Estimation of tool wear during CNC milling using neural network-based sensor fusion

By N. Ghosh, Y. Ravi, A. Patra, S. Mukhopadhyay, S. Paul, A. Mohanty, A. B. Chattopadhyay. 2007

409 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Electrical and Electronic Engineering, Mechanical Engineering

How researchers see this paper

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

In Biomedical Engineering

In Electrical and Electronic Engineering

In Mechanical Engineering

Citations in the corpus

A survey of data fusion in smart city applications (2019, background): “Thus, preventive maintenance has been studied in [124], [125], [175], [176] and attempts to predict the remaining useful life (RUL) of a machine accurately.”
Cloud-enabled prognosis for manufacturing (2015, methodology): “Ghosh et al. [74] adopted a MLP network to fuse features extracted from a number of machining zone signals to estimate the average flank wear of the main cutting edge of the cutting tool.”
Big Data in product lifecycle management (2015, methodology): “analysis (SSA) [75], Adaptive Neuro fuzzy Inference system (ANFIS) [76], and neural network-based sensor fusion [77].”
Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review (2022, methodology): “Ghosh et al. (2007) applied the multi-sensor model based on NN for tool wear monitoring (Ghosh et al., 2007).”
A review of machining monitoring systems based on artificial intelligence process models (2010, methodology): “An appropriate signal processing strategy is mandatory before selecting/extracting sensory features due to the high levels of mechanical, electrical and acoustic noises in industrial environments [ 39 ].”
Tool condition monitoring techniques in milling process — a review (2020, methodology): “85 s with the tool wear prediction error of less than 25 m [51].”
Tool wear predictability estimation in milling based on multi-sensorial data (2016, background): “Study [38] indicates that less costly andmountable current and voltage sensors may offer an alternative to force sensors for industrial tool condition monitoring.”
Application of soft computing techniques in machining performance prediction and optimization: a literature review (2010, methodology): “[78] developed a neural networkbased sensor fusion model to estimate tool wear during CNC milling process.”