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Joint UK/EU project to scale-up additive manufacturing
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Consortium aims to improve deposition rates and build efficiency in wire-based DED
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AML3D expands 3D printing contract with Boeing
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The wire arc additive manufacturing (WAAM) is one of the advanced manufacturing processes to fabricate full-density three-dimensional (3D) Inconel 718 (IN718) metal parts in an open freeform environment. Thus, there is no size restrictions of the fabricated parts using this process which is suitable for industry-led medium to large production supply chain. So far, the use of WAAM process in the fabrication of IN718 parts is solely focused on the effects of standard heat-treatments on the evolution of microstructural constituents and mechanical properties. Therefore, the present study is attempted to systemically investigate the effects of welding parameters, heat-treatment, high-oxidation temperatures and hot-deformation characteristics on the processing-microstructure-property relationship of IN718 alloys manufactured via Gas Tungsten Arc Welding (GTAW)-based WAAM process. A wrought IN718 alloy was also studied for comparison.
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 provid