A data-driven approach for predicting printability in metal additive manufacturing processes
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A data-driven approach for predicting printability in metal additive manufacturing processes William Mycroft1 · Mordechai Katzman1 · Samuel Tammas-Williams2,4 · Everth Hernandez-Nava2 · George Panoutsos3 · Iain Todd2 · Visakan Kadirkamanathan3 Received: 20 August 2019 / Accepted: 22 January 2020 © The Author(s) 2020
Abstract Metal powder-bed fusion additive manufacturing technologies offer numerous benefits to the manufacturing industry. However, the current approach to printability analysis, determining which components are likely to build unsuccessfully, prior to manufacture, is based on ad-hoc rules and engineering experience. Consequently, to allow full exploitation of the benefits of additive manufacturing, there is a demand for a fully systematic approach to the problem. In this paper we focus on the impact of geometry in printability analysis. For the first time, we detail a machine learning framework for determining the geometric limits of printability in additive manufacturing processes. This framework consists of three main components. First, we detail how to construct strenuous test artefacts capable of pushing an additive manufacturing process to its limits. Secondly, we explain how to measure the printability of an additively manufactured test artefact. Finally, we construct a predictive model capable of estimating the printability of a given artefact before it is additively manufactured. We test all steps of our framework, and show that our predictive model approaches an estimate of the maximum performance obtainable due to inherent stochasticity in the underlying additive manufacturing process. Keywords Additive manufacturing · Machine learning · Powder bed fusion · Electron beam melting · Printability analysis
Introduction Additive manufacturing technologies are critical drivers for innovation and offer potential business benefits to the industrial sector (Azam et al. 2018; Mueller 2012; Santos et al. 2006). High value manufacturing companies aim to produce additively manufactured parts that form critical components for numerous industries. Not only are such technologies ideal for rapid prototyping and building bespoke components, but This work was funded by the UK Engineering and Physical Science Research Council (EPSRC) through Grant EP/P030262/1.
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William Mycroft [email protected]
1
School of Mathematics and Statistics, University of Sheffield, Sheffield, UK
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Department of Materials Science and Engineering, University of Sheffield, Sheffield, UK
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Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK
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Department of Maritime and Mechanical Engineering, Liverpool John Moores University, Liverpool, UK
they also allow for the formation of geometries that may be difficult or impossible to construct via more conventional technologies. Consequently, additive manufacturing technologies can be used to produce parts capable of greater performance. One of the key challenges to make this technology robust and cost-effective is to ensure that t
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