工业视觉检测与TinyML高性能质量控制

IF 1.6 4区工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Instrumentation & Measurement Magazine Pub Date : 2023-11-01 DOI:10.1109/mim.2023.10292593

Andrea Albanese, Davide Brunelli

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引用次数: 0

摘要

在工业过程中，工件的预测性维护或自动光学分析是确保低成本高质量产品的基础。然而，这一步仍然是由复杂的系统或人工操作完成的。用低成本的解决方案自动化这一过程，同时保持高产品质量是工业物联网(IIoT)最具挑战性的目标之一。工业物联网促进了一种基于自动化的生产模式，它使用机器数据来实现更快、更灵活、更高效的生产线[1]，从而使企业以更低的成本生产出更高质量的产品。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Industrial Visual Inspection with TinyML for High-Performance Quality Control

In industrial processes, predictive maintenance or automated optical analysis of artifacts is fundamental to ensure high-quality products with low costs. However, this step is still done by sophisticated systems or human operators. Automating this process with low-cost solutions while keeping high product quality is one of the most challenging goals of the Industrial Internet of Things (IIoT). IIoT fosters an automation-based production model that uses machine data to enable faster, more flexible, and more efficient production lines [1], leading companies to produce higher-quality goods at lower costs.

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来源期刊

IEEE Instrumentation & Measurement Magazine 工程技术-工程：电子与电气

CiteScore

4.20

自引率

4.80%

发文量

147

审稿时长

>12 weeks

期刊介绍： IEEE Instrumentation & Measurement Magazine is a bimonthly publication. It publishes in February, April, June, August, October, and December of each year. The magazine covers a wide variety of topics in instrumentation, measurement, and systems that measure or instrument equipment or other systems. The magazine has the goal of providing readable introductions and overviews of technology in instrumentation and measurement to a wide engineering audience. It does this through articles, tutorials, columns, and departments. Its goal is to cross disciplines to encourage further research and development in instrumentation and measurement.