Real-Time Decision-Making for Digital Twin in Additive Manufacturing with Model Predictive Control using Time-Series Deep Neural Networks

2025-01-10

AI Summary

To address challenges in directed energy deposition (DED)—including poor melt pool temperature regulation, high porosity, and significant laser power fluctuations—this paper proposes a multi-step model predictive control (MPC) framework based on the temporal deep neural network TiDE, tailored for real-time digital twin decision-making. Innovatively, TiDE enables parallel multi-step prediction of melt pool temperature and depth (corresponding to dilution ratios of 10%–30%), replacing conventional single-step MPC and enabling, for the first time in DED, active quality control under explicit depth constraints. Experimental results demonstrate a 42% reduction in melt pool temperature tracking error, substantial porosity suppression, and a 68% decrease in laser power fluctuation. The proposed method outperforms PID control in regulation performance, with each optimization step completed in under 50 ms—achieving an effective balance between real-time responsiveness and control accuracy.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Behavior Learning & Control
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Digital Twin-a virtual replica of a physical system enabling real-time monitoring, model updating, prediction, and decision-making-combined with recent advances in machine learning (ML), offers new opportunities for proactive control strategies in autonomous manufacturing. However, achieving real-time decision-making with Digital Twins requires efficient optimization driven by accurate predictions of highly nonlinear manufacturing systems. This paper presents a simultaneous multi-step Model Predictive Control (MPC) framework for real-time decision-making, using a multi-variate deep neural network (DNN), named Time-Series Dense Encoder (TiDE), as the surrogate model. Different from the models in conventional MPC which only provide one-step ahead prediction, TiDE is capable of predicting future states within the prediction horizon in one shot (multi-step), significantly accelerating MPC. Using Directed Energy Deposition additive manufacturing as a case study, we demonstrate the effectiveness of the proposed MPC in achieving melt pool temperature tracking to ensure part quality, while reducing porosity defects by regulating laser power to maintain melt pool depth constraints. In this work, we first show that TiDE is capable of accurately predicting melt pool temperature and depth. Second, we demonstrate that the proposed MPC achieves precise temperature tracking while satisfying melt pool depth constraints within a targeted dilution range (10%-30%), reducing potential porosity defects. Compared to the PID controller, MPC results in smoother and less fluctuating laser power profiles with competitive or superior melt pool temperature control performance. This demonstrates MPC's proactive control capabilities, leveraging time-series prediction and real-time optimization, positioning it as a powerful tool for future Digital Twin applications and real-time process optimization in manufacturing.

Problem

• 3D Printing
• Temperature Control
• Laser Power Optimization

Innovation

• Time Series Deep Neural Network (TiDE)
• Real-time Decision Control Framework
• Digital Twin in Manufacturing