Due to the high degree of technologisation of production processes, especially through fast measuring and regulation algorithms, highly complex manufacturing methods, which have historically been used rather in research projects or for single parts production, can be used for batch production. All of these methods have one thing in common: their parameters are only developed during the process itself. The part production from composites, the application of functional coatings and additive manufacturing are examples of those methods.
These methods when used in batch production produce a lot of data from different areas. It all starts with data around raw materials or semi-finished products, from machinery, from process control tools and eventually quality parameters of the final products. Consequently, the data can by vastly different: from simple meta data regarding the position of a part, to parameters such as gas influence, up to pictures from electron microscopes or information from mass spectrometers.
To handle and automatically evaluate all of those types of data within one system is WIAM’s strong-suit. As WIAM® ICE can handle all types of data, any parameter can be managed with the system. The software can handle cross-influences across different systems particularly well and can consequently generate new knowledge by using AI and Machine Learning algorithms.