Feature extraction on machined surface texture image of tool wear based on fractional brown motion

2015 ◽  
Author(s):  
Chao Peng ◽  
Jian-Ming Zheng ◽  
Xu-Bo Li ◽  
Yan-Chao Song ◽  
Jiao-Jiao Shi
Keyword(s):  
Feature Extraction ◽  
Tool Wear ◽  
Surface Texture ◽  
Texture Image ◽  
Machined Surface ◽  
Brown Motion

Influence of milling toolpaths in machining of the turbine blade

2019 ◽  
Vol 91 (10) ◽  
pp. 1327-1339
Author(s):  
Seyedamin Jarolmasjed ◽  
Behnam Davoodi ◽  
Babak Pourebrahim Alamdari
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Turbine Blade ◽  
Tool Life ◽  
Surface Texture ◽  
Turbine Blades ◽  
Measuring System ◽  
Machining Processes ◽  
Machined Surface ◽  
Content Type

Purpose The purpose of this paper is to machine the pressure surface of the turbine blade made of A286 iron-based superalloy by using four directions of raster strategy, including horizontal upward, horizontal downward, vertical upward and vertical downward, to achieve appropriate surface roughness and to investigate the tool wear in each strategy. Design/methodology/approach In this study, all cutting tests were performed by DAHLIH-MCV 1020 BA vertical 3-axis machining center with ball nose end mill. After milling by each strategy, according to the surface slope, the surface was divided into 27 meshes, and roughness of surface was studied and compared. Roughness measuring after machining was implemented by using portable Mahr ps1 roughness tester, and surface texture was photographed by CCD 100× optical zoom camera. Also, to measure tool flank wear in each strategy as an indication of tool life, the surface of workpiece was divided into four equal areas. The wear of the inserts was measured by ARCS vertical non-contact measuring system at the end of each area. Findings The results indicate that cutting directions and toolpath strategies have significant influence on tool wear and surface roughness in machining processes and that they can be taken into consideration individually as determinative parameters. In this case, the most uniform surface texture and the lowest surface roughness are obtained by using horizontal downward direction; in addition, abrasion is a dominant tool wear mechanism in all experiments, and tool wear in the horizontal downward is lower than other strategies. Practical implications Machining of turbine blades or other airfoil-shaped workpieces is quite common in manufacturing aerospace and aircraft products. The results of this research contribute to increasing quality of machined surface and tool life in machining of turbine blade. Originality/value This work proves the significance of milling strategies in machining of the turbine blade made of A286 superalloy and, consequently, exhibits the proper strategy in terms of surface roughness and tool life. Also, this work explains and elaborates the behavior of A286 superalloy in machining processes, which has not been studied much in recent research works.

scholarly journals Influence of turning tool wear on the surface integrity and anti-fatigue behavior of Ti1023

2021 ◽  
Vol 13 (4) ◽  
pp. 168781402110112
Author(s):  
Li Xun ◽  
Wang Ziming ◽  
Yang Shenliang ◽  
Guo Zhiyuan ◽  
Zhou Yongxin ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Plastic Deformation ◽  
Titanium Alloy ◽  
Fatigue Life ◽  
Tool Wear ◽  
Surface Integrity ◽  
Low Cycle Fatigue ◽  
Fatigue Behavior ◽  
Machined Surface ◽  
Deformation Layer

Titanium alloy Ti1023 is a typical difficult-to-cut material. Tool wear is easy to occur in machining Ti1023, which has a significant negative effect on surface integrity. Turning is one of the common methods to machine Ti1023 parts and machined surface integrity has a direct influence on the fatigue life of parts. To control surface integrity and improve anti-fatigue behavior of Ti1023 parts, it has an important significance to study the influence of tool wear on the surface integrity and fatigue life of Ti1023 in turning. Therefore, the effect of tool wear on the surface roughness, microhardness, residual stress, and plastic deformation layer of Ti1023 workpieces by turning and low-cycle fatigue tests were studied. Meanwhile, the influence mechanism of surface integrity on anti-fatigue behavior also was analyzed. The experimental results show that the change of surface roughness caused by worn tools has the most influence on anti-fatigue behavior when the tool wear VB is from 0.05 to 0.25 mm. On the other hand, the plastic deformation layer on the machined surface could properly improve the anti-fatigue behavior of specimens that were proved in the experiments. However, the higher surface roughness and significant surface defects on surface machined utilizing the worn tool with VB = 0.30 mm, which leads the anti-fatigue behavior of specimens to decrease sharply. Therefore, to ensure the anti-fatigue behavior of parts, the value of turning tool wear VB must be rigorously controlled under 0.30 mm during finishing machining of titanium alloy Ti1023.

COMPARISON OF PREPARATION METHODS IN TERMS OF TOOL LIFE AND SURFACE ROUGHNESS

2021 ◽  
Vol 2021 (4) ◽  
pp. 4836-4840
Author(s):  
ROBERT STRAKA ◽  
◽  
JOZEF PETERKA ◽  
TOMAS VOPAT ◽  
◽  
...  
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Cutting Parameters ◽  
Turning Process ◽  
Machined Surface ◽  
Preparation Methods ◽  
Machined Surface Roughness ◽  
Cutting Inserts ◽  
Drag Finishing ◽  
Edge Preparation

The article compares two cutting edge preparation methods and their influence on the machined surface roughness of the difficult to cut nickel alloy Inconel 718 and the tool wear of cutting inserts made of cemented carbide. The manufacturing and preparation process of cutting inserts used in the experiment were made by Dormer Pramet. The preparation methods used in the experiment were drag finishing and brushing. Cutting parameters did not change during the whole turning process to maintain the same conditions in each step of the process and were determined based on tests for a semi-finishing operation of the turning process. To obtain durability of 25 to 30 minutes with controlled development of the tool wear the cutting parameters were determined with cooperation with the cutting inserts manufacturer.

Research on Tool Wear Based on Texture Fractal Dimension

2011 ◽  
Vol 66-68 ◽  
pp. 1163-1166
Author(s):  
Mao Jun Chen ◽  
Zhong Jin Ni ◽  
Liang Fang
Keyword(s):  
Fractal Dimension ◽  
Tool Wear ◽  
Surface Texture ◽  
Manufacturing Systems ◽  
Experimental Results ◽  
Machining Process ◽  
Work Piece ◽  
Automated Manufacturing ◽  
Automated Manufacturing Systems ◽  
Wear Condition

In automated manufacturing systems, one of the most important issues is the detection of tool wear during the machining process. The Hausdorff-Besicovitch (HB) dimension is used to analyze the feature of the surface texture of work-piece in this paper. The value of the fractal dimension of the work-piece surface texture tends to decrease with the machining process, due to the texture becoming more complex and irregular, and the tool wear is also becoming more and more serious. That can describe the inherent relationship between work-piece surface texture and tool wear. The experimental results demonstrate the probability of using the fractal dimension of work-piece surface texture to monitor the tool wear condition.

scholarly journals Feature extraction-based prediction of tool wear of Inconel 718 in face turning

2018 ◽  
Vol 60 (8) ◽  
pp. 443-450 ◽  
Author(s):  
M Murua ◽  
A Suárez ◽  
L N López de Lacalle ◽  
R Santana ◽  
A Wretland
Keyword(s):  
Feature Extraction ◽  
Tool Wear ◽  
Inconel 718

scholarly journals A cluster-based feature selection method for image texture classification

2019 ◽  
Vol 14 (3) ◽  
pp. 1433 ◽  
Author(s):  
Abbas F. H. Alharan ◽  
Hayder K. Fatlawi ◽  
Nabeel Salih Ali
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Clustering Algorithm ◽  
Gabor Filter ◽  
Texture Feature ◽  
Image Feature ◽  
Image Texture ◽  
Support Vector ◽  
Texture Image ◽  
Texture Feature Extraction

<p>Computer vision and pattern recognition applications have been counted serious research trends in engineering technology and scientific research content. These applications such as texture image analysis and its texture feature extraction. Several studies have been done to obtain accurate results in image feature extraction and classifications, but most of the extraction and classification studies have some shortcomings. Thus, it is substantial to amend the accuracy of the classification via minify the dimension of feature sets. In this paper, presents a cluster-based feature selection approach to adopt more discriminative subset texture features based on three different texture image datasets. Multi-step are conducted to implement the proposed approach. These steps involve texture feature extraction via Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter. The second step is feature selection by using K-means clustering algorithm based on five feature evaluation metrics which are infogain, Gain ratio, oneR, ReliefF, and symmetric. Finally, K-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers are used to evaluate the proposed classification performance and accuracy. Research achieved better classification accuracy and performance using KNN and NB classifiers that were 99.9554% for Kelberg dataset and 99.0625% for SVM in Brodatz-1 and Brodatz-2 datasets consecutively. Conduct a comparison to other studies to give a unified view of the quality of the results and identify the future research directions.</p>

scholarly journals Multi-dimensional Assessment of Precision Machined Surface Texture Based on Laser Speckle Pattern Analysis

Procedia CIRP ◽  
2015 ◽  
Vol 33 ◽  
pp. 251-256 ◽  
Author(s):  
Motochika Shimizu ◽  
Hiroshi Sawano ◽  
Hayato Yoshioka ◽  
Hidenori Shinno
Keyword(s):  
Surface Texture ◽  
Pattern Analysis ◽  
Speckle Pattern ◽  
Laser Speckle ◽  
Machined Surface ◽  
Dimensional Assessment
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