DVQI: A Multi-task, Hardware-integrated Artificial Intelligence System for Automated Visual Inspection in Electronics Manufacturing

As electronics manufacturers continue to face pressure to increase production efficiency amid difficulties with supply chains and labour shortages, many printed circuit board assembly (PCBA) manufacturers have begun to invest in automation and technological innovations to remain competitive. One such method is to leverage artificial intelligence (AI) to greatly augment existing manufacturing processes. In this paper, we present the DarwinAI Visual Quality Inspection (DVQI) system, a hardware-integration artificial intelligence system for the automated inspection of printed circuit board assembly defects in an electronics manufacturing environment. The DVQI system enables multi-task inspection via minimal programming and setup for manufacturing engineers while improving cycle time relative to manual inspection. We also present a case study of the deployed DVQI system's performance and impact for a top electronics manufacturer.

No comments: Addressing commentary sections in websites' analyses

Removing or extracting the commentary sections from a series of websites is a tedious task, as no standard way to code them is widely adopted. This operation is thus very rarely performed. In this paper, we show that these commentary sections can induce significant biases in the analyses, especially in the case of controversial Highlights $\bullet$ Commentary sections can induce biases in the analysis of websites' contents $\bullet$ Analyzing these sections can be interesting per se. $\bullet$ We illustrate these points using a corpus of anti-vaccine websites. $\bullet$ We provide guidelines to remove or extract these sections.