System identification and closed-loop control of laser hot-wire directed energy deposition using the parameter-signature-property modeling scheme

Hot-wire directed energy deposition using a laser beam (DED-LB/w) is a method of metal additive manufacturing (AM) that has benefits of high material utilization and deposition rate, but parts manufactured by DED-LB/w suffer from a substantial heat input and undesired surface finish. Hence, monitoring and controlling the process parameters and signatures during the deposition is crucial to ensure the quality of final part properties and geometries. This paper explores the dynamic modeling of the DED-LB/w process and introduces a parameter-signature-property modeling and control approach to enhance the quality of modeling and control of part properties that cannot be measured in situ. The study investigates different process parameters that influence the melt pool width (signature) and bead width (property) in single and multi-layer beads. The proposed modeling approach utilizes a parameter-signature model as F_1 and a signature-property model as F_2. Linear and nonlinear modeling approaches are compared to describe a dynamic relationship between process parameters and a process signature, the melt pool width (F_1). A fully connected artificial neural network is employed to model and predict the final part property, i.e., bead width, based on melt pool signatures (F_2). Finally, the effectiveness and usefulness of the proposed parameter-signature-property modeling is tested and verified by integrating the parameter-signature (F_1) and signature-property (F_2) models in the closed-loop control of the width of the part. Compared with the control loop with only F_1, the proposed method shows clear advantages and bears potential to be applied to control other part properties that cannot be directly measured or monitored in situ.Comment: 28 pages, 14 figures, 4 tables

Additive Manufacturing Modeling and Simulation A Literature Review for Electron Beam Free Form Fabrication

Additive manufacturing is coming into industrial use and has several desirable attributes. Control of the deposition remains a complex challenge, and so this literature review was initiated to capture current modeling efforts in the field of additive manufacturing. This paper summarizes about 10 years of modeling and simulation related to both welding and additive manufacturing. The goals were to learn who is doing what in modeling and simulation, to summarize various approaches taken to create models, and to identify research gaps. Later sections in the report summarize implications for closed-loop-control of the process, implications for local research efforts, and implications for local modeling efforts

Laser Line Scan Characterization of Geometric Profiles in Laser Metal Deposition

Laser Metal Deposition (LMD) is an additive manufacturing process in which material is deposited by blowing powdered metal into a melt pool formed by a laser beam. When fabricating parts, the substrate is subjected to motion control such that the melt pool traces a prescribed path to form each part layer. Advantages of LMD include relatively efficient powder usage, the ability to create functionally-graded parts and the ability to repair high-value parts. The process, however, is sensitive to variations in process parameters and a need for feedback measurements and closed-loop control has been recognized in the literature [1, 2]. To this end, a laser line scanner is being integrated into an LMD system at the Missouri University of Science and Technology. Measurements from the laser line scanner will provide the feedback data necessary for closed-loop control of the process. The work presented here considers characteristics of the laser line scanner as it relates to scanning LMD depositions. Errors associated with the measurement device are described along with digital processing operations designed to remove them. The parameter bead height is extracted from scans for future use in a closed-loop control strategy

Repetitive process control of additive manufacturing with application to laser metal deposition

Additive Manufacturing (AM) is a set of manufacturing processes which has promise in the production of complex, functional structures that cannot be fabricated with conventional manufacturing and the repair of high-value parts. However, a significant challenge to the adoption of additive manufacturing processes to these applications is proper process control. In order to enable closed-loop process control compact models suitable for control design and for describing the layer-by-layer material addition process are needed. This dissertation proposes a two-dimensional modeling and control framework, with an application to a specific metal-based AM process, whereby the deposition of the current layer is affected by both in-layer and layer-to-layer dynamics, both of which are driven by the state of the previous layer. The proposed modeling framework can be used to create two-dimensional dynamic models for the analysis of layer-to-layer stability and as a foundation for the design of layer-to-layer controllers for AM processes. In order to analyze the stability of this class of systems, linear repetitive process results are extended enabling the treatment of the process model as a two-dimensional analog of a discrete time system. For process control, the closed-loop repetitive process is again treated as a two-dimensional analog of a discrete time system for which controllers are designed. The proposed methodologies are applied to a metal-based AM process, Laser Metal Deposition (LMD), which is known to exhibit layer-to-layer unstable behavior and is also of significant interest to high-value manufacturing industries --Abstract, page iii

Symmetry and its application in metal additive manufacturing (MAM)

Additive manufacturing (AM) is proving to be a promising new and economical technique for the manufacture of metal parts. This technique basically consists of depositing material in a more or less precise way until a solid is built. This stage of material deposition allows the acquisition of a part with a quasi-final geometry (considered a Near Net Shape process) with a very high raw material utilization rate. There is a wide variety of different manufacturing techniques for the production of components in metallic materials. Although significant research work has been carried out in recent years, resulting in the wide dissemination of results and presentation of reviews on the subject, this paper seeks to cover the applications of symmetry, and its techniques and principles, to the additive manufacturing of metals.The authors are grateful to the Basque Government for funding the EDISON project, ELKARTEK 2022 (KK-2022/00070)

Additive Manufacturing (AM) of Metallic Alloys

The introduction of metal AM processes in such industrial sectors as the aerospace, automotive, defense, jewelry, medical and tool-making fields, has led to a significant reduction in waste material and in the lead times of the components, innovative designs with higher strength, lower weight, and fewer potential failure points from joining features. This Special Issue on “Additive Manufacturing (AM) of Metallic Alloys” contains a mixture of review articles and original contributions on some problems that limit the wider uptake and exploitation of metals in AM

Wire Arc Additive Manufacturing of Mn4Ni2CrMo Steel: Comparison of Mechanical and Metallographic Properties of PAW and GMAW

Wire arc additive manufacturing, WAAM, is a popular wire-feed additive manufacturing technology that creates components through the deposition of material layer-by-layer. WAAM has become a promising alternative to conventional machining due to its high deposition rate, environmental friendliness and cost competitiveness. In this research work, a comparison is made between two different WAAM technologies, GMAW (gas metal arc welding) and PAW (plasma arc welding). Comparative between processes is centered in the main variations while manufacturing Mn4Ni2CrMo steel walls concerning geometry and process parameters maintaining the same deposition ratio as well as the mechanical and metallographic properties obtained in the walls with both processes, in which the applied energy is significantly different. This study shows that acceptable mechanical characteristics are obtained in both processes compared to the corresponding forging standard for the tested material, values are 23% higher for UTS and 56% for elongation in vertical direction in the PAW process compared to GMAW (no differences in UTS and elongation results for horizontal direction and in Charpy for both directions) and without significant directional effects of the additive manufacturing technology used.This research was funded by BASQUE GOVERNMENT, grant number KK-2018/00115 (ADDISEND, ELKARTEK 2018 programme) and grant number ZE-2017/00038 (HARITIVE, HAZITEK 2017 programme)

Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing.

Cold spray additive manufacturing is an emerging technology that offers the ability to deposit oxygen-sensitive materials and to manufacture large components in the solid state. For further development of the technology, the geometric control of cold sprayed components is fundamental but not yet fully matured. This study presents a neural network predictive modelling of a single-track profile in cold spray additive manufacturing to address the problem. In contrast to previous studies focusing only on key geometric feature predictions, the neural network model was employed to demonstrate its capability of predicting complete track profiles at both normal and off-normal spray angles, resulting in a mean absolute error of 8.3%. We also compared the track profile modelling results against the previously proposed Gaussian model and showed that the neural network model provided comparable predictive accuracy, even outperforming in the predictions at cold spray profile edges. The results indicate that a neural network modelling approach is well suited to cold spray profile prediction and may be used to improve geometric control during additive manufacturing with an appropriate process planning algorithm.This research was funded by CSIRO’s Active Integrated Matter Future Science Platform (AIM FSP)under the testbed number: TB10_WB04