عنوان	Utilizing and Implementing Machine Learning in the Electronics Industry
نوع پژوهش	مقاله چاپ‌شده در مجلات علمی
کلیدواژه‌ها	Machine Learning, Electronics Industry, Predictive Maintenance, Quality Control, Defect Detection, PCB Manufacturing
چکیده	The electronics industry is undergoing a significant transformation due to the integration of machine learning (ML) techniques. ML algorithms enhance various aspects of electronics manufacturing, including predictive maintenance, quality control, defect detection, and supply chain optimization. This paper explores the applications of ML in the electronics sector, reviews existing literature, and presents a methodological framework for implementing ML solutions. A case study on defect detection in printed circuit board (PCB) manufacturing is discussed, demonstrating how ML models improve accuracy and efficiency. The results indicate that ML-driven approaches reduce production costs and enhance product reliability. The study concludes with recommendations for future research and industry adoption.
پژوهشگران	سعید پوراکبری لیسار (نفر اول)، هادی جهانی راد (نفر دوم)