Sundaram Sarvesh, Zeid Abe
College of Engineering, Northeastern University, Boston, MA 02135, USA.
Micromachines (Basel). 2023 Feb 27;14(3):570. doi: 10.3390/mi14030570.
In today's era, monitoring the health of the manufacturing environment has become essential in order to prevent unforeseen repairs, shutdowns, and to be able to detect defective products that could incur big losses. Data-driven techniques and advancements in sensor technology with Internet of the Things (IoT) have made real-time tracking of systems a reality. The health of a product can also be continuously assessed throughout the manufacturing lifecycle by using Quality Control (QC) measures. Quality inspection is one of the critical processes in which the product is evaluated and deemed acceptable or rejected. The visual inspection or final inspection process involves a human operator sensorily examining the product to ascertain its status. However, there are several factors that impact the visual inspection process resulting in an overall inspection accuracy of around 80% in the industry. With the goal of 100% inspection in advanced manufacturing systems, manual visual inspection is both time-consuming and costly. Computer Vision (CV) based algorithms have helped in automating parts of the visual inspection process, but there are still unaddressed challenges. This paper presents an Artificial Intelligence (AI) based approach to the visual inspection process by using Deep Learning (DL). The approach includes a custom Convolutional Neural Network (CNN) for inspection and a computer application that can be deployed on the shop floor to make the inspection process user-friendly. The inspection accuracy for the proposed model is 99.86% on image data of casting products.
在当今时代，监测制造环境的健康状况对于防止意外维修、停机以及检测可能造成重大损失的缺陷产品至关重要。数据驱动技术以及物联网（IoT）传感器技术的进步使得系统的实时跟踪成为现实。通过使用质量控制（QC）措施，还可以在整个制造生命周期中持续评估产品的健康状况。质量检验是评估产品并判定其合格或不合格的关键流程之一。目视检查或最终检查过程涉及人工操作员通过感官检查产品以确定其状态。然而，有几个因素会影响目视检查过程，导致该行业的整体检查准确率约为80%。在先进制造系统中，以100%检查为目标，人工目视检查既耗时又成本高昂。基于计算机视觉（CV）的算法有助于实现目视检查过程的部分自动化，但仍存在未解决的挑战。本文提出了一种基于人工智能（AI）的深度学习（DL）目视检查方法。该方法包括用于检查的定制卷积神经网络（CNN）以及可部署在车间以实现检查过程用户友好的计算机应用程序。在所提出模型对铸造产品图像数据的检查准确率为99.86%。
