Automatic Detection of Injection and Press Mold Parts on 2D Drawing Using Deep Neural Network

TL;DR

This paper introduces a deep learning approach using Cascade R-CNN and Resnet-50 to automatically detect and determine the orientation of injection and press mold parts in 2D CAD drawings, improving industrial design efficiency.

Contribution

It presents a novel deep neural network pipeline specifically designed for detecting and orienting mold parts in 2D CAD images, with high accuracy metrics.

Findings

Detection accuracy of 84.1% in AP and 91.2% in AR.

Orientation accuracy of 94.4% for injection parts and 92.0% for press parts.

Method facilitates faster and more accurate industrial product design.

Abstract

This paper proposes a method to automatically detect the key feature parts in a CAD of commercial TV and monitor using a deep neural network. We developed a deep learning pipeline that can detect the injection parts such as hook, boss, undercut and press parts such as DPS, Embo-Screwless, Embo-Burring, and EMBO in the 2D CAD drawing images. We first cropped the drawing to a specific size for the training efficiency of a deep neural network. Then, we use Cascade R-CNN to find the position of injection and press parts and use Resnet-50 to predict the orientation of the parts. Finally, we convert the position of the parts found through the cropped image to the position of the original image. As a result, we obtained detection accuracy of injection and press parts with 84.1% in AP (Average Precision), 91.2% in AR(Average Recall), 72.0% in AP, 87.0% in AR, and orientation accuracy of injection and press parts with 94.4% and 92.0%, which can facilitate the faster design in industrial product design.