It has always been my philosophy to obtain as much information as possible about the polymer with which I’m working. As techniques for predicting polymer performance during processing become more sophisticated and computer models for predicting polymer product response to applied temperature and force conditions become more exacting, our thirst for data seems to grow exponentially. There are two approaches to satisfying the demands for more information about polymer performance. Not surprisingly, the first is prediction of polymer properties from first principle. The second is experimental generation of requisite properties.

First Principle Prediction

For a century or more, physical chemists and chemical physicists have developed and fine-tuned mathematical models for small molecules. For example, in graduate school, we studied from Hirschfelder, Curtiss and Bird [J.O. Hirschfelder, C.F. Curtiss, and R. B. Bird, Molecular Theory of Gases and Liquids, John Wiley & Sons, NY, 1964], where we learned that molecular properties such as PVT, surface tension and the Joule-Thompson effect, could be adequately predicted for small molecule materials in thermodynamic equilibrium. We also learned that molecular properties such as viscosity, diffusion and thermal conductivity, could be almost predicted for small molecule materials that were not in thermodynamic equilibrium. Most importantly, however, we learned that most predictive models were very shaky when they were applied to macromolecular materials, which were almost never in thermodynamic equilibrium. In the decades since, other, braver souls have accepted the challenge of first principle prediction of polymer properties. Perhaps the best summary of the state-of-art is given by Bicerano [J. Bicerano, Prediction of Polymer Properties, 2e, Marcel Dekker, Inc., NY, 1996]. It now appears that, for simple polymers at least, we can predict, within engineering accuracy, such properties as volumetric properties, glass transition and crystalline melting temperature, heat capacity, thermal conductivity, cohesive energy, solubility parameter, surface tension, optical and electrical properties, and even mechanical properties such as structure-property relationships for glassy and rubbery polymers. And there appears to be hope in predicting polymer properties for copolymers, blends and even for filled and plasticized polymers. The question remains, however, as to whether these predictive properties are sufficient for computer analyses. Insofar as I can tell, the biggest hurdle in using the predictive correlations offered today is that they are academic. By that I mean, in order to use the techniques, I need to input substantial information about the behavior of elements within the molecular structure, not at just one environmental condition, but at the wide range of environmental conditions dictated either by product use or the process used to make the product. In simple terms, then, the current technologies lack “user friendliness”, forcing the design engineer to spend his time on molecular design instead of product design.

Measurement

If you can’t predict it, at least within the accuracy needed to meet QC or customer demands, then you need to measure it. In a recent conference, Dr. Shastri of Dow outlined not only the polymer performance criteria needed by computer-aided engineering [CAE] programs, but also the cost involved in obtaining the requisite data [R.K. Shastri, “The ISO Guide on Design Data for Plastics”, paper presented at SPE Plastics Product Design and Development Forum, 31 May-2 June 1998, Chicago IL]. Dr. Shastri is one of the leaders in ASTM D 20.10.24 subcommittee on Engineering Properties and Design. This ASTM subcommittee has issued D 5592-94, “Guide for Material Properties Needed in Engineering Design Using Plastics”. The following table identifies material property needs currently required for commercial CAE programs:

Table 1 – Polymer Material Property Needs

Mechanical Properties
Elastic modulus * Poisson’s ratio *
Bulk modulus Uniaxial compression
Shear modulus Creep data
Fatigue data Fracture strength
Inelastic strain rate Tension cracking stress
Tension softening modulus Crushing compression strain
Shear retention factor

* also as function of anisotropy

Thermal Properties
Thermal conductivity * Specific heat *
Melt density * PVT data **
Thermal diffusivity CLTE ***
No flow/solidification temperature Glass transition temperature
Crystallization temperature Heat of fusion
Recrystallation temperature Crystallization kinetics
Emissivity

* as function of temperature and pressure
** as function of cooling rate
*** coefficient of linear thermal expansion, as function of temperature and anisotropy

Rheological Properties
Viscosity as function of pressure Viscosity model coefficients
Extensional viscosity Normal stresses
Normal stress differences G’, G”
Cure kinetics for thermosets Coefficient of friction
Longitudinal shear relaxation modulus Transverse shear relaxation modulus
Relaxation time

The enormous task of collecting and disseminating physical property data is currently a joint effort between the International Technical and Standards Advisory Committee of The Society of the Plastics Industry, Inc. [ITSAC/SPI] and CAMPUS, a worldwide consortium of polymer resin suppliers. As Dr. Shastri points out, there are two huge barriers to this task. The first lies directly with the user of the information. And the second deals with the incredible cost of generating requisite data.

The Uninformed User

As Dr. Shastri points out, designers often have misconceptions about which properties are essential for the design of their products and are often confused by the lack of a standardized reporting format for the data that are available. In the first case, designers often have limited knowledge about plastics in general and the plastic they want to use, in particular. Furthermore, they frequently lack the skills to translate the “handbook” or “data sheet” values into product performance requirements, and vice versa. To complicate matters, CAE code designers frequently require physical properties that are unmeasurable, academic, or just plain irrelevant. And to compound this, CAE code designers do not provide sufficient “sensitivity analyses”, so that the product designer can determine what levels of accuracy are required for the requisite physical properties. Unfortunately, because of these barriers, the designer frequently will select polymers with properties that approximate those of traditional materials, will over-design with over-generous safety factors, or will request inordinate amounts of information from resin suppliers, with the mistaken belief that somewhere, in the mass of data, he or she will find enough information to allow the CAE program to crank away.

The Cost of Measuring

Let’s focus on the request for data from the resin supplier. Astute resin suppliers, and now CAMPUS, provide basic data on their commercial resin blends. We call that database, the “data sheet”. As we are learning, particularly in thermoforming, these data sheets have only limited use when trying to determine whether a polymer will sag in the oven. But what does it cost to generate these and other design data? According to Dr. Shastri, a consensus of seven testing facilities in the U.S., U.K, and Germany found the approximate costs given in Table 2.

Table 2 Cost of Material Data Generation

Property (Single-point data) Cost Range Per Grade Average Cost
Mechanical properties $780 – $3120 $1500
Thermal properties $1030 – $3270 $2000
Rheological properties $ 370 – $ 650 $ 500
Electrical properties $1020 – $1860 $1500
Other properties $ 170 – $ 540 $300
$5800
Property (Multi-point data)
[viz, creep, stress-strain]
Average Cost
$14,484 – $93,140

In other words, it costs nearly $6000 to achieve a relatively rudimentary database for one grade of one polymer. When you consider that there are perhaps 20,000 grades of polymers in use worldwide, you can clearly see the prohibitive cost involved. And that’s just to generate some very basic data.

So, keep in mind that when the thermoformer [or the consultant, urging on the thermoformer] decides he or she needs temperature-dependent stress-strain data for several grades of polymer, in order to determine how each of these grades performs during an FEA wall thickness calculation, he or she is asking the resin supplier to generate data that lie outside the normal measuring protocol. Of course, the resin supplier would be more than happy to do this for a customer, if there is financial incentive to do so. In other words, if you’re going to buy a few million pounds of his stuff, he’ll do it. If you’re buying a few thousand pounds, forget it!

In Short, Then

And, folks, in a nutshell, that’s why we in the thermoforming community are having a really tough time getting relevant processing data for our polymers.

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