DYNA JOURNAL ENGINEERING

Key words:

Torneado CNC, predicción de la rugosidad superficial, predicción de la desviación del diámetro, parámetros de corte variables, fabricación distribuida, aprendizaje profundo, CNC turning, surface roughness prediction, diameter deviation prediction, variable cutting parameters, distributed manufacturing, deep learning.

Article type:

ARTICULO DE INVESTIGACION / RESEARCH ARTICLE

Section:

RESEARCH ARTICLES

This research arises from the need identified during the COVID-19 pandemic, where supply chain disruptions highlighted the importance of developing more adaptable manufacturing systems capable of redirecting production between different sites while maintaining quality standards. For this purpose, the application of artificial intelligence has become an imperative necessity.
This work presents the development of a transferable Deep Learning model for simultaneous prediction of surface roughness (Ra) and diameter deviation (Ddev) through process-measured signals (vibrations, forces, acoustic emission, among others) in CNC machining processes, applied across four different manufacturing sites (MS1-MS4) to implement a resilient distributed manufacturing strategy.