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.
Being dependent on the above approach is stressful, anti-innovative, and a waste of both resource and money. Although Thomas Edison famously made thousands of failed light bulbs before he got one to work, I’m sure he would have preferred to have taken a more methodical and knowledge-based approach if he could.
For additive manufacturing especially, a comprehensive approach to managing machine, powder and settings selection is needed to close the loop of create, test, improve. Robust, pedigreed, and version-controlled data is required to evaluate and support design and manufacturing processes. Intelligent materials data management accelerates the time to value for AM machinery, and reduces the time taken to achieve qualification and certification of AM parts.
Join our live webinar ‘Additive Manufacturing — Understanding critical process parameters and supporting the digital thread’ on Thursday, February 15, and hear how to make AM a standard manufacturing method in your organization. Register here >
Read more about Additive Manufacturing >
The rapid development of Additive Manufacturing (AM) technology displays signs of immense promise for making topologically-optimized parts with optimal cost and performance. But with great power comes great challenges! Engineers require an understanding of the complex interactions and relationship between part design, materials, production processes and part performance. Designing the ‘ideal’ geometry can also prove to be a significant challenge. One secret is that succeeding in the real world of AM production requires you to do the right things in the virtual world—in how you simulate AM processes and handle AM data.
I attended the Siemens PLM Connection event in Berlin last week – a gathering of over 1,000 users of engineering and product lifecycle software applications such as Teamcenter, Simcenter, and NX. Aside from the very entertaining iPad magician at the gala dinner, two things struck me from the conference sessions and discussions with other delegates.
The first was the emphasis on Additive Manufacturing (AM), with Siemens PLM launching new capabilities such as topology optimization for additive applications. There was a strong sense from attendees that this is a technology coming into its own, and an interest in how it applies to them. Of course, data about materials, processing parameters, and the relationship between the two is vital to developing effective AM.
I recently attended the Additive Manufacturing for MedTech, BioPrinting, Medicine and Dental Summit in Boston and it was interesting to review the latest trends in the industry and think about their materials information implications. The event concentrated on the main challenges in Additive Manufacturing (AM) for medical, bringing together both major device companies (Stryker, GE Healthcare, Medtronic) and smaller consulting firms. It explored the latest printing techniques, ground-breaking research, and innovative materials for improving AM strategies, implementation and processes.
Additive manufacturing, often referred to as ‘3-D printing’, is creating great excitement in advanced manufacturing. Use of the technology means that fully functional objects can be built from plastics and metal, layer-by-layer, in extraordinary detail, without the need for expensive moulding and with minimal post-processing required. Research in this area has attracted funding from governmental agencies seeking to establish a competitive advantage and offset the loss of much traditional heavy manufacturing to lower-wage regions. Such projects target increased automation, greater material and energy efficiencies, and a reduction in waste. To meet these targets, many practical challenges must be overcome—effective use of materials information will be an important success factor.