Additive Manufacturing – Will anyone think of the data?

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Organizations make big investments in Additive Manufacturing. AM machines, new materials, experts in AM processes, testing, analysis, and simulation – no expense is spared. These costs feel justified in the light of the benefits that AM can bring – parts that can be printed-to-order, new lightweight components with previously unachievable shapes, or reduced manufacturing lead times.

But, there is one aspect we might be forgetting. Will anyone think of the data? Specifically, are we investing enough into managing the complex data created from our AM projects and, if we do, are we thinking about it early enough?

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From fashion to flight: the diverse users of CES Selector

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Speaking at a recent webinar, experts from Honeywell Aerospace, Saudi Aramco, and Burberry presented the benefits of systematic materials selection.

With roots in fashion, oil and gas, and aerospace, these organizations are not only diverse in their focus, but in their experiences of using the CES Selector software. The Tempe site within Honeywell Aerospace has been using the software since 2001. Principal Materials Engineer John Perek presented two examples of how it reduced selection time, and minimized cost. The first was a materials substitution project for a pressure regulator housing that was experiencing delayed cracking after molding. The second example involved the necessary replacement of a Be-Cu pitot tube to comply with the restriction of hazardous substances (RoHS) legislation.

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10 Ways Material Information Management will make your Chief Executive Happy

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If you haven’t been involved in a material information management project, you might think it’s only of interest to materials engineers.

However, the work involved in having a single, organized source of materials information creates benefits that spread far wider than just the engineering department. In fact, the advantages go right to the top of the chain and help address the key goals of a business.

Here are 10 ways that materials information management benefit the entire company and make your Chief Executive happy.

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Is the future of materials science and AI intertwined?

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My mother always tells the story of how I learnt to type my name on a computer before I could put pen to paper. I grew up with a love of computers and am not ashamed to say that the topic of artificial intelligence (AI) – covering the gamut of machine learning, and deep learning – is a particular passion. You can imagine my delight, therefore, when I came across not one but two recent articles on how the future of materials science and AI may be intertwined.

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Composite complications – why hybrid materials are a particular materials information menace

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With a broad range of applications like corrosion protection, scratch resistance, and structural parts, hybrid materials receive a great deal of attention – particularly in high-performance engineering sectors such as aerospace, and automotive. As well as boasting high specific strength and stiffness, hybrid materials and structures like sandwich panels, foams, lattices, and composites, have the potential to reduce the environmental impact of those industries. But how can we ensure that the full benefits of this class of material are realized? And what challenges are there within the design and development process that could prevent this from happening?

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Simulation for the Automotive industry – Babbage was right, so what are we going to do about it?

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Can users of today’s advanced simulation methods for Automotive still learn from sentiments expressed 150 years ago?

On two occasions I have been asked, “Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?” … I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question.

So said Charles Babbage, widely considered the father of the computer, way back in 1864. He was obviously right – but are we still guilty of the confusion he identifies?

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The cost of trial and error in Additive Manufacturing

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Additive Manufacturing — Understanding critical process parameters and supporting the digital thread

The rate of adoption of additive manufacturing (AM) is incredible. AM brings a physicality to ideas, and offers ways for people to touch upon solutions that would have been impossible to otherwise imagine. Equally impressive is the scale of investment in machines for producing AM parts, which is of course supported by business cases highlighting reduced development times, fewer prototype costs, reduced part counts, and flexible manufacturing. But, I am seeing more and more evidence that the prescribed route to this ‘Nirvana’ is via a process of trial and error for settings, powders, and even machine capability.

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Materials shortage – what next?

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Southern Texas is the hub of the US’ supply of speciality chemicals and petrochemicals; the basis of plastics used to manufacture everything from water bottles to pill coatings. So, when Storm Harvey hit the Gulf Coast in 2017, companies within the US and worldwide were affected. For example, the close of company Arkema alone resulted in the loss of supply of 50% of the US’ supply of ethylene and polyethylene, and 40% of its chloralkaline and polyvinyl chloride.

When the availability of materials can be cut drastically short at a moment’s notice, how can companies be prepared to respond?

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A clear view of the materials paradigm

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Educators teaching introductory materials science courses know the drill: we have large classes filled with students from diverse backgrounds, with divergent aspirations and interests. And as with any type of compulsory learning experience, we look out onto a sea of people – some of whom want to be there; some don’t – and are tasked with finding how best to convey an understanding of a vast range of scale and concepts. Arguably, at this introductory level, the most fundamental of which is the relationship between Process, Structure, and Properties – otherwise known as the materials paradigm.

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Is your simulation data traceable to the source?

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As any simulation analyst can tell you, quality materials and property data is essential for modeling and simulation within product design. However, this information often exists in many different formats and locations throughout an organization. For authors of data, generating the right materials information for simulation (usually by analyzing populations of materials test data) can be time consuming, the process can be inefficient, and it’s certainly always complex. Moreover, the final data that’s produced out of this process is not always then traceable to its source.

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