Example applications
Process and experimental control
A webinar with Photocentric showed how Alchemite™ is used to identify optimal process parameters for parts manufactured in an innovative high-speed digital process. The resulting models can be connected to live machines, learning and setting better process parameters as more parts are made. Similar automation can be used, for example, to modify high throughput experimentation parameters while an experiment is in progress.
On-device
Another project developed a light footprint version of an ML model to run on a chip in a consumer device. For this device, a number of inputs – for example, how frequently the device is activated, for how long, and in what orientation – combine in subtle ways to affect the user experience. The model takes inputs from the operation of the device and uses these to select the settings most likely to ensure satisfactory outputs for the user. This scenario is common in, for example, medical devices.
An interactive design tool
A leading provider of a formulated product has taken its use of Alchemite™ machine learning beyond the lab and is providing a machine learning model to its customers via a customised app. Customers can use the app to fine-tune their use of the product, modifying ingredients and process conditions for results that best match their exact application.
