Tool-Wear Monitoring Based on Continuous Hidden Markov Models

By Antonio Vallejo Jr., Juan Arturo Nolazco-Flores, Rubén Morales-Menéndez, Luis Enrique Sucar, Ciro A. Rodríguez. 2005

38 citations recorded by the source corpus.

Subfields: Mechanical Engineering, Mechanics of Materials, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Mechanical Engineering

1. Octavio Loyola-González
2. J. V. Abellán-Nebot
3. Fernando Romero Subirón

In Mechanics of Materials

1. Octavio Loyola-González
2. J. V. Abellán-Nebot
3. Fernando Romero Subirón

In Signal Processing

1. Octavio Loyola-González

Citations in the corpus

• Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, methodology): “Markov network-based models have been used in different applied context, such as financial engineering [72], image annotation [73], social network analysis tools in ecology [74], and tool-wear monitoring [75]; among others.”
• A review of machining monitoring systems based on artificial intelligence process models (2010, methodology): “Other AI approaches such as BN [22, 48], hidden Markov models [ 69 , 75], evolutionary algorithms [76, 77] or support vector machines [78] Int J Adv Manuf Technol (2010) 47:237–257 253”