Optimization of the Turning Process by Means of Machine Learning Using Published Data

  1. de Arriba-Pérez, Francisco
  2. García-Méndez, Silvia
  3. Carou, Diego
  4. Medina-Sánchez, Gustavo
Libro:
Materials Forming, Machining and Tribology

ISSN: 2195-0911,  2195-092X

ISBN: 9783031484674,  9783031484681

Año de publicación: 2024

Páginas: 273-287

Tipo: Capítulo de Libro

DOI: 10.1007/978-3-031-48468-1_13 GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

Machining parameters play a critical role in the results of the turning process: cutting forces, dimensional accuracy, surface roughness, tool wear, etc. Manufacturers offer recommendations for their tools, but the complex relations between machining parameters make the process optimization process not straightforward. Researchers usually opt for performing experimental studies to optimize specific or multiple outputs of these processes. However, this approach is costly and time-consuming. Thus, in the present chapter, a methodology leveraging Machine Learning is introduced, capitalizing on extensive volumes of published data within the literature. Particularly, the chapter aims to study the surface roughness attained in turning the Ti6Al4V alloy.

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