AI-Powered Next-Gen PCB Manufacturing: Advancing Green Processes for Sustainable Technology

Abstract

The rapid expansion of electronics manufacturing has intensified environmental concerns associated with conventional printed circuit board (PCB) fabrication, including excessive energy consumption, hazardous chemical usage, and substantial material waste. In response to these challenges, this study proposes an AI-powered next-generation PCB manufacturing framework aimed at advancing green processes for sustainable technology development. The proposed approach integrates artificial intelligence techniques—such as machine learning, predictive analytics, and intelligent process control—into key stages of PCB production to optimize resource utilization and minimize environmental impact. By leveraging real-time data from sensors and manufacturing execution systems, AI models enable adaptive control of chemical dosing, energy usage, and process parameters, thereby reducing emissions, water consumption, and defect rates. A comprehensive methodology combining experimental evaluation and sustainability metrics is employed to assess system performance. The results demonstrate significant improvements in manufacturing efficiency, with measurable reductions in energy consumption, material waste, and carbon footprint when compared to conventional PCB fabrication practices. Furthermore, the study highlights the role of AI-driven decision-making in supporting eco-friendly materials selection and life cycle assessment of PCBs. The findings confirm that AI-enabled green PCB manufacturing not only enhances production quality and operational efficiency but also aligns with global sustainability goals and Industry 4.0 principles.