Wire Arc Additive Manufacturing

The brand new Hybrid Manufacturing technologies is providing much more capability to improve and facilitate the manufacturing of metallic parts that requires special standards, complex shape and higher specifications that can‘t be easily manufactured by convectional manners of manufacturing or it‘s very expensive and time consuming to manufacture these parts using the conventional method at high production rate. The combination of more than one manufacturing technologies in one manufacturing method is called Hybrid Manufacturing Technology. Furthermore, when we combine one of the additive manufacturing with another one or more manufacturing process the resulted process is called Hybrid Additive Manufacturing process. One of the most widely used processes of hybrid additive manufacturing is Hybrid Wire-Arc Additive Manufacturing process which is our main subject that is covered throughout this article. This technology uses wire-arc additive manufacturing WAAM as one of the processes combined together within the scope of the hybrid system. In this article we are going to know more about the hybrid system in manufacturing and about the additive manufacturing technologies in brief. Moreover, within the scope of this article we will learn more about WAAM technology in terms of the classification of this method, the types of materials used for it, the defect that may take place in the part during the usage of this technology and the methods and techniques used in order to improve the final product.

Open Access Article: https://doi.org/10.3390/app11041654

Cold spray is emerging as an additive manufacturing technique, particularly advantageous when high production rate and large build sizes are in demand. To further accelerate tech-nology's industrial maturity, the problem of geometric control must be improved, and a neural network model has emerged to predict additively manufactured geometry. However, limited data on the effect of deposition conditions on geometry growth is often problematic. Therefore, this study presents data-efficient neural network modelling of a single-track profile in cold spray additive manufacturing. Two modelling techniques harnessing prior knowledge or existing model were proposed , and both were found to be effective in achieving the data-efficient development of a neural network model. We also showed that the proposed data-efficient neural network model provided better predictive performance than the previously proposed Gaussian function model and purely data-driven neural network. The results indicate that a neural network model can outperform a widely used mathematical model with data-efficient modelling techniques and be better suited to improving geometric control in cold spray additive manufacturing.

Related works:
(Open Access) Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing - https://doi.org/10.3390/ma12172827

Open Access Article:
https://doi.org/10.3390/ma12172827,

Abstract:
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.

Related works:
(Open Access) Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing - https://doi.org/10.3390/app11041654

Additive Manufacturing (AM) processes intended for large-scale components deposit large volumes of material to shorten process duration. This reduces the resolution of the AM process, which is typically defined by the deposition nozzle size. If the resolution limitation is not considered when designing for Large-Scale Additive Manufacturing (LSAM), difficulties can arise in the manufacturing process, which may require the adaptation of deposition parameters. This work incorporates the nozzle size constraint into Topology Optimisation (TO) in order to generate optimised designs suitable to the process resolution. This article proposes and compares two methods, which are based on existing TO techniques that enable control of minimum and maximum member size, and of minimum cavity size. The first method requires the minimum and maximum member size to be equal to the deposition nozzle size, thus design features of uniform width are obtained. The second method defines the size of solid members sufficiently small for the resulting structure to resemble a structural skeleton, which can be interpreted as the deposition path. Through filtering and projection techniques, the thin structures are thickened according to the chosen nozzle size. Thus, a topology tailored to the deposition nozzle size is obtained along with a deposition proposal. The methods are demonstrated and assessed using 2D and 3D benchmark problems.

Cold spray additive manufacturing is an emerging technology that offers unique advantages, including high production rate, unlimited product size and the ability to process oxygen-sensitive materials. However, dimensional control and accuracy in cold spray additive manufacturing are challenging, which limits its integration into commercial manufacturing systems. These problems originate from the poor understanding of the complex relationship between process parameters and the resulting fabricated geometry. This knowledge gap motivated the development of an accurate predictive model for the geometry of a cold spray track profile to overcome the problems. Recently, a machine learning approach has gained interest in developing the predictive model of such a complex additive manufacturing process due to its superior nonlinear mapping capability, as seen in other manufacturing applications. Nevertheless, such a mapping capability can be realised only with a large amount of experimental data which is often impractical to collect in additive manufacturing applications. This limited data issue has motivated the exploration of a data-efficient machine learning approach suitable for complex process modelling with limited data. Therefore, the objective of this study was to investigate a data-efficient machine learning approach to geometry prediction in cold spray additive manufacturing. The proposed approach was of hybrid modelling framework, incorporating a conventional mathematical Gaussian model into the development and learning process of a data-driven model. We compared to purely mathematical Gaussian and data-driven modelling results and showed that the proposed hybrid modelling approach provided improved predictive accuracy. The findings can contribute to the control and optimisation of the process for shorter production time and the development of build strategy for better as-fabricated surface and dimensional quality control. The approach in this study is also applicable in other deposition-based additive manufacturing technologies such as Wire and Arc Additive Manufacturing.

For Further Information:
(Open Access) Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing - https://doi.org/10.3390/app11041654

Our Related works:
(Open Access) Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing - https://doi.org/10.3390/ma12172827

Cold spray is emerging as an additive manufacturing technique, particularly advantageous when high production rate and large build sizes are in demand. To further accelerate technology’s industrial maturity, the problem of geometric control must be improved, and a neural network model has emerged to predict additively manufactured geometry. However, limited data on the effect of deposition conditions on geometry growth is often problematic. Therefore, this study presents data-efficient neural network modelling of a single-track profile in cold spray additive manufacturing. Two modelling techniques harnessing prior knowledge or existing model were proposed, and both were found to be effective in achieving the data-efficient development of a neural network model. We also showed that the proposed data-efficient neural network model provided better predictive performance than the previously proposed Gaussian function model and purely data-driven neural network. The results indicate that...

The additively fabricated functionally gradient structures can be a potential replacement for conventionally manufactured structures via fusion welding techniques. A SS321/Inconel 625 functionally gradient material was processed by wire arc additive manufacturing (WAAM) process. The WAAM-formed SS321 comprises of equiaxed and columnar structures, while the Inconel 625 consists of dendritic structures. It can be concluded that a very narrow interface was formed between the additively manufactured SS321/Inconel 625 FGM without forming cracks or fissures. Energy-dispersive x-ray spectroscopy (EDS) element maps show a smooth transition of elements at the interface without much segregation while EDS point scan confirmed the presence of laves phase. The electron backscatter diffraction results at the interface region revealed continuous crystallographic growth with large elongated grains in the <001> orientation. Tensile properties were better for SS321 and comparable for Inconel 625 than the wrought alloys, while the interface FGM sample failed on the SS321 side. The micro-hardness steadily changed from 226-195 HV and 272-236 HV in SS321 and Inconel 625, respectively. The WAAM process demonstrates that successful FGM components can be fabricated with multi-material and controlled properties.

Cold spray is emerging as an additive manufacturing technique, particularly advantageous when high production rate and large build sizes are in demand. To further accelerate technology’s industrial maturity, the problem of geometric control must be improved, and a neural network model has emerged to predict additively manufactured geometry. However, limited data on the effect of deposition conditions on geometry growth is often problematic. Therefore, this study presents data-efficient neural network modelling of a single-track profile in cold spray additive manufacturing. Two modelling techniques harnessing prior knowledge or existing model were proposed, and both were found to be effective in achieving the data-efficient development of a neural network model. We also showed that the proposed data-efficient neural network model provided better predictive performance than the previously proposed Gaussian function model and purely data-driven neural network. The results indicate that...

Open Access Article:
https://doi.org/10.3390/ma12172827,

Abstract:
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.

Related works:
(Open Access) Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing - https://doi.org/10.3390/app11041654