Journal
2021 21ST INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS 2021)
Volume -, Issue -, Pages 1606-1609Publisher
IEEE
DOI: 10.23919/ICCAS52745.2021.9649875
Keywords
deep neural network; 2D Drawing; image patch; mold parts; industrial product design
Categories
Funding
- Samsung Electronics Global Technology Center
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This paper presents a deep learning-based method for automatically detecting injection mold parts and press mold parts in 3D CAD models of commercial TVs. By converting and cropping the models, alongside using Cascade R-CNN and ResNet-50, the study achieved accurate detection and localization of mold parts, enhancing industrial product design efficiency.
This paper proposes a deep learning-based method to automatically detect the injection mold parts (i.e., hook or boss) and press mold parts (i.e., DPS or Embo) in 3D CAD models of commercial TV. We first converted the 3D CAD models into 2D drawings and cropped them into a smaller image patch for the training efficiency of a deep neural network. Then, we found the position and type of mold parts using Cascade R-CNN and estimated the orientation of the detected mold parts using ResNet-50. Finally, we converted the 2D position of the mold parts to the 3D position of the original image. We obtained detection accuracy of injection mold parts with an average precision (AP) of 84.1% and an average recall (AR) of 91.2% and detection accuracy of press mold parts with an AP of 72.0% and an AR of 87.0%, as well as an orientation accuracy of injection and press mold parts with 94.4% and 92.0%, respectively, which can facilitate faster industrial product design.
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