Model predictive control of laser metal deposition

Liu, Yangbo; Wang, Liuping; Brandt, Milan

doi:10.1007/s00170-019-04279-9

Cited by 21 publications

(5 citation statements)

References 20 publications

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“…Calleja et al focused on the process control of feed-rate on laser cladding to track the desired path-feed [28]. Most authors use controllers either from the PID family, such a simple proportional gain K p (P controller) or combination of P, PI, PID [29][30][31][32][33], or a more complex methodology, such as model predictive control (MPC) [34][35][36][37]. However, no publication has been found to apply H-Infinity (a form of norm correlating input-output), H-2 (a different norm), or mixed-mode controller via solving Linear Matrix Inequalities, hence, this is investigated here as an effective means to control a single parameter, that of the maximum observed temperature at the melt-pool area, under uncertainties.…”

Section: State Of the Art On Manufacturing Processes Monitoring And C...mentioning

confidence: 99%

Robust Additive Manufacturing Performance through a Control Oriented Digital Twin

Stavropoulos

Papacharalampopoulos

Michail

et al. 2021

Metals

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The additive manufacturing process control utilizing digital twins is an emerging issue. However, robustness in process performance is still an open aspect, due to uncertainties, e.g., in material properties. To this end, in this work, a digital twin offering uncertainty management and robust process control is designed and implemented. As a process control design method, the Linear Matrix Inequalities are adopted. Within specific uncertainty limits, the performance of the process is proven to be acceptably constant, thus achieving robust additive manufacturing. Variations of the control law are also investigated, in order for the applicability of the control to be demonstrated in different machine architectures. The comparison of proposed controllers is done against a fine-tuned conventional proportional–integral–derivative (PID) and the initial open-loop model for metals manufacturing. As expected, the robust control design achieved a 68% faster response in the settling time metric, while a well-calibrated PID only achieved 38% compared to the initial model.

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Section: State Of the Art On Manufacturing Processes Monitoring And C...mentioning

confidence: 99%

Robust Additive Manufacturing Performance through a Control Oriented Digital Twin

Stavropoulos

Papacharalampopoulos

Michail

et al. 2021

Metals

View full text Add to dashboard Cite

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“…Analytical models have also attracted attention for predicting the W-LDED process, aiming to lower the overall expenses. These models rely heavily on assumptions rather than numerical methods [125,193,[230][231][232].…”

Section: Modeling and Predicting W-lded Processmentioning

confidence: 99%

A Review on Wire-Laser Directed Energy Deposition: Parameter Control, Process Stability, and Future Research Paths

Ghanadi,

Pasebani

2024

JMMP

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Wire-laser directed energy deposition has emerged as a transformative technology in metal additive manufacturing, offering high material deposition efficiency and promoting a cleaner process environment compared to powder processes. This technique has gained attention across diverse industries due to its ability to expedite production and facilitate the repair or replication of valuable components. This work reviews the state-of-the-art in wire-laser directed energy deposition to gain a clear understanding of key process variables and identify challenges affecting process stability. Furthermore, this paper explores modeling and monitoring methods utilized in the literature to enhance the final quality of fabricated parts, thereby minimizing the need for repeated experiments, and reducing material waste. By reviewing existing literature, this paper contributes to advancing the current understanding of wire-laser directed energy deposition technology. It highlights the gaps in the literature while underscoring research needs in wire-laser directed energy deposition.

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“…There are several bead geometry control systems that employ an MPC approach in conventional welding areas (Li & Zhang, 2014;Stoyanov & Bailey, 2017). MPC approaches have also been employed in non-metal AM, powder-based metal AM and conventional gas tungsten arc welding (GTAW) processes to achieve a similar action of layer-by-layer control (Inyang-Udoh et al, 2020;Liu et al, 2019). These approaches are extended in this work by implementing a multi-input multi-output (MIMO) MPC strategy to output welding parameters between each layer of the fabrication process.…”

Section: Introductionmentioning

confidence: 99%

Layer-by-layer model-based adaptive control for wire arc additive manufacturing of thin-wall structures

Polden

et al. 2022

J Intell Manuf

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Improving the geometric accuracy of the deposited component is essential for the wider adoption of wire arc additive manufacturing (WAAM) in industries. This paper introduces an online layer-by-layer controller that operates robustly under various welding conditions to improve the deposition accuracy of the WAAM process. Two control strategies are proposed and evaluated in this work: A PID algorithm and a multi-input multi-output model-predictive control (MPC) algorithm. After each layer of deposition, the deposited geometry is measured using a laser scanner. These measurements are compared against the CAD model, and geometric errors are then compensated by the controller, which generates a new set of welding parameters for the next layer. The MPC algorithm, combined with a linear autoregressive (ARX) modelling process, updates welding parameters between successive layers by minimizing a cost function based on sequences of input variables and predicted responses. Weighting coefficients of the ARX model are trained iteratively throughout the manufacturing process. The performance of the designed control architecture is investigated through both simulation and experiments. Results show that the real-time control performance is improved by increasing the complexity of implemented control algorithm: controlled geometric fluctuations in the test component were reduced by 200% whilst maintaining fluctuations within a 3 mm limit under various welding conditions. In addition, the adaptiveness of designed control strategy is verified by accurately controlling the fabrication of a part with complex geometry.

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Model predictive control of laser metal deposition

Cited by 21 publications

References 20 publications

Robust Additive Manufacturing Performance through a Control Oriented Digital Twin

Robust Additive Manufacturing Performance through a Control Oriented Digital Twin

A Review on Wire-Laser Directed Energy Deposition: Parameter Control, Process Stability, and Future Research Paths

Layer-by-layer model-based adaptive control for wire arc additive manufacturing of thin-wall structures

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