卷积神经网络
3D打印
计算机科学
过程(计算)
人工智能
质量(理念)
钥匙(锁)
特征(语言学)
人工神经网络
产品(数学)
填充
计算机视觉
机器学习
工程制图
工业工程
深度学习
工程类
机械工程
结构工程
语言学
认识论
操作系统
哲学
计算机安全
数学
几何学
作者
Mohammad Farhan Khan,Aftaab Alam,Mohammad Ateeb Siddiqui,Mohammad Saad Alam,Yasser Rafat,Nehal Salik,Ibrahim Alsaidan
标识
DOI:10.1016/j.matpr.2020.10.482
摘要
3D printing or additive manufacturing is one of the key aspects of industry 4.0. However, 3D printing technology has its vulnerabilities due to the defects that develop for various reasons. This project focuses to develop a Convolutional Neural Network (CNN)-Deep Learning model to detect real-time malicious defects to prevent the production losses and reduce human involvement for quality checks. The method proposed here is based on feature extraction of geometrical anomalies occurring in infill patterns due to inconsistent extrusion, weak infills, lack of supports, or sagging and compare it to the features of a perfect 3D print. This approach is built on the concepts of image classification and computer vision using machine learning, which is an extremely popular technology because of the availability of datasets, monitoring systems, and the ability to detect causal relationships of defects. To check the quality of the parts, an integrated camera with the 3D printer captures images at regular intervals and process it using the CNN model. The results of this project are a more optimized and automated 3D printing process with the potential to solve the most widespread problem of product variability in 3D printing.
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