The frenzy of media attention surrounding Artificial Intelligence (AI) dwarfs the past hype surrounding Additive Manufacturing (AM). Whether you look to the future with fear or excitement, there is no escaping the wave of change that is coming. Whilst we once again hear words like ‘revolution’ being used – to which so many have become immune – Dr Omar Fergani believes that we are now at a crucial point of convergence for AM and AI. Here, he explains why AM is in an especially strong position to leverage the potential of AI, with the power to transform many areas of our industry, from part design to machine operation, quality management and beyond.
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While metal Additive Manufacturing is revolutionising industries from aerospace to healthcare, the need for efficient process development and the acquisition of validated material data is becoming increasingly important. In this context, a groundbreaking solution emerges – that of the platform-based exploitation of metal AM data assets to accelerate industrialisation and unlock new revenue streams. Here, Rosswag GmbH’s Philipp Schwarz and Gregor Graf delve into the significance of this concept and how it fits as a crucial piece in the AM industrialisation puzzle.
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France’s ADAXIS is tackling two of the most complex advanced industrial manufacturing disciplines head on — Robotics and Additive Manufacturing. Its solution aims to make robotic Additive Manufacturing more accessible to any company that wants it, including for metal processes, irrespective of background or industry sector. Rachel Park spoke with Henri Bernard and Emil Johansson, two of ADAXIS’ co-founders, as well as project partners, to discover their story and ambitions.
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The growth of metal Additive Manufacturing has been held back, believes Oqton’s Dr Ben Schrauwen, by a specific set of challenges: repeatability, cost, and the need for a high level of expertise. This article considers how next-generation software solutions that leverage Artificial Intelligence, cloud computing, and hybrid modelling are improving metal AM workflows. By addressing all three challenges, Schrauwen believes that metal AM can achieve faster and deeper adoption, leading to a more efficient and innovative future.
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In the race to optimise AM technologies for the creation of both breakthrough designs and direct part replacements, it is more necessary than ever that companies have access to the deep insights and data needed to advance part designs and processing at every phase of product development. Thanks to its ability to provide these insights non-destructively and in great detail, CT analysis provides an invaluable tool for the advancement of AM processes and their adoption. Philip Sperling, Product Manager AM at Volume Graphics, explains how CT analysis can advance the development and adoption of metal Binder Jetting (BJT).
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Is it possible to actively monitor the huge volumes of data from a Laser Beam Powder Bed Fusion (PBF-LB) machine to identify, through machine learning (ML), build errors as they happen? To answer this question, Renishaw and Altair played a unique game of hide and seek. In this innovative experiment, an error was deliberately hidden in a build for an artificial intelligence (AI)-based solution to find. The hope? True ‘on the fly’ quality assurance for Additive Manufacturing processes for accelerated product development, and dramatically reduced post-production quality checks. [First published in Metal AM Vol. 8 No. 1, Spring 2022]
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A technical session at the Euro PM2021 Virtual Congress, organised by the European Powder Metallurgy Association (EPMA) and held October 18–22, 2021, was devoted to the consideration of process developments and numerical simulation approaches for Powder Bed Fusion (PBF) Additive Manufacturing technologies. In this report, Dr David Whittaker reviews four of the papers presented on this topic, looking at process parameter optimisation, increasing quality for Ti6Al4V medical parts, techniques to improve the AM of hot-work tool steels, and powder spreading improvements for stainless steel. [First published in Metal AM Vol. 7 No. 4, Winter 2021]
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They each have similar two-letter acronyms, and, for both technologies, it can be hard to separate hype from reality. But Artificial Intelligence (AI) and Additive Manufacturing also overlap in interesting and beneficial ways. In this article, Stephen Warde of Intellegens considers how AI methods such as Machine Learning (ML) could help AM to deliver against expectations – and at the very least, to meet more realistic and commercially essential objectives, such as consistently delivering lighter, stronger components and supporting on-demand manufacturing. [First published in Metal AM Vol. 7 No. 4, Winter 2021]
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In the early days of metal Additive Manufacturing process development, automation was off the radar of machine manufacturers. Technologies created for rapid prototyping simply had no need for it and, until the last decade, few truly anticipated the pace at which Laser Beam Powder Bed Fusion and Binder Jetting have evolved in the race towards the series production of metal parts. In this article, Joseph Kowen reports on how the industry has addressed the challenges of automation so far, and what developments we can expect in the near future. [First published in Metal AM Vol. 6 No. 4, Winter 2020]
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As industry marches toward automation, networked communication and robotics, Additive Manufacturing has a unique advantage. No other production technology has been designed, from its inception, to enable connectivity and communication; AM machines around the world are already producing more build data than any other manufacturing technology. If used properly, this data will provide the foundation for the development of Machine Learning tools that can improve and industrialise the AM process at nearly every point in the workflow. In this article, Chelsea Cummings and John Barnes, from The Barnes Global Advisors, discuss the present and future of Machine Learning in AM. [First published in Metal AM Vol. 6 No. 4, Winter 2020]
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Computational Fluid Dynamics (CFD) is widely applied to solve a broad range of research and engineering problems, from aerodynamics to engine combustion and microfluidics. In this article, Pareekshith Allu, Senior CFD Engineer at Flow Science, Inc, explains how CFD can also be used to improve laser- and non-laser-based metal Additive Manufacturing processes, including Laser Beam Powder Bed Fusion (PBF-LB), Directed Energy Deposition (DED), Binder Jetting (BJT) and Material Extrusion (MEX). [First published in Metal AM Vol. 6 No. 4, Winter 2020]
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As the metal Additive Manufacturing industry matures at a rapid rate, organisations are now faced with the challenge of scaling their AM operations. Based on a study of 253 companies in a number of sectors, US-based AM software specialist Link3D has developed an ‘Additive Manufacturing Maturity Model’. This simple model can be used as a tool to understand an organisation’s AM maturity whilst also helping them navigate the steps to developing an agile and resilient AM supply chain. Shane Fox, CEO and co-founder of Link3D, explains. [First published in Metal AM Vol. 6 No. 2, Summer 2020]
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