Sigma Labs Inc (SASI)

[truthandlove]

"Process signature data is considered the “universal translator.”
https://www.digitalengineering247.com/article/senvol-and-nist-new-project-to-establish-am-process-structure-property-relationships/

"The concept of transfer learning will be particularly useful. For example, if you have a data-set combination for one machine that is qualified to produce parts to a spec, what happens when the machine is given a new software update — do you have to requalify it? Or if you decide to make that same part on a new machine, can you transfer some of the parameters directly? With Senvol ML software’s predictive assurance, some of this rework should be unnecessary.
Simkin says that transfer learning also posits, if two machines give you equivalent process signatures (the same in-situ monitoring data), you should achieve the same material properties, and therefore the same final mechanical performance. “You’ll almost certainly use different process parameters for machine X and machine Y,” he notes, “but as long as those two different sets of parameters are yielding equivalent process signatures, that should determine the same performance.” In terms of Senvol ML’s modules 1, 2, 3 and 4 (as above), module 2 data — process signature data — is considered the “universal translator.”
glta