Electronic Components Provenance Assurance Through AI and Big Data in Assembly Processes – Eyal Weiss
This paper presents a groundbreaking method for real-time authentication of electronic components during the assembly process. Leveraging images captured by pick-and-place machines, our approach
AI-Powered Real-Time Inspection for Electronic Component Assembly
This paper introduces an innovative approach to enhancing the quality of electronic component assembly through real-time, inline inspection utilizing AI-powered deep learning techniques. The
Advancements in Electronic Component Assembly: Real-Time AI-Driven Inspection Techniques | Eyal Weiss
Advancements in Electronic Component Assembly: Real-Time AI-Driven Inspection Techniques Abstract: This study presents an advanced methodology for improving electronic assembly quality through
Impact of Component Quality on Electronic Product Reliability and Environmental Sustainability | Preprint
Abstract: The electronics industry is a significant contributor to environmental challenges, generating approximately 50 million tons of electronic waste annually and high levels of carbon
Real-Time Defect Detection in Electronic Components during Assembly through Deep Learning
Eyal Weiss * , Shir Caplan, Kobi Horn and Moshe Sharabi Technology Department, Cybord.ai, Tel-Aviv 6744332, Israel * Correspondence: eyal.w@cybord.ai Abstract: This paper introduces a
Detecting Corrosion to Prevent Cracks in MLCCs with AI
Abstract The electronics industry faces a challenge posed by cracks in multilayer ceramic capacitors (MLCC), which can undermine device reliability and longevity. In this study, we
AI-Enhanced Assessment of Component Degradation Impact on Industrial Electronic Product Reliability
Abstract — The electronics industry has a significant environmental impact due to high levels of carbon dioxide emissions and electronic waste generated during the production of electronic