of drilled holes

Cited by 64 publications

References 9 publications

Research on Object Detection of PCB Assembly Scene Based on Effective Receptive Field Anchor Allocation

Research on Object Detection of PCB Assembly Scene Based on Effective Receptive Field Anchor Allocation

IMFNet: Interpretable Multimodal Fusion for Point Cloud Registration

Interpretable deep learning for guided microstructure-property explorations in photovoltaics

Contact Info

Product

Resources

About