Adaptive Model Predictive Control for Microstructure Control in Laser Material Processing

Abstract

Laser material processing (LMP) has revolutionized traditional fabrication methods across industries, evolving from laser cutting to encompassing advanced techniques like laser heat treatment (LHT), laser welding, and laser additive manufacturing, enabling precise alteration of material properties and unprecedented design freedom across industries. However, achieving consistent material characteristics remains a significant challenge, particularly in advanced additive manufacturing processes such as laser-directed energy deposition (LDED), where the complex interplay between process parameters and material properties hinders uniform product quality, emphasizing the need for advanced process control strategies.

Conventional control methods like proportional-integral-derivative controllers struggle to anticipate the intricate interactions inherent in LMP processes, making it difficult to control multiple parameters simultaneously. Model-based control strategies, leveraging numerical models, offer promise in providing a comprehensive understanding of process dynamics. However, their practical implementation in real-time control applications is impeded by the computational challenges of numerical models. Overcoming these obstacles is crucial to harnessing the full potential of numerical models for enhanced process control and ensuring consistent, reproducible material characteristics.

In this research, a novel adaptive model predictive control (AMPC) algorithm was developed to address the challenges of ensuring consistent material characteristics in LMP processes. Initially, a two-dimensional (2D) adaptive thermal model was designed for real-time prediction of thermal dynamics during the LHT process, focusing on parameters like peak temperature and spatial cooling rate. Subsequently, a one-dimensional (1D) adaptive thermal model was developed with improvements on efficiency, accuracy, and suitability for control applications compared to the 2D counterpart, focusing on real-time prediction of the temperature distribution and spatial cooling rate. Additionally, a model predictive control (MPC) algorithm utilizing a 2D thermal model was developed for single-input single-output (SISO) peak temperature control during LHT to improve the consistency of hardness and hardening depth. Finally, an AMPC algorithm was designed using the 1D adaptive thermal model for multi-input multi-output (MIMO) temperature and spatial cooling rate control during LDED to achieve consistent material characteristics throughout the process.

A series of LHT and LDED experiments were designed to assess the real-time thermal dynamic prediction capabilities of the models and the real-time control capabilities of the MPC algorithms in LMP. These experiments encompass open-loop LHT and LDED scenarios, targeting the validation of adaptive 1D and 2D thermal models, respectively. Additionally, closed-loop LHT and LDED experiments were designed to investigate the efficacy of the MPC algorithms in controlling one or multiple process parameters to achieve consistent hardness values. The 2D adaptive thermal model effectively adjusted to the thermal dynamic changes in real-time, yielding precise predictions of peak temperature and spatial cooling rates during LHT. Similarly, validations of the 1D adaptive thermal model showcased near-perfect temperature and cooling rate predictions during LDED, along with impressive computational efficiency. Utilizing the SISO MPC algorithm ensured consistent hardness and hardening depth through closed-loop peak temperature control during LHT. Meanwhile, deploying the MIMO AMPC algorithm enabled consistent hardness across the entire deposition process. This was achieved by simultaneously controlling the temperature and spatial cooling rates during the LDED experiments.

In conclusion, this research marks significant advancements in real-time process control within LMP applications. Through the integration of adaptive thermal models and MPC algorithms, the study achieves the crucial objective of ensuring consistent material characteristics in LMP-manufactured parts. The developed AMPC algorithm demonstrates unprecedented levels of control, stability, and reliability. Moreover, its versatility and simplicity extend its applicability beyond LMP processes, enabling adoption in various advanced manufacturing processes utilizing concentrated energy sources. Thus, the AMPC methodology holds the potential to address the crucial need in advanced manufacturing by ensuring consistent and reproducible material characteristics in manufactured parts across the entire industry.