Innovative Plastics

Table of Contents


There is not a person in this world who is unfamiliar with polymers, the material class which is more commonly known as plastic.

From the delivery room in the hospital to our final days, we encounter so many products made entirely or in part from polymers. Polymers make up television displays, prescription glasses, non-stick cookware, and lots of insulating applications that keep us protected from electricity.

Many industries and materials-related domains rely on polymers and plastics: packaging, 3D printing, medical and health, building materials, electronics, additive manufacturing, nanotech, greentech, etc.

The global plastic market was valued at $580B in 2020 which means production of countless tons of plastics for the world's needs.

Plastics have also been recently associated with recycling and sustainability through biodegradable polymers, all for the purpose of saving the planet. Such category can be considered as innovative plastics.

Natural and synthetically occurring polymers are involved in the facilitation of almost all human activity. It is why it matters so much that we make the most of the material and address challenges such as components abundance, cost challenges and more.

Challenges in developing and producing innovative plastics

Although introducing new polymer / plastic formulation from the innovation (R&D) phase to production might be quick (months, vs years when developing a totally new material or a new device technology), it is still a time consuming and costly activity for the organization.

The research and engineering teams should undergo these steps:

  1. Read the required specifications.
  2. Look in the company’s database (if any) for similar specifications.
  3. Learn from researching the literature.
  4. Plan and design the experiments.
  5. Experimental samples preparations.
  6. Samples characterization and check if meeting the spec.

Steps 4-6 are in a loop until meeting the specifications. On average, 20 iterations are needed in the plastics industry to finalize a formulation. All the above is also under the constraints of meeting time-to-market deadlines, supply chain and costs challenges.

From our experience working with innovative plastics developers and manufacturers, a plastic formulation is typically made of 5-15 ingredients. This makes the problem multi-dimensional only by considering each ingredient composition as a variable, which at the end prevents the researcher / scientist to see the trees from the forest and this results in trial-and-error experimentation instead of data-driven decisions.

Considering 20 iterations to meet the spec of one formulation, and assuming a company with ~100 products, this results in ~2000 data points correlating composition and processing with properties. This makes innovative plastics compounders and manufacturers data-rich companies! However, such data is normally dispersed, unstructured, incomplete, and sparse; which makes it very difficult for the organization to visualize the data properly and apply machine learning tools to obtain better understandings, better R&D decisions, and hence faster time to market.

In addition, we noticed an operational problem when failed batches were observed. The core of the problem is the lack of data flow and inter-connection between “R&D” <> “scale-up” <> “production”. When a failed batch was observed, it was impossible to fix and adapt quickly in the production line (which produces hundreds of kilograms per hour), resulting in hundreds of kilograms or even tons of waste.

The polymers domain has an infinite number of material possibilities. You synthesize a new monomer and here you can get hundreds of possibilities varying in molecular weights and hence in properties. Now imagine doing a small variation in the monomer, and here you get hundreds of possibilities more.

The main problem in polymers / plastics is how to source the best material option for the discussed used-case from thousands of possibilities and/or find the most suitable substitute for a raw material in use. Obviously, the sourcing should be according to the desired properties, such as: transparency, processability, melting point, glass transition temperature, strength, etc, and many more potential properties.

MaterialsZone solution for innovative plastics- A materials informatics platform (MIP)

Overcoming the above mentioned challenges requires an organizational materials informatics platform (MIP) that harvests, digitizes, stores, normalizes and harmonizes the materials-related data across the organization. Once the data is harmonized and available in the cloud-based platform, sharing, commenting and collaboration across the organizational teams is as easy as a click of a button. For more information about materials data management, checkout MaterialsZone’s dedicated solution here.

One step further beyond data management is to use this well arranged data in machine learning models to design and find-out the next set of experiments to be done in order to reach the target as quickly as possible.

MaterialsZone has developed a methodology and an approach for design of experiments (figure 1). See our dedicated solution here.

Figure 1: Design of experiments algo.

Controlling and monitoring the production line is one of the important challenges in plastics. MaterialsZone allows materials manufacturing QC (six-sigma) by collecting manufacturing data from the production line. Once the manufacturing data is collected from the various stations along the line and stored in a well structured manner, MaterialsZone can automatically monitor various parameters simultaneously.

Materials manufacturing QC (six-sigma) parameters can be visualized vs time to inspect un-regular behaviors as shown in figure 2.

Figure 2: Plastic layer thickness monitored in the production line.

In addition to visualization, MaterialsZone is doing behind the scenes work and alerts in case any of the monitored parameters go out of spec.

The next leap in materials manufacturing QC (six-sigma) is to detect a potential out of spec that is expected to happen either in the following stations down the line or that is expected to happen on the same station in the coming batches.

MaterialsZone is a one-stop shop for monitoring your materials manufacturing QC (six-sigma). Read about the manufacturing solution here.

Using the Materials Zone platform one can slice and dice the materials database based on properties. The materials database can include materials the company works with or materials catalogs from suppliers, which a company can order from.

Assuming we are looking at plastics for packaging and we have ~500 materials records under this category. However, we are interested in materials with a melting point higher than 200C.

Within several clicks we obtain the following histogram (figure 3):

Figure 3: A histogram that shows the distribution of packaging materials candidates with melting point above 200C.

  • We ended up with 61 possibilities out of ~500. Sourcing is more focused and much easier now.
  • We can go quickly through the candidates.
  • Additional filtering by a certain property is possible to reduce the number of candidates further.
  • Combined filtering by property and by composition, simultaneously, is available.

For additional information how MaterialsZone can help with supply chain issues, visit the dedicated solution here.

From the use-cases we observed, utilizing the existing historical data in plastics companies helped to significantly reduce the number of iterations (significantly reduced the R&D costs) until meeting the required specification. This is because of the better R&D decisions made after visualizing and analyzing the data.

In addition to the above, for innovative plastics use-cases, the platform helped with:

  • Digitizing the laboratory work.
  • Automating the data collection from instruments.
  • Normalizing the data and making it AI/ML ready.
  • Providing insights through big data analytics dashboards.
  • Performing predictions of product properties.

Whether you are an innovative plastics company, or any other materials-related company, Materials Zone, the materials informatics platform, will help your organization to accelerate its innovation and time-to-market via AI/ML insights and predictions from materials data:

  • Less and shorter development cycles – faster and better data-driven decisions (reduced trial & error).
  • Knowledge gap closure - design next experiment or data acquisition smarter/faster.
  • Organizational wisdom - all the stakeholders, all the data, all the insights, in one cloud platform.
  • Cross-functional efficiencies – R&D, supply chain, manufacturing, OEM/channel.

Other MaterialsZone solutions include:

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