Neural Networks

Tan, Pang-Ning

doi:10.1007/978-1-4614-8265-9_560

Cited by 1 publication

(1 citation statement)

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“…ANNs are composed of three layers of nodes, called neurons, which perform operations with the data entered in the input layer, passing through a hidden layer where a nonlinear transformation of the information is performed to obtain a classification of the data in the output layer. When a network topology is configured so that the outputs from one layer are fed into the next one, it is called a feed-forward neural network (FFNN), and this is the most common configuration [44]. Additionally, ANNs can have more than one hidden layer, becoming deep neural networks (DNN), where the topology to be used is decided by empirical issues related to the applications in which they are used [45].…”

Section: Linear Discriminant Analysis (Lda)mentioning

confidence: 99%

Methodology for Tool Wear Detection in CNC Machines Based on Fusion Flux Current of Motor and Image Workpieces

et al. 2023

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In the manufacturing industry, computer numerical control (CNC) machine tools are of great importance since the processes in which they are used allow the creation of elements used in multiple sectors. Likewise, the condition of the cutting tools used is paramount due to the effect they have on the process and the quality of the supplies produced. For decades, methodologies have been developed that employ various signals and sensors for wear detection, prediction and monitoring; however, this field is constantly evolving, with new technologies and methods that have allowed the development of non-invasive, efficient and robust systems. This paper proposes the use of magnetic stray flux and motor current signals from a CNC lathe and the analysis of images of machined parts for wear detection using online and offline information under the variation in cutting speed and tool feed rate. The information obtained is processed through statistical and non-statistical indicators and dimensionally reduced by linear discriminant analysis (LDA) and a feed-forward neural network (FFNN) for wear classification. The results obtained show a good performance in wear detection using the individual signals, achieving efficiencies of 77.5%, 73% and 89.78% for the analysis of images, current and stray flux signals, respectively, under the variation in cutting speed, and 76.34%, 73% and 63.12% for the analysis of images, current and stray flux signals, respectively, under the variation of feed rate. Significant improvements were observed when the signals are fused, increasing the efficiency up to 95% for the cutting speed variations and 82.84% for the feed rate variations, achieving a system that allows detecting the wear present in the tools according to the needs of the process (online/offline) under different machining parameters.

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Section: Linear Discriminant Analysis (Lda)mentioning

confidence: 99%