Project

Sampled-data Control Design for Smart Additive Manufacturing

Goal: A fundamental challenge in digital control arises when the controlled plant is subjected to a fast process/disturbance dynamics but is only equipped with a relatively slow sensor. Such intrinsic difficulties are, however, commonly encountered in many novel applications, such as laser- and electron-beam-based additive manufacturing. This research investigates fundamental limitations and solutions in sampled-data control.

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Project log

Dan Wang
added a research item
Despite the advantages and emerging applications, broader adoption of powder bed fusion (PBF) additive manufacturing is challenged by insufficient reliability and in-process variations. Finite element modeling and control-oriented modeling have been shown to be effective for predicting and engineering part qualities in PBF. This paper first builds a finite element model (FEM) of the thermal fields to look into the convoluted thermal interactions during the PBF process. Using the FEM data, we identify a novel surrogate system model from the laser power to the melt pool width. Linking a linear model with a memoryless nonlinear sub-model, we develop a physics-based Hammerstein model that captures the complex spatiotemporal thermomechanical dynamics. We verify the accuracy of the Hammerstein model using the FEM and prove that the linearized model is only a representation of the Hammerstein model around the equilibrium point. Along the way, we conduct the stability and robustness analyses and formalize the Hammerstein model to facilitate the subsequent control designs.
Dan Wang
added 2 research items
A high-precision additive manufacturing process, powder bed fusion (PBF) has enabled unmatched agile manufacturing of a wide range of products from engine components to medical implants. While finite element modeling and closed-loop control have been identified key for predicting and engineering part qualities in PBF, existing results in each realm are developed in opposite computational architectures wildly different in time scale. This paper builds a first-instance closed-loop simulation framework by integrating high-fidelity finite element modeling with feedback controls originally developed for general mechatronics systems. By utilizing the output signals (e.g., melt pool width) retrieved from the finite element model (FEM) to update directly the control signals (e.g., laser power) sent to the model, the proposed closed-loop framework enables testing the limits of advanced controls in PBF and surveying the parameter space fully to generate more predictable part qualities. Along the course of formulating the framework, we verify the FEM by comparing its results with experimental and analytical solutions and then use the FEM to understand the melt-pool evolution induced by the in-and cross-layer thermomechan-ical interactions. From there, we build a repetitive control algorithm to attenuate variations of the melt pool width.
Dan Wang
added a research item
Despite the advantages and emerging applications, broader adoption of powder bed fusion (PBF) additive manufacturing is challenged by insufficient reliability and in-process variations. Finite element modeling and control-oriented modeling have been identified fundamental for predicting and engineering part qualities in PBF. This paper first builds a finite element model (FEM) of the thermal fields to look into the convoluted thermal interactions during the PBF process. Using the FEM data, we identify a novel surrogate system model from the laser power to the melt pool width. Linking a linearized model with a memory-less nonlinear submodel, we develop a physics-based Hammer-stein model that captures the complex spatiotemporal thermomechanical dynamics. We verify the accuracy of the Hammerstein model using the FEM and prove that the linearized model is only a representation of the Hammerstein model around the equilibrium point. Along the way, we conduct the stability and robustness analyses and formalize the Hammerstein model to facilitate the subsequent control designs.
Dan Wang
added a research item
Stably inverting a dynamic system model is fundamental to subsequent servo designs. Current inversion techniques have provided effective model matching for feedforward controls. However, when the inverse models are to be implemented in feedback systems, additional considerations are demanded for assuring causality, robustness, and stability under closed-loop constraints. To bridge the gap between accurate model approximations and robust feedback performances, this paper provides a new treatment of unstable zeros in inverse design. We provide first an intuitive pole-zero-map-based inverse tuning to verify the basic principle of the unstable-zero treatment. From there, for general nonminimum-phase and unstable systems, we propose an optimal inversion algorithm that can attain model accuracy at the frequency regions of interest while constraining noise amplification elsewhere to guarantee system robustness. Along the way, we also provide a modern review of model inversion techniques. The proposed algorithm is validated on motion control systems and complex high-order systems.
Dan Wang
added 2 research items
Powder bed fusion (PBF) additive manufacturing has enabled unmatched agile manufacturing of a wide range of products from engine components to medical implants. While high-fidelity finite element modeling and feedback control have been identified key for predicting and engineering part qualities in PBF, existing results in each realm are developed in opposite computational architectures wildly different in time scale. Integrating both realms, this paper builds a first-instance closed-loop simulation framework by utilizing the output signals retrieved from the finite element model (FEM) to directly update the control signals sent to the model. The proposed closed-loop simulation enables testing the limits of advanced controls in PBF and surveying the parameter space fully to generate more predictable part qualities. Along the course of formulating the framework, we verify the FEM by comparing its results with experimental and analytical solutions and then use the FEM to understand the melt-pool evolution induced by the in-layer thermomechanical interactions. From there, we build a repetitive control algorithm to greatly attenuate variations of the melt pool width.
Dan Wang
added a research item
Although laser-based additive manufacturing (AM) has enabled unprecedented fabrication of complex parts directly from digital models, broader adoption of the technology remains challenged by insufficient reliability and in-process variations. In pursuit of assuring quality in the selective laser sintering (SLS) AM, this paper builds a modeling and control framework of the key thermodynamic interactions between the laser source and the materials to be processed. First, we develop a three-dimensional finite element simulation to understand the important features of the melt-pool evolution for designing sensing and feedback algorithms. We explore how the temperature field is affected by hatch spacing and thermal properties that are temperature-dependent. Based on high-performance computer simulation and experimentation , we then validate the existence and effect of periodic disturbances induced by the repetitive in-and cross-layer thermomechanical interactions. From there, we identify the system model from the laser power to the melt pool width and build a repetitive control algorithm to greatly attenuate variations of the melt pool geometry. 1 Introduction Different from conventional subtractive machining, additive manufacturing (AM, also called 3D printing) builds up a part from its digital model by adding together materials layer by layer. This paper studies laser-based AM technologies , with a focus on the selective laser sintering (SLS) subcategory. This AM technology applies laser beams as the energy source to melt and join powder materials. A typical workpiece is built from many thousands of thin layers. Within each layer, the laser beam is controlled to follow tra-jectories predefined by the part geometry in a slicing process. After the sintering of one layer is finished, a new thin layer of powder is spread on top, and then another cycle begins. SLS accommodates a broad range of materials (e.g., metals , polymers, and ceramics) and can build customized parts with complex features and high accuracy requirements. Despite the advantages and continuously emerging applications, * Corresponding author broader adoption of the technology remains challenged by insufficient reliability and in-process variations. These variations are induced by, for example, environmental vibrations, powder recycling, imperfect laser-material interactions, and mechanical wears [1-3]. Predictive modeling and process control have thus been key for mitigating the variations and enhancing the energy deposition in SLS. Several existing strategies employ numerical and control-oriented modeling to understand SLS and other laser-based AM processes such as laser metal deposition. In numerical modeling, most researchers adopt finite element analysis (FEA) to investigate thermal fields of the powder bed and substrate, melt pool geometries, and mechanical properties of the printed parts in response of various scanning patterns, scan speeds, number of lasers, and over-hanging structures [4-6]. In control-oriented modeling, current researches often implement low-order system models obtained from system identification techniques, taking laser power or scan speed as the input and melt pool temperature or geometry as the output [2, 7-9]. Furthermore, [8, 10] connect a nonlinear memoryless submodel in series with the linear system model to account for nonlinearities. [9] builds a spatial-domain Hammerstein model to identify the coupled repetitive in-and cross-layer dynamics. The Rosenthal equations give the analytical solutions for a moving laser source in thick and thin plates and have been used to predict the temperature distribution of the powder bed [11-14]. Based on the reduced-order models, existing researches [2, 15, 16] apply PID control to regulate the process parameters and reduce the in-process errors. From there, [17] adds a feedforward path for tracking improvement. Other controllers have also been shown capable in improving the dimensional accuracy of the printed parts, including but not limited to the sliding mode controller [10], predictive controller [7], and iterative learning controller [18]. Note that except for [2], which was developed for SLS, all the other reviewed controllers were tailored for laser metal deposition. Stepping beyond current architectures, this study builds 1 Copyright c by ASME
Dan Wang
added 4 research items
This paper studies repetitive control (RC) algorithms to advance the quality of repetitive energy deposition in laser-based additive manufacturing (AM). An intrinsic limitation appears in discrete-time RC when the period of the exogenous signal is not an integer multiple of the sampling time. Such a challenge hampers high-performance applications of RC to laser-based AM because periodicity of the exogenous signal has no guarantees to comply with the sampling rate of molten-pool sensors. This paper investigates three RC algorithms to address such fractional-order RC cases. A wide-band RC and a quasi RC apply the nearest integer approximation of the period, yielding overdetermined and partial attenuation of the periodic disturbance. A new multirate RC generates high-gain control signals exactly at the fundamental frequency and its harmonics. Experimentation on a dual-axis galvo scanner in laser-based AM compares the effectiveness of different algorithms and reveals fundamental benefits of the proposed multirate RC.
Dan Wang
added a research item
This paper discusses fractional-order repetitive control (RC) to advance the quality of periodic energy deposition in laser-based additive manufacturing (AM). It addresses an intrinsic RC limitation when the exogenous signal frequency cannot divide the sampling frequency of the sensor, e.g., in imaging-based control of fast laser-material interaction in AM. Three RC designs are proposed to address such fractional-order repetitive processes. In particular, a new multirate RC provides superior performance gains by generating high-gain control exactly at the fundamental and harmonic frequencies of exogenous signals. Experimentation on a galvo laser scanner in AM validates effectiveness of the designs.
Dan Wang
added a research item
A fundamental challenge in sampled-data control arises when a continuous-time plant is subject to disturbances that possess significant frequency components beyond the Nyquist frequency of the feedback sensor. Such intrinsic difficulties create formidable barriers for fast high-performance controls in modern and emerging technologies such as additive manufacturing and vision servo, where the update speed of sensors is low compared to the dynamics of the plant. This paper analyzes spectral properties of closed-loop signals under such scenarios, with a focus on mechatronic systems. We propose a spectral analysis method that provides new understanding of the time- and frequency-domain sampled-data performance. Along the course of uncovering spectral details in such beyond-Nyquist controls, we also report a fundamental understanding on the infeasibility of single-rate high-gain feedback to reject disturbances not only beyond but also below the Nyquist frequency. New metrics and tools are then proposed to systematically quantify the limit of performance. Validation and practical implications of the limitations are provided with experimental case studies performed on a precision mirror galvanometer platform for laser scanning.
Dan Wang
added a research item
Many servo systems require micro/nano-level positioning accuracy. This requirement sets a number of challenges from the viewpoint of sensing, actuation, and control algorithms. This article considers control algorithms for precision positioning. We examine how prior knowledge about the parameterization of control structure and the disturbance spectrum should be utilized in the design of control algorithms. An outer-loop inverse-based Youla–Kucera parameterization is built in the article. The presented algorithms are evaluated on a tutorial example of a galvo scanner system.
Dan Wang
added a project goal
A fundamental challenge in digital control arises when the controlled plant is subjected to a fast process/disturbance dynamics but is only equipped with a relatively slow sensor. Such intrinsic difficulties are, however, commonly encountered in many novel applications, such as laser- and electron-beam-based additive manufacturing. This research investigates fundamental limitations and solutions in sampled-data control.
 
Dan Wang
added a research item
A fundamental challenge in digital and sampled-data control arises when the continuous-time plant is subject to fast disturbances that possess significant frequency components beyond Nyquist frequency. Such intrinsic difficulties are more and more encountered in modern manufacturing applications, where the measurement speed of the sensor is physically limited compared to the plant dynamics. The paper analyzes the spectral properties of the closed-loop signals under such scenarios, and uncovers several fundamental limitations in the process.