Geometry and Distortion Prediction of Multiple Layers for Wire Arc Additive Manufacturing with Artificial Neural Networks

Wire arc additive manufacturing (WAAM) is a direct energy deposition (DED) process with high deposition rates, but deformation and distortion can occur due to the high energy input and resulting strains. Despite great efforts, the prediction of distortion and resulting geometry in additive manufacturing processes using WAAM remains challenging. In this work, an artificial neural network (ANN) is established to predict welding distortion and geometric accuracy for multilayer WAAM structures. For demonstration purposes, the ANN creation process is presented on a smaller scale for multilayer beads on plate welds on a thin substrate sheet. Multiple concepts for the creation of ANNs and the handling of outliers are developed, implemented, and compared. Good results have been achieved by applying an enhanced ANN using deformation and geometry from the previously deposited layer. With further adaptions to this method, a prediction of additive welded structures, geometries, and shapes in defined segments is conceivable, which would enable a multitude of applications for ANNs in the WAAM-Process, especially for applications closer to industrial use cases. It would be feasible to use them as preparatory measures for multi-segmented structures as well as an application during the welding process to continuously adapt parameters for a higher resulting component quality

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

Machine Learning Based Defect Detection in Robotic Wire Arc Additive Manufacturing

In the last ten years, research interests in various aspects of the Wire Arc Additive Manufacturing (WAAM) processes have grown exponentially. More recently, efforts to integrate an automatic quality assurance system for the WAAM process are increasing. No reliable online monitoring system for the WAAM process is a key gap to be filled for the commercial application of the technology, as it will enable the components produced by the process to be qualified for the relevant standards and hence be fit for use in critical applications in the aerospace or naval sectors. However, most of the existing monitoring methods only detect or solve issues from a specific sensor, no monitoring system integrated with different sensors or data sources is developed in WAAM in the last three years. In addition, complex principles and calculations of conventional algorithms make it hard to be applied in the manufacturing of WAAM as the character of a long manufacturing cycle. Intelligent algorithms provide in-built advantages in processing and analysing data, especially for large datasets generated during the long manufacturing cycles. In this research, in order to establish an intelligent WAAM defect detection system, two intelligent WAAM defect detection modules are developed successfully. The first module takes welding arc current / voltage signals during the deposition process as inputs and uses algorithms such as support vector machine (SVM) and incremental SVM to identify disturbances and continuously learn new defects. The incremental learning module achieved more than a 90% f1-score on new defects. The second module takes CCD images as inputs and uses object detection algorithms to predict the unfused defect during the WAAM manufacturing process with above 72% mAP. This research paves the path for developing an intelligent WAAM online monitoring system in the future. Together with process modelling, simulation and feedback control, it reveals the future opportunity for a digital twin system

Application of Multiple Kernel Support Vector Regression for Weld Bead Geometry Prediction in Robotic GMAWProcess

Modelling and prediction of weld bead geometry is an important issue in robotic GMAW process. This process is highly non-linear and coupled multivariable system and the relationship between process parameters and weld bead geometry cannot be defined by an explicit mathematical expression. Therefore, application of supervised learning algorithms can be useful for this purpose. Support vector machine is a very successful approach to supervised learning. In this approach, a higher degree of accuracy and generalization capability can be obtained by using the multiple kernel learning framework, which is considered as a great advantage in prediction of weld bead geometry due to the high degree of prediction accuracy required. In this paper, a novel approach for modelling and prediction of the weld bead geometry, based on multiple kernel support vector regression analysis has been proposed, which benefits from a high degree of accuracy and generalization capability. This model can be used for proper selection of welding parameters in order to obtain a desired weld bead geometry in robotic GMAW process

A Numerical and Experimental Study on Effect of Composition of Ar-N2 Shielding Gas on the Weld Bead Profile and its Prediction for Hot Wire Arc Additive Manufacturing

Wire arc additive manufacturing is a process of making three-dimensional metal parts in a layer-by-layer approach using a feed wire and electric arc as a heat source. Wire arc additive manufacturing (WAAM) is becoming more popular due to its ability to create complex 3D parts, less build time, high deposition rate, and significant cost savings. Out of the many parameters used in WAAM, one of the important parameters is shielding gas which plays a significant role in material quality, properties, and defects. In this study, a controlled amount of Argon (Ar) and Nitrogen (N2) shielding gases are used to see the effect on the weld bead depth and width. In addition, a computational fluid dynamics (CFD) model is used to perform numerical analysis. The data collected from the experiment is used to perform a regression analysis to predict future values. The amount of shielding gas mixture is controlled through a flowmeter to three different total flowrates. The result shows there is an increase in the depth and width of the weld bead with the increase in N2 percentage in the Ar-N2 shielding gas mixture. With the increase in Nitrogen percentage, the tungsten arc is observed unstable and spattering is noticed. The temperature of the surface of the base metal is increased while using the Ar-N2 mixture. The experiment result is further verified by developing and analyzing a three-dimensional computational fluid dynamics model using a volume of fluid (VOF) method. Support vector machine (SVM) regression model with Gaussian kernel function is used to perform the predictive regression analysis. Out of all the regression models, SVM has the lowest model loss for the collected data

Bacterial Memetic Algorithm Trained Fuzzy System-Based Model of Single Weld Bead Geometry

This article presents a fuzzy system-based modeling approach to estimate the weld bead geometry (WBG) from the welding process variables (WPVs) and to achieve a specific weld bead shape. The bacterial memetic algorithm (BMA) is applied to solve these problems in two different roles, as a supervised trainer, and as an optimizer. As a supervised trainer, the BMA is applied to tune two different WBG models. The bead geometry properties (BGP) model follows a traditional approach providing the WBG properties as outputs. The direct profile measurement (DPM) model describes the bead profiles points by a non-linear function realized in the form of fuzzy rules. As an optimizer, the BMA utilizes the developed fuzzy systems to find the solution sets of WPVs to acquire the desired WBG. The best performance is achieved by applying six rules in the BGP model and eleven rules in the DPM model. The results indicate that the normalized root means square error for the validation data set lies in the range of 0:40 - 1:56% for the BGP model and 4:49 - 7:52% for the DPM model. The comparative analysis suggests that the BGP model estimates the BWG in a superior manner when several WPVs are altered. The developed fuzzy systems provide a tool for interpreting the effects of the WPVs. The developed optimizer provides multiple valid set of WPVs to produce the desired WBG, thus supporting the selection of those process variables in applications

A multi-bead overlapping model for robotic wire and arc additive manufacturing (WAAM)

Wire and arc additive manufacturing (WAAM) is a promising alternative to traditional subtractive manufacturing for fabricating large aerospace components that feature high buy-to-fly ratio. Since the WAAM process builds up a part with complex geometry through the deposition of weld beads on a layer-by-layer basis, it is important to model the geometry of a single weld bead as well as the multi-bead overlapping process in order to achieve high surface quality and dimensional accuracy of the fabricated parts. This study firstly builds models for a single weld bead through various curve fitting methods. The experimental results show that both parabola and cosine functions accurately represent the bead profile. The overlapping principle is then detailed to model the geometry of multiple beads overlapping together. The tangent overlapping model (TOM) is established and the concept of the critical centre distance for stable multi-bead overlapping processes is presented. The proposed TOM is shown to provide a much better approximation to the experimental measurements when compared with the traditional flat-top overlapping model (FOM). This is critical in process planning to achieve better geometry accuracy and material efficiency in additive manufacturing

Model-free adaptive iterative learning control of melt pool width in wire arc additive manufacturing

© 2020, Springer-Verlag London Ltd., part of Springer Nature. Wire arc additive manufacturing (WAAM) is a Direct Energy Deposition (DED) technology, which utilize electrical arc as heat source to deposit metal material bead by bead to make up the final component. However, issues like the lack of assurance in accuracy, repeatability and stability hinder the further application in industry. Therefore, a Model Free Adaptive Iterative Learning Control (MFAILC) algorithm was developed to be applied in WAAM process in this study. The dynamic process of WAAM is modelled by adaptive neuro fuzzy inference system (ANFIS). Based on this ANFIS model, simulations are performed to demonstrate the effectiveness of MFAILC algorithm. Furthermore, experiments are conducted to investigate the tracking performance and robustness of the MFAILC controller. This work will help to improve the forming accuracy and automatic level of WAAM