In an interview with Asia Pacific Metalworking News, Dr. Mohsen Seifi, Director of Global Additive Manufacturing Programs at ASTM International, discusses the benefits of additive manufacturing (AM) in manufacturing and the role of data analytics in AM.
Tell us more about ASTM International, for those who may not be familiar with the organisation.
ASTM International is one of the world’s leading standards development organisations, founded in 1898. We have 150 technical committees that oversee about 13,000 standards that are widely used around the world. Several of those committees are in emerging industries, including one for additive manufacturing technology that now has nearly 1,000 members, known as F42. For over a decade, this group of the world’s top additive manufacturing experts has been meeting and working through ASTM to develop groundbreaking standards that have begun to form the technical foundation for the future of additive manufacturing. Furthermore, ASTM International has made a dramatic investment in front-end research to develop even more standards through our Additive Manufacturing Center of Excellence, a network of high-profile partners around the globe which includes Singapore’s National Additive Manufacturing Innovation Cluster (NAMIC). Please visit our website for more detailed information.
In the Industry 4.0 era, greater efficiency and product innovation are key priorities for manufacturers. How can they leverage additive manufacturing/3D printing to achieve both?
A big challenge for manufacturers is the lack of communication between stakeholders at different steps in the process chain. Smart, digital manufacturing could allow manufacturers to effectively transfer the most relevant information across all stages of product development, from designers to end-users. Additive Manufacturing is an integral part of Industry 4.0 and is an excellent technology for product innovation that could significantly reduce the time for product development through iterative design capabilities.
Also, Additive manufacturing can substantially improve the efficiency of the manufacturing process by parts consolidation. This will enhance the effectiveness of a system as a whole in terms of weight reduction, material optimisation, and reduction in fuel consumption. For AM, digital manufacturing means integrating physical system-oriented manufacturing with digital system-oriented Industry 4.0 technologies (e.g., artificial intelligence (AI), big data, robotics, cybersecurity, and Internet of Things [IoT]). To fully unlock the potential of smart, digital manufacturing, there are still issues to address, which include cybersecurity concerns, data management challenges, and other critical gaps. ASTM uses various roadmaps to develop standards to address these gaps and to meet the industry needs.
Which end-markets do you see increasing adoption of additive manufacturing?
AM has the potential to impact all manufacturing-related sectors—from aerospace, medical and automotive to oil/gas, maritime and other sectors—and we anticipate adoption will increase exponentially across the board in the next 10 years. In particular, AM holds great promise for aerospace/defense and medical applications. Both of these sectors require complex, specialised parts, which AM is capable of producing. More importantly, the demand for AM qualification and certification in these high-tech areas/end-markets is high. This is because successful qualification and certification provide end-market users with increased confidence (i.e., improvements in quality and reduced safety concerns). According to a recent survey, the three most significant challenges to adoption of AM for end-market users over the next ten years are: 1) the certification of finished parts and products, hindering its mainstream commercial uptake in the future; 2) the quality and standardisation of material inputs; and 3) unknown quality of printed components.
What are the biggest challenges when it comes to additive manufacturing?
As an emerging field, the AM industry still needs a shared language and framework for addressing problems. Lack of standards is one of the biggest challenges for additive manufacturing in addition to other challenges such as lack of qualified workforce, limited availability of materials, and the lack of full-fledged certification programs. Standards provide a common reference point to help the industry avoid the time and expense of solving problems by trial and error. For example, there is an ongoing need for a better understanding of feedstock properties, methods for in-process monitoring and control, machine-to-machine variation, and rapid inspection methods for AM parts, among other topics. In addition, standards are a key enabler of the qualification and certification procedures that were mentioned above.
To accelerate the development of standards to address these challenges, we launched the AM Center of Excellence (CoE), a collaborative partnership among industry, academia, and government that integrates research and development (R&D) with standards development. By initiating R&D projects that target specific high-priority standards needs, I believe we can speed the overall advancement and adoption of AM technologies. Detailed information will be available in our upcoming external R&D roadmap, which will be released this spring. In the meantime, our annual report provides an overview of the AM CoE’s activities.
Why is analytics a feasible solution?
One benefit of analytics is that it presents decision-makers with the key information required to make informed decisions. Manufacturers have access to a wealth of data about their products and processes but are not always able to use it. Analytics is a great tool to convert data into actionable knowledge that can be used to optimise product development. In the case of AM, solutions such as data-enabled material screening, build monitoring, and post-build characterisation ensure the product meets its specifications with as few iterations as possible, helping minimise production time and cost.
How will data analytics make additive manufacturing more efficient?
AM generates more data than any other manufacturing field—this data has great value, but there are challenges to extracting useful information. Structuring data in a way that adheres to FAIR principles (findable, accessible, interoperable, and reusable) will be vital to the success of AM. Data analytics holds the key to processing and making sense of vast stores of data, which will ultimately accelerate the AM development timeline. Data analytics is a solution that cuts across all sectors and is already shaping the future of technology as we know it.
Through AI, which encompasses machine learning (ML) and deep learning (DL), the AM industry can quickly decode quantitative structure/process/property/performance relationships, which is a core challenge in the AM field. For example, it is possible to use AI to sift through potential AM materials to find those with optimal properties or functionalities. AI can also enable data-driven in-situ/real-time monitoring for identifying better processes. However, to enable these data-driven advances, the AM community needs an AM data ecosystem that enables the easy and secure generation, storage, analysis, and sharing of data. ASTM and America Makes recently convened a workshop on manufacturing data management and schema to identify and prioritise challenges and potential solutions for strengthening the AM data ecosystem.
What is your outlook for additive manufacturing/3D printing this year?
It is very hard to predict the future of AM because technology is rapidly changing, but I would like to see 2020 as the year of standards. There is an exciting opportunity for more integration between AM and other elements of industry 4.0, in terms of automation, robotics, cybersecurity, and big data—creating these links is a great way to connect the physical world and digital world. I believe that the best way to create synergy between these critical technologies is through standardisation to add trust. The more we can focus on developing standards, the sooner we can see these advances.
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