Tool-Path Problem in Direct Energy Deposition Metal-Additive Manufacturing: Sequence Strategy Generation

Murua, Maialen; Suárez, Alejandro Rosete; Galar, Diego; Santana, Roberto

doi:10.1109/access.2020.2994748

Cited by 6 publications

(3 citation statements)

References 49 publications

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“…Alternatively, the optimal tool path to reduce build time for a part with a predefined orientation was obtained from multiattribute optimization with evolutionary algorithms. [158] However, having the optimal tool path for reduced support material and build time does not guarantee a perfect build, due to inevitable uncertainties in AM processes. While such uncertainties cannot be effectively reduced yet, uncertainties in material deposition and solidification were predicted using CNNs when specific process parameters and tool paths were provided.…”

Section: Tool Path Planning To Reduce Print Time and Improve Dimensional Accuracymentioning

confidence: 99%

Data‐Driven Approaches Toward Smarter Additive Manufacturing

Tian

Bustillos

et al. 2021

Advanced Intelligent Systems

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The latest industrial revolution, Industry 4.0, is driven by the emergence of digital manufacturing and, most notably, additive manufacturing (AM) technologies. The simultaneous material and structure forming in AM broadens the material and structural design space. This expanded design space holds a great potential in creating improved engineering materials and products that attract growing interests from both academia and industry. A major aspect of this growing interest is reflected in the increased adaptation of data‐driven tools that accelerate the exploration of the vast design space in AM. Herein, the integration of data‐driven tools in various aspects of AM is reviewed, from materials design in AM (i.e., homogeneous and composite material design) to structure design for AM (i.e., topology optimization). The optimization of AM tool path using machine learning for producing best‐quality AM products with optimal material and structure is also discussed. Finally, the perspectives on the future development of holistically integrated frameworks of AM and data‐driven methods are provided.

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Section: Tool Path Planning To Reduce Print Time and Improve Dimensional Accuracymentioning

confidence: 99%

Data‐Driven Approaches Toward Smarter Additive Manufacturing

Tian

Bustillos

et al. 2021

Advanced Intelligent Systems

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“…The melted powder is deposited in the molten pool area and forms the deposition layers after cooling and solidification. The deposition layers incrementally grow in a predetermined pattern through additive formation [1]. This technology possesses high flexibility and efficiency, reducing energy consumption and industrial costs.…”

Section: Introductionmentioning

confidence: 99%

Residual Stress and Distortion Prediction for Laser Directed Energy Deposition Based on Cyclic Heat Transfer Model

Chai,

Li,

Miao

et al. 2024

IEEE Access

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Predicting the residual stress and distortion caused by inhomogeneous temperature fields in the laser directed energy deposition (LDED) process is a challenging task. This study proposes a novel thermodynamic coupling simulation method based on the cyclic heat transfer model to accurately predict temperature, stress, and distortion evolution during the deposition process. The model effectively calculates the layer-by-layer superposition of thermal effects and cyclic accumulation of thermal stress during the deposition process, leading to improved prediction accuracy for temperature, residual stress, and distortion. Initially, the heat source model, the cyclic heat transfer model, and the thermoelastic matrix are established. The thermoelastic constitutive equation and the equilibrium differential equation are formulated to capture the actual process characteristics of the LDED accurately in order to achieve the thermodynamic coupling solution. Then, numerical simulations are performed on a typical model specimen, with simulation parameters consistent with the actual deposition parameters. Finally, the predicted results are validated through actual deposition experiments, and the temperature, stress, and distortion history are analyzed. The results demonstrate that the cyclic thermodynamic coupling model proposed in this study can effectively predict the deposited components' temperature, residual stress, and distortion evolution. This study establishes a crucial foundation for achieving precision and performance control in the deposition process and reducing residual stress and distortion in the components.INDEX TERMS Laser directed energy deposition (LDED), cyclic heat transfer model, thermodynamic coupling, residual stress, distortion.

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“…18 Other studies also integrate qualities throughout the lifecycle of the piece, such as useful life, functionality, customer satisfaction, safety, reliability, etc. 19 Finally, the integration of the process variable is sought, so that the designer adjusts the models to the process in use, to make it more effective and productive, including the feasibility of its use with the material to be processed 9 ; therefore, computer aided design (CAD) tools have been improved, by adapting them to DfAM. 20 The adaption of computer aided manufacturing (CAM) toolboxes of current software to AM processes that are under development is another important area of study, adding knowledge of the process leading to the most advantageous path toward the scaling-up of the parts for manufacture.…”

Section: Introductionmentioning

confidence: 99%

Wire Arc Additive Manufacturing Process for Topologically Optimized Aeronautical Fixtures

Veiga

Suárez

Aldalur

et al. 2023

3D Printing and Additive Manufacturing

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Additive manufacturing (AM) technologies in metallic materials have experienced significant growth over recent decades. Concepts such as design for additive manufacturing have gained great relevance, due to their flexibility and capacity to generate complex geometries with AM technologies. These new design paradigms make it possible to save on material costs oriented toward more sustainable and green manufacturing. On the one hand, the high deposition rates of wire arc additive manufacturing (WAAM) stand out among the AM technologies, but on the other hand, WAAM is not as flexible when it comes to generating complex geometries. A methodology is presented in this study for the topological optimization of an aeronautical part and its adaptation, by means of computer aided manufacturing, for WAAM manufacturing of aeronautical tooling with the objective of producing a lighter part in a more sustainable manner.

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Tool-Path Problem in Direct Energy Deposition Metal-Additive Manufacturing: Sequence Strategy Generation

Cited by 6 publications

References 49 publications

Data‐Driven Approaches Toward Smarter Additive Manufacturing

Data‐Driven Approaches Toward Smarter Additive Manufacturing

Residual Stress and Distortion Prediction for Laser Directed Energy Deposition Based on Cyclic Heat Transfer Model

Wire Arc Additive Manufacturing Process for Topologically Optimized Aeronautical Fixtures

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