Machine Learning In Production Engineering: A Comprehensive Review

Abstract

The study examines how machine learning (ML) methods can be incorporated into production engineering practices. The paper highlights data preprocessing and cleaning as essential steps to maintain data quality and reliability for ML applications. The review shows the production environment challenges that include missing data values and the presence of outliers along with data inconsistencies. The text explains how advanced automation techniques decrease human involvement while improving feature extraction methods, which produce uniform features across different manufacturing systems. The paper emphasizes that effective model deployment relies on rigorous data engineering pipelines that perform comprehensive data ingestion, transformation, and feature engineering. The review intends to explore the existing ML applications within production engineering while identifying key practices that enable model readiness and reliability.