AI-Powered Predictive Maintenance System for PCB Manufacturing

Our startup is positioned at the forefront of electronics manufacturing, focusing on producing high-quality printed circuit boards (PCBs) for a diverse clientele. We are experiencing challenges with unexpected equipment failures which cause significant production downtime and increased operational costs. To combat this, we want to develop an AI-driven predictive maintenance system that utilizes computer vision and machine learning techniques. This system will continuously monitor equipment health and predict potential failures before they occur. The project will involve deploying Computer Vision techniques using YOLO and other deep learning models to analyze visual data from manufacturing equipment. Predictive analytics powered by TensorFlow and PyTorch will process this data to forecast wear and tear, while Edge AI will enable real-time data processing. The goal is to proactively service machinery and prevent unplanned production halts. We are looking for a freelancer who can deliver a robust, scalable solution within a 4-6 weeks timeline. The ideal candidate will have expertise in OpenAI API for integrating intelligent decision-making and should be proficient with NLP technologies from Hugging Face and Pinecone for data processing and analysis. Experience in deploying AI solutions in industrial settings is highly desirable.