Process planning for robotic wire ARC additive manufacturing

Robotic Wire Arc Additive Manufacturing (WAAM) refers to a class of additive manufacturing processes that builds parts from 3D CAD models by joining materials layerupon- layer, as opposed to conventional subtractive manufacturing technologies. Over the past half century, a significant amount of work has been done to develop the capability to produce parts from weld deposits through the additive approach. However, a fully automated CAD-topart additive manufacturing (AM) system that incorporates an arc welding process has yet to be developed. The missing link is an automated process planning methodology that can generate robotic welding paths directly from CAD models based on various process models. The development of such a highly integrated process planning method for WAAM is the focus of this thesis

Realization of the true 3D printing using multi directional wire and arc additive manufacturing

Robotic wire and arc based additive manufacturing has been used in fabricating of metallic parts owing to its advantages of lower capital investment, higher deposition rates, and better material properties. Although many achievements have been made, the build direction of Wire Arc Additive Manufacturing (WAAM) is still limited in the vertical up direction, resulting in extra supporting structure usage while fabricating metallic parts with overhanging features. Thus, the current WAAM technology should be also called 2.5D printing rather than 3D printing. In order to simplify the deposition set up and increase the flexibility of the WAAM process, it is necessary to find an alternative approach for the deposition of ‘overhangs’ in a true 3D space. This dissertation attempts to realize true 3D printing by developing a novel multi directional WAAM system using robotic Gas Metal Arc Welding (GMAW) to additively manufacture metal components in multiple directions. Several key steps including process development, welding defect investigation and avoidance, and robot path generation are presented in this study

Towards the Fabrication Strategies for Intelligent Wire Arc Additive Manufacturing of Wire Structures from CAD Input to Finished Product

With the increasing demand for freedom of part design in the industry, additive manufacturing (AM) has become a vital fabrication process for manufacturing metallic workpieces with high geometrical complexity. Among all metal additive manufacturing technologies, wire arc additive manufacturing (WAAM), which uses gas metal arc welding (GMAW), is gaining popularity for rapid prototyping of sizeable metallic workpieces due to its high deposition rate, low processing conditions limit, and environmental friendliness. In recent years, WAAM has been developed synergistically with industrial robotic systems or CNC machining centers, enabling multi-axis free-form deposition in 3D space. On this basis, the current research of WAAM has gradually focused on fabricating strut-based wire structures to enhance its capability of producing low-fidelity workpieces with high spatial complexity. As a typical wire structure, the large-size free-form lattice structure, featuring lightweight, superior energy absorption, and a high strength-weight ratio, has received extensive attention in developing its WAAM fabrication process. However, there is currently no sophisticated WAAM system commercially available in the industry to implement an automated fabrication process of wire or lattice structures. The challenges faced in depositing wire structures include the lack of methods to effectively identify individual struts in wire structures, 3D slicing algorithms for the whole wire structures, and path planning algorithms to establish reasonable deposition paths for these generated discrete sliced layers. Moreover, the welded area of the struts within the wire structure is relatively small, so the strut forming is more sensitive and more easily affected by the interlayer temperature. Therefore, the control and prediction of strut formation during the fabricating process is still another industry challenge. Simultaneously, there is also an urgent need to improve the processing efficiency of these structures while ensuring the reliability of their forming result

Experimental Studies on Assessment and Reduction of Surface Waviness for Weld Deposition based Additive Manufacturing

Weld deposition based Additive Manufacturing (AM) is one of the economical and efficient ways for fabricating mesoscale metallic objects. This study focuses on the use of Gas Metal Arc Welding (GMAW) based weld-deposition for obtaining the near-net shape of the object, subsequently to be finish machined to the final dimensions. The near-net shape in most of the techniques is obtained through a series of weld-deposition and face milling for each layer. The interlayer face milling is needed because of the uneven surface produced during welddeposition. However, this operation increases the total time of the process and also reduces the material utilization. Hence, this study focuses on reducing the surface waviness of a given layer eliminating/minimizing the need for interlayer face milling. The surface waviness is caused mainly due to improper process parameters and repetitions/gaps arising in area-filling paths. While there is sizable literature on suitable process parameters, the effect of the area-filling path on the surface waviness is not fully analysed. The current work presents different experimental studies carried out for studying the effect of different areafilling features on the surface waviness. Accordingly, three area filling patterns namely spiral-in, spiral-out, and rectilinear with different surface waviness have been described. The material utilisation is measured using a 3D scanner and face milling. Both approaches gave similar results signifying the suitability of 3D scanner approach. Subsequently, the multi-layer experiments are also carried out for different area filling patterns and surface waviness is measured for 5-layer. Rectilinear is found to be the best. In the rectilinear pattern, different overlapping methods namely offset overlap, and criss-cross overlap is also explored. Among these methods, criss-cross shows the best Rt value

In Situ Process Monitoring and Machine Learning Based Modeling of Defects and Anomalies in Wire-Arc Additive Manufacturing

Wire Arc Additive Manufacturing (WAAM) has made great strides in recent years however, there remain numerous persistent challenges still hindering more widespread adoption. Defects in the parts produced degrade their mechanical performance. Inconsistency in the geometry of the weld beads or undesirable anomalies such as waviness, or humps can lead to loss of geometric accuracy and in extreme cases, when anomalies propagate to subsequent layers, build failure. Such defects can be mitigated by a controls framework, which would require a model that maps undesirable outcomes to information about the process that can be obtained in real time. This thesis explores the development of a multi-sensor framework for real time data acquisition and several approaches for arriving at such a model, employing well known machine learning methodologies including Random Forests, Artificial Neural Networks and Long Short Term Memory. The merits and drawbacks of these methods is discussed, and a physics based approach intended to mitigate some of the drawbacks is explored. The models are trained first on data obtained on a single build layer, and subsequently on a multi-layer wall

The current state of research of wire arc additive manufacturing (WAAM): a review

Wire arc additive manufacturing is currently rising as the main focus of research groups around the world. This is directly visible in the huge number of new papers published in recent years concerning a lot of different topics. This review is intended to give a proper summary of the international state of research in the area of wire arc additive manufacturing. The addressed topics in this review include but are not limited to materials (e.g., steels, aluminum, copper and titanium), the processes and methods of WAAM, process surveillance and the path planning and modeling of WAAM. The consolidation of the findings of various authors into a unified picture is a core aspect of this review. Furthermore, it intends to identify areas in which work is missing and how different topics can be synergetically combined. A critical evaluation of the presented research with a focus on commonly known mechanisms in welding research and without a focus on additive manufacturing will complete the review

Geometry Prediction in Wire Arc Additive Manufacturing Using Machine Learning

Wire Arc Additive Manufacturing has disruptive potential for modern manufacturing. The technology comes with the flexibility and material efficiency of additive manufacturing processes while mitigating the disadvantages through high material output and high energy efficiency. The prevalence of the technology is inhibited by the large induced residual stresses and geometrical inaccuracy. This work tackles the latter by assessing the process parameter-geometry relationship using Machine Learning (ML) algorithms. To do so, multiple mild steel welding beads with varying shape features like corner angle are printed using a Metal Inert Gas (MIG) welding machine attached to an industrial robot. The cross sectional profile of the printed beads is measured using a point laser sensor and correlated through different ML algorithms to input features such as travel speed (TS), wire feed speed (WFS), interlayer temperature, and shape features. By incorporating varying bead shapes, a holistic model, not limited to geometry prediction of straight beads only, is trained. Thus, the model holds the potential to learn the process parameter-geometry relationship for different shape features of a part. Using the model, excess material at the inner angle of corners determined by the overlapping regions of the two adjacent beads can be predicted. By generating a database of possible bead shapes a inverse algorithm was created, that suggests welding parameter combinations resulting in a smoother bead shape at corners. Additionally, a study on the transferability of common bead geometry prediction models on other research testbeds was conducted. The importance of input features for transferability is assessed and the potential to increase transferability by infusing the model training with mass conservation is examined.M.S