Sensors selection for tool failure detection during machining processes: A simple accurate classification model

2021 ◽  
Vol 32 ◽  
pp. 108-119
Author(s):  
Mohamed Abubakr ◽  
Muhammed A. Hassan ◽  
Grzegorz M. Krolczyk ◽  
Navneet Khanna ◽  
Hussien Hegab
Keyword(s):  
Failure Detection ◽  
Classification Model ◽  
Machining Processes ◽  
Tool Failure ◽  
Selection For

WC-Co Tool Failure Analysis and the Grinding Effect Study

2010 ◽  
Vol 139-141 ◽  
pp. 269-273 ◽  
Author(s):  
Xiu Xu Zhao
Keyword(s):  
Residual Stress ◽  
Effect Study ◽  
Work Piece ◽  
Grinding Process ◽  
X Ray Diffraction ◽  
Machining Processes ◽  
Tool Failure ◽  
Micro Cracks ◽  
Comparison Results ◽  
Grinding Conditions

Grinding is one of the important machining processes for the WC-Co carbide product. Different grinding strategies will have different impact on the work piece material. This study focuses on the WC-Co carbide grinding process, and the effect of grinding condition on the WC-Co carbide microstructure, figures out the relationship between different grinding strategies and material microstructure which relate to the WC-Co carbide tool failure. A specific microstructure analysis with Scanning Electric Microscope (SEM) will be presented based on a series of grinding experiments. The residual stress that generated in the grinding process will also be discussed based on the X-Ray Diffraction (XRD) measurements. It has been found that micro cracks are generated at certain grinding conditions with certain level. The residual stress which generated in the grinding process can be calculated by the d-spacing shift, and the comparison results show micro-cracks level is corresponding with the peaks shift in XRD test.

New Methods for Tool Failure Detection in Micromilling

2006 ◽  
Vol 220 (2) ◽  
pp. 137-144 ◽  
Author(s):  
E Gandarias ◽  
S Dimov ◽  
D T Pham ◽  
A Ivanov ◽  
K Popov ◽  
...  
Keyword(s):  
Failure Detection ◽  
Tool Failure ◽  
New Methods

In-process tool-failure detection by means of AR models

1997 ◽  
Vol 13 (2) ◽  
pp. 86-94 ◽  
Author(s):  
A. Lombardo ◽  
A. Masnata ◽  
L. Settineri
Keyword(s):  
Failure Detection ◽  
Tool Failure ◽  
Ar Models

scholarly journals An Investigation of the Adhesive Effect on the Flank Wear Properties of a WC/Co-based TiAlN-Coated Tool for Milling a Be/Cu Alloy

Metals ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 444
Author(s):  
Zuo ◽  
Lin ◽  
He
Keyword(s):  
Failure Process ◽  
Cutting Speed ◽  
Cutting Process ◽  
Tool Geometry ◽  
Machining Processes ◽  
Tool Failure ◽  
Force Fluctuations ◽  
Adhesive Effect ◽  
The Impact ◽  
Peak Value

Abstract: In this paper, an experimental investigation, based on force special parameters, is adopted to analyze the relationship between the milling tool and adhesive phenomena in milling C17200. Generally speaking, the adhesive characteristics, force fluctuations, and tool failure are the main factors affecting the impact of the cutting process on tool wear patterns. However, difficult-to-cut materials, such as the beryllium–copper alloy C17200, require machining processes with tools with lives that are difficult to predict, due to their excellent mechanical properties. To analyze the tool failure process, a series of experiments based on cutting speed and tool geometry are presented in this paper to observe the adhesive effect on tool flank surfaces and force fluctuations. The results show that the variation of special force parameters in different directions reveals that the thermal–mechanical effect on sticking substances reached a possible peak value, with inflection points in different parameters at 200 m/min. The sticking substances and tool surfaces (observed by energy disperse spectroscopy and scanning electron microscope), wear capacity, and back-scattered electron imaging also confirmed that adhesion in the wear zone reached a peak value at 200 m/min in the cutting process, exacerbating the adhesive effect on tool failure.

scholarly journals GENETIC DIVERGENCE IN POPULATIONS OF Caryocar brasiliense Camb. FROM THE PHYSICAL CHARACTERISTICS OF THE FRUITS

2018 ◽  
Vol 42 (1) ◽  
Author(s):  
Paulo Henrique Rodrigues dos Santos ◽  
Samuel Cunha Oliveira Giordani ◽  
Blenda Calazans Soares ◽  
Fernando Higino Lima e Silva ◽  
Elizabethe Adriana Esteves ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Phenotypic Variability ◽  
Hierarchical Classification ◽  
Physical Characteristics ◽  
Classification Model ◽  
Caryocar Brasiliense ◽  
Selection For ◽  
Main Components ◽  
Two Populations ◽  
Selection Of

ABSTRACT The study of the genetic and phenotypic variability of the populations of Caryocar brasiliense Camb. assists in strategies for conservation and breeding of the specie. The objective of this work was to characterize the genetic diversity through the physical characteristics of pequi fruits of two populations through uni and multivariate analysis, as well as the implications in their domestication and breeding. Fruits were harvested from 20 mother trees (ten fruits per mother tree) from two populations in the state of Minas Gerais (Curvelo and São Gonçalo do Rio Preto Municipalities) during the years 2010, 2011 and 2012. The data were submitted to analysis of variance using a hierarchical classification model. An analysis of clusters and main components was used for studying the divergences. The effects of the populations were not significant for all the characters analyzed in isolation. The gathering of pequi mother trees shown by multivariate analysis did not correspond to the grouping established by the populations. Selection for breeding and/or preservation purposes should concentrate efforts on fewer populations with larger numbers of mother trees per population. Selection of mother trees with higher fruits and thicker pulp fruits among them, even by visual evaluations only, is recommend in sampling mother trees for breeding purposes.

scholarly journals Feature Selection for Phishing Detection with Machine Learning

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1039-1045
Keyword(s):  
Machine Learning ◽  
Classification Model ◽  
Feature Ranking ◽  
Great Success ◽  
Machine Learning Model ◽  
Selection For ◽  
Good Set ◽  
Depth Feature ◽  
Phishing Detection ◽  
Ranking Analysis

In the last 10 years machine learning has been widely used to combat phishing attacks. The most common approach was to build a classification model that would be able to detect whether or not a given URL or website is a phishing attack. In order to effectively detect a phishing page with machine learning we must find an effective method to represent websites (both phish and benign) as features which can be fed into a machine learning model. One of the challenges faced by these approaches was to find a good set of features to represent the phishing and benign sites. Within the last 10 years hundreds of different features had been proposed and used to great success [6] [7] [9]. However, due to the curse of dimensionality, use of all available features will exponentially increase the sparsity of the dataset, lowering the odds of successful classification. In this work we extract 31 features that had been commonly used in the literature and perform an in depth feature ranking analysis in order to find the most effective features for phishing detection. Using both filter and wrapping methods we were able to find 23 effective features for phishing detection. The F1-score for all 31 features was 0.88 and time taken to train the multilevel perceptron model was 45.49 seconds and the size of the data used is 100k. Using these 23 features we were able to train a model that has 0.99 F1-score and which was comparable with all previous work while reducing the overall dimensionality of the data and time taken to train the model was 43.71 seconds.

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