Integrated approach to Wire Arc Additive Manufacturing (WAAM) optimization: Harnessing the synergy of process parameters and deposition strategies

The flexibility of Additive Manufacturing (AM) technologies in the metal 3D printing process has gained significant attention in research and industry, which allows for fabricating complicated and intricate Near-Net-Shape (NNS) geometry designs. The achievement of desired characteristics in Wire-Arc Additive Manufactured (WAAM) components is primarily contingent upon the careful selection and precise control of significant processing variables, including bead deposition strategy, wire materials, type of heat source, wire feed speed, and the application of shielding gas. As a result, optimizing these most significant process parameters has improved, producing higher-quality WAAM-manufactured components. Consequently, this has contributed to the overall rise in the method's popularity and many applications. This article aims to provide an overview of the wire deposition strategy and the optimization of process parameters in WAAM. The optimization of numerous wire deposition techniques and process parameters in the WAAM method, which is required to manufacture high-quality additively manufactured metal parts, is summarised. The WAAM optimization algorithm, in addition to anticipate technological developments, has been proposed. Subsequently, a discussion ensues regarding the potential for WAAM optimization within the swiftly growing domain of WAAM. In the end, conclusions have been derived from the reviewed research work

Development of Stand Alone Application Tool for Processing and Quality Measurement of Weld Imperfection Image Captured by μ-Focused Digital Radiography Using MATLAB- Based Graphical User Interface

Digital radiography incresingly is being applied in the fabrication industry. Compared to film- based radiography, digitally radiographed images can be acquired with less time and fewer exposures. However, noises can simply occur on the digital image resulting in a low-quality result. Due to this and the system’s complexity, parameters’ sensitivity, and environmental effects, the results can be difficult to interpret, even for a radiographer. Therefore, the need of an application tool to improve and evaluate the image is becoming urgent. In this research, a user-friendly tool for image processing and image quality measurement was developed. The resulting tool contains important components needed by radiograph inspectors in analyzing defects and recording the results. This tool was written by using image processing and the graphical user interface development environment and compiler (GUIDE) toolbox available in Matrix Laboratory (MATLAB) R2008a. In image processing methods, contrast adjustment, and noise removal, edge detection was applied. In image quality measurement methods, mean square error (MSE), peak signal-to-noise ratio (PSNR), modulation transfer function (MTF), normalized signal-to-noise ratio (SNRnorm), sensitivity and unsharpness were used to measure the image quality. The graphical user interface (GUI) wass then compiled to build a Windows, stand-alone application that enables this tool to be executed independently without the installation of MATLAB.