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Smart Manufacturing for Smart Materials

Jun 23, 2020 | Industry 4.0 / IoT / IIoT, Smart Manufacturing

Closed loops of sensors, data collection and analysis, visualization and  interpretation will drive accelerated learning, inform design, and have  the power to link the entire value chain from raw material to end  users.

The Industrial Internet of Things (IIoT) and smart manufacturing  initiatives are accelerating the creation of new technologies,  transforming global manufacturing. Like most innovation, competitive  advantage and measurable, improved economics will be the biggest drivers  of adoption. Enhanced sensors, data collection, analysis, visualization  and interpretation will drive accelerated learning from existing  processes and inform the design of new ones.

This closed loop will have the power to link the entire value chain  from raw material to end user and will eventually lead to continuously  optimized manufacturing driven by artificial intelligence. Specifically,  in polymer manufacturing, a broad approach is underway to improve and  optimize the production and research of new materials.

Synthetic and natural polymers make our modern lives possible through  a multitude of industrial and consumer applications ranging from  manufacturing and medicine to oil and gas, water management, food,  electronics  and construction. Continually pushing the boundaries of  performance in existing and new applications is what drives the  development of new materials. Additionally, there is a perpetual drive  to improve the efficiency, yield and quality in the production of these  materials. Typical examples of companies focused on implementing changes  targeting improvement include information sharing, data analytics,  enhanced process measurements, process modeling and control algorithms,  and the training and re-training of factory operators, technicians,  scientists and engineers.

For decades, much of the chemical manufacturing industry has operated  through a continually-evolving combination of approaches including  quantitative, empirical, fundamental first principles and qualitative  processes. Empirical and quantitative analysis has been a major focus,  evidenced by the chemical industry’s leadership in its use of process  data.

The industry has a long history of recording and utilizing  data to analyze performance, troubleshoot problems and incrementally  improve existing processes. Fundamental chemistry and process  understanding, good maintenance practices, and talented engineers have  worked alongside these empirical methods to optimize processes, leading  to active development and improvement of process modeling and control  efforts. This leads to improved plant design incorporating state of the  art technologies and methods.

Another common phenomenon is the qualitative experience of seasoned  front line factory workers who have learned, managed and operated  production assets for years. Replicating the intricate sensor network  (sight, sound, smell, touch, taste) and the natural human capability of  advanced pattern recognition and rapid historical recall is a very  complex problem with the current sensor mix and capabilities found in  many facilities. This is especially pronounced where processes are  particularly complex and regularly evolve with the requirements of end  users, such as in polymer production.

Some issues lie in complex chemistry where true quantitative or  fundamental first principle understanding of production is rendered  difficult or nearly impossible due to variability in plant-to-plant  equipment and process design, conditions, feedstocks and other inputs.  Typical problems then become inconsistent quality across a network of  plants producing similar products, off-spec material, longer than  required cycle times, or over-processing resulting in other quality  issues.

Many industry standard methods rely on off-line measurements in  addition to the combination of quantitative, empirical, qualitative and  fundamental methods. However, as processes continue to evolve, so will  the required technology and tools to operate them efficiently and  effectively.

In the future, as manufacturing advances, so will the research,  development and manufacturing of new materials. These materials, many  polymer based, will drive next generation consumer and industrial  products. Just as the manufacturing processes are engineered to be  ‘smart,’ materials themselves are undergoing a similar transition to be  ‘Smart,’ more customized and more application specific. This  transformation requires a new fundamental understanding and tighter  control of production assets.

The polymer industry, like many others, is already shifting to  embrace and experiment with new technology and processes to drive the  needed optimization and control. With IIoT, smart manufacturing and  Industry 4.0, more sensors, data and real-time information about  fundamental production will provide additional leverage to existing  manufacturing systems. Examples can include the dynamic fusion of data  sets from process sensors and new detector measurements to yield  typically sophisticated data in a simplified format.

In the first instance of new systems, there will be human-in-the-loop  control where real-time actionable information is provided to  production personnel to optimize, correct disruptions and operate more  effectively. Once critical parameters are measured, these can oftentimes  be combined with existing and new kinetic models for predictive,  artificial intelligence based closed loop feedback control.

Finally, this rapid evolution in many industries when simultaneously  leveraged with robust wireless networks in manufacturing, advanced data  analysis algorithms, visualization and simplified data handling will  present a myriad of opportunities. Another major opportunity is the  cross-pollination of ideas and technologies among seemingly opposite  industries such as transportation, medicine and manufacturing, which  could drive true transformative innovation and change. Often, people  solve complex problems that are directly applicable to new problems in  other industries, but a generally siloed approach prevents  dissemination. This cross-pollination is an absolute necessity to take  advantage of the rapidly changing landscape and will serve to accelerate  the current evolution of manufacturing.

Alex Reed is president and CEO of Advanced Polymer Monitoring  Technologies, a spin-out company from Tulane University that sells  patented hardware and software products used in lab and industrial  applications for polymers and biopharmaceuticals. He has spearheaded the  company’s strategy and execution, which led to an expansion from the  research idea phase to an innovative operating company with a number of  strategic partnerships. He also serves on several technology advisory  boards, including the Smart Manufacturing Leadership Coalition and the  Applied Polymer Technology Extension Consortium. He has been featured  recently in Forbes’ 30 under 30 in manufacturing, and by Silicon Bayou among its top 100 most influential people in technology and entrepreneurship.

 

 

 

Original Article by Industry Week/ Alex Reed / 2017

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