Even without physical data, the simulation can still be useful for comparing the design, material, and process. This creates a foundation of trust in the data outputs. If you are used to designing in metals, you might be surprised when a polymer’s technical data sheet does not translate into part performance you anticipated.

Find Data Technical Data Sheets Find material data

A guide for describing, evaluating, and reporting environmental, social, and governance (ESG) data Social-related data reported to stakeholders rose 37% in 2021 compared to 2020 (McKinsey & Company). Interested in learning more about your ESG data and how to achieve your sustainability goals?

A guide for describing, evaluating, and reporting environmental, social, and governance (ESG) data Social-related data reported to stakeholders rose 37% in 2021 compared to 2020 (McKinsey & Company). Interested in learning more about your ESG data and how to achieve your sustainability goals?

Find Data Technical Data Sheets Find material data

Predictive simulation technology uses conventional (isotropic) and advanced (anisotropic) modeling data from material characterizations, mold filling analysis, and finite element analysis (FEA) to more accurately forecast performance and potential failure points. By factoring in static and dynamic data, the CAE simulations can identify where an injection molded plastic part may crack or break when exposed to real-world conditions such as physical loading force, sudden impact (crash simulation), or environmental factors like temperature and humidity.   For more technical details on the structural performance of reinforced polymers using CAE, automotive design engineers may access material data and models for Avient’s fiber-reinforced solutions using Hexagon’s Digimat-MX Tool.

Predictive simulation technology uses conventional (isotropic) and advanced (anisotropic) modeling data from material characterizations, mold filling analysis, and finite element analysis (FEA) to more accurately forecast performance and potential failure points. By factoring in static and dynamic data, the CAE simulations can identify where an injection molded plastic part may crack or break when exposed to real-world conditions such as physical loading force, sudden impact (crash simulation), or environmental factors like temperature and humidity.   For more technical details on the structural performance of reinforced polymers using CAE, automotive design engineers may access material data and models for Avient’s fiber-reinforced solutions using Hexagon’s Digimat-MX Tool.

Refer to the individual Technical Data Sheet for drying recommendations for specific grades. For a specific overmolding grade, follow the color carrier recommendations on the individual Technical Data Sheets. Refer to the Technical Data Sheet for the chosen grade to determine barrel temperature settings.

Refer to the individual Technical Data Sheet for drying recommendations for specific grades. For a specific overmolding grade, follow the color carrier recommendations on the individual Technical Data Sheets. Refer to the Technical Data Sheet for the chosen grade to determine barrel temperature settings.

To monitor progress against these goals, we track waste data from each site on a monthly basis. Beyond quantities of waste generated, this monthly data includes information on positive and negative influences that impact performance.