Advancing Additive Manufacturing: A Markov Decision Process Approach for real-time Quality Assurance

Abstract

Markov Decision Process (MDP) construct for additive manufacturing (AM) with real-time quality and precision was introduced in this work. For consistent AM process optimization, stochastic decision-making MDP works best. MDP printing benefits from quality assurance. Instantly adjust settings and materials. MDP optimizes strategy, policy, transition probabilities, and incentives. They improve manufacturing quality and reduce errors and rejects. Temperature and pressure are easily measured; thus, quality issues may be discovered quickly. Sensor data processing and machine learning are needed. This procedure uses two common machine-learning methods:An investigation of regression Sensors is understood.Use control charts to analyze sensor data.

The MDP framework optimizes rules and value functions with value iteration. MDP iteratively learns to adapt to AM process changes, improving product quality. This unique combination of optimization, real-time monitoring, and machine learning strengthens the AM quality control system.