The manufacturing digital and physical environments are changing rapidly, and emerging technologies are becoming even more critical to our long-term technological competitiveness.
At the same time, however, manufacturers are facing greater challenges than ever to understand and take action. There are nearly 300,000 factories in the U.S., of which 90 percent have fewer than 100 employees. And only a few of these smaller firms have the wherewithal and resources to use advanced manufacturing processes.
“Our research indicates that it’s likely less than 10 percent of small and medium-sized manufacturing enterprises in the U.S. are extensively adopting these emerging technologies,” said Greg Harris, Auburn University’s director of the Interdisciplinary Center for Advanced Manufacturing Systems, who is studying this information with SME.
The White House, via the National Science and Technology Council (NSTC) and the National Strategy for Advanced Manufacturing, focuses on American innovation and national security. NSTC’s recently released “Critical and Emerging Technologies” list contains 19 technology areas, including advanced manufacturing and the subfields of additive, sustainable, smart, and nano manufacturing, as well as related fields such as artificial intelligence (A.I.), autonomous systems, and robotics.
Similarly, the National Strategy for Advanced Manufacturing contains 18 priority technologies, including additive manufacturing, A.I., high-performance materials, and sustainable manufacturing. In addition, many prominent technology research and consulting firms have identified these areas in common as top strategic technology trends for research and development and business applications that can strengthen and revitalize advanced manufacturing competitiveness.
If our future depends on the pervasive development and institutionalization of these ambitious technologies, which of them will have the most significant impact, and how can we accelerate the pace of implementation?
Innovation has become the catchphrase for manufacturing in the belief that wide-ranging and persistent advances can reverse the present negative industrial decline and lead to long-term sustainable competitiveness.
Innovation in the way we conceive what can be produced, in the way we design products, and in the way that we manufacture and support them, is shifting radically. The manufacturing infrastructure supporting this innovation is based on new, widely deployed computational methods and tools that support a totally digital product realization process.
Importantly, a substantial amount of product development and testing can now be done in a virtual environment. However, there are still critical gaps in the ability to simulate the behavior of many materials and manufacturing operations at a very high level of fidelity. The future capability to perform comprehensive and trustworthy product development and qualification through modeling and simulation will be revolutionary.
A.I. includes a broad spectrum of transformational computational applications (e.g., machine learning) across many industries as the leading technology within the digital environment. Likewise, A.I. is the fastest-growing field of software applications for manufacturing operations as demand grows for substantial improvements in efficiency and productivity.
A.I.-enabled manufacturing, which ranges from physics-based materials science to the analysis of supply chains/logistics and product support, affords the capability where the envisioned MBx (model-based everything) process across the lifecycle will be smart and self-adjusting in real time based on circumstances and experience. NASA recently requested proposals to exploit new developments to advance the use of model-based tools for accelerated certification of critical additively manufactured aerospace products, including through A.I. and integrated computational materials engineering.
This approach is desired over the traditional building-block approach of repetitive trial-and-error experimental methods, which take too long and cost too much. The NSTC subcommittee on advanced manufacturing, and another on machine learning and artificial intelligence, recently issued a report on the benefits of A.I. adoption in manufacturing and issues that inhibit its widescale adoption.
According to the report, “The pervasive application of A.I. in manufacturing can provide a world-leading advantage to the U.S. manufacturing industry. However, a much faster pace of development of A.I. skills and tools is needed to accelerate industry adoption.”
As an indicator of just how important A.I. will be, you can, as they say, follow the money. The global artificial intelligence market is projected to exceed one trillion dollars this decade.
Digital twins are becoming among the most important parts of the digital transformation, industrial revolution, and implementation of Industry 4.0. Conceived in 2010, digital-twin technology is focused on manufacturing and product lifecycle management, and it has broadly evolved to address social, business, healthcare, environmental, and many more complex systems-of-systems. While predicated on the ubiquitous use of modeling and simulation, data analytics, IoT, A.I., and other applications and technologies, digital twins are, in fact, the culmination of these applications.
Each of these applications has significant value; however, it is the combination of the two that offers extraordinary impact. Fundamentally, digital twins are interdisciplinary and interoperable, where multiple systems communicate and work together in an autonomous approach to analyze and synthesize data and information into a coordinated, coherent, and actionable whole.
That describes a highly efficient comprehensive design and manufacturing machine that is more capable than any other of producing a multitude of innovations across processes, systems, and products. Today there are probably hundreds of reasonable definitions and concepts of digital twins that provide purpose and value.
One of the most cited definitions comes from my 2017 book, co-authored by Michael Grieves, Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems, which states: “The digital twin is a set of virtual information constructs that fully describes a potential or actual physical manufactured product from the micro atomic level to the macro geometrical level. At its optimum, any information that could be obtained from inspecting a physical manufactured product can be obtained from its digital twin.”
Integrated MBx is again the key innovation for the digital twin. The approach delivers far-reaching improvements and moves the ecosystem to the brink of what can be done, realizing all of the performance that can be imagined for design and manufacturing challenges. The approach conveys a multidimensional integration of technologies across the full product lifecycle, which may take the form of a virtual reality “command center,” where the user visualizes the resulting systems/processes in real time and helps manufacturers make intelligent decisions.
Almost all modern manufacturing operations, equipment, and infrastructure have been to some degree computerized, undergone digitalization, or, a step further, behave as holistic cyber-physical systems. There is a natural confluence between digital and physical manufacturing technologies that can play an outsized role to deliver more capable, more affordable, and more reliable processes and products, and expand commercial markets.
The integration of digital and physical manufacturing technologies are transforming the way we work throughout the end-to-end manufacturing operation. Whether it is automation for composites, intelligent machine tools, robotics, advanced characterization, or additive manufacturing, the holistic integration of computation and physical equipment offers the greatest potential to deliver the essential and meaningful change in industrial competitiveness.
Among physical manufacturing technologies, additive manufacturing (AM) is surely the best example of a successful cyber-physical system. AM is dramatically changing the design and manufacturing landscape, resulting in a new wave of advanced technologies to build hardware across many sectors.
The benefits of AM include remarkable design flexibility, affordability, schedule, performance, sustainability, and on-demand production. While AM was born digital, traditional manufacturing processes are incrementally making the transition to digitalization.
Today’s competitiveness goals and the much-needed accelerated adoption of emerging manufacturing technologies cannot be accomplished with limited-point solutions to solve specific problems. Instead, these challenges demand a blueprint that harnesses the power of physics-based digital technologies working in concert with physical infrastructure that spans disciplines and sectors.
The digital transformation is not hyped too much. In fact, its perpetual innovation across the continuum of basic research and complex operational systems offers amazing promise and possibilities for the most difficult advanced manufacturing problems.
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