We look at color stability of our engineering materials. Drift of color values over time are quantified. A mid-large dataset is available, but it is challenging to create an accurate predictive model. What additional data, should be generated to get to improved predictivity? Classical DoE's are too extensive and we want to make use of all historical data available. What data is missing and can we make data/AI driven decisions on what additional experiments to do?
Color stability testing is time-consuming increasing lead times. If we can predict stability, we can gain in lead time and save efforts and costs.
Concepts for approach to get to decisions for additional measurements.
Envalior is a global leader in Engineering Materials, with a focus on innovative and sustainable solutions that enable future-proof product designs. Envalior supports customers all the way from the development of new materials to application concept development, product design, material processing and part testing.
Envalior's global presence spans across various continents with a network of 18 production sites, 14 R&D centers and multiple sales offices. These sites are home to Envalior's diverse, engaged and skilled workforce, who innovate, engineer, and produce the company's wide range of material solutions.
By co-developing along the value chain from polymer and compound production through to collaboration with molders, system suppliers and OEMs, Envalior supports a wide variety of industries.
A unique company feature is Envalior’s high level of backward integration of production processes. Envalior produces both high-performance engineering materials and relevant raw materials and resins, securing the supply chain and reducing dependency on external suppliers. Envalior’s product portfolio consists of a wide variety of products brands.
