scholarly journals Optimization of surface roughness and tool wear in sustainable dry turning of Iron based Nickel A286 alloy using Taguchi’s method

2021 ◽  
Vol 2 ◽  
pp. 100034
Author(s):  
M. Venkata Ramana ◽  
G. Krishna Mohana Rao ◽  
Bidya Sagar ◽  
Ravi Kumar Panthangi ◽  
B.V. Raja Ravi Kumar
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Taguchi’S Method ◽  
Dry Turning ◽  
Iron Based

Modeling and optimization of machining parameters for minimizing surface roughness and tool wear during AISI 52100 steel dry turning

2021 ◽  
Author(s):  
Uma Maheshwera Reddy Paturi ◽  
Ankathi Yash ◽  
Sai Teja Palakurthy ◽  
N.S. Reddy
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Machining Parameters ◽  
Dry Turning ◽  
Modeling And Optimization ◽  
Aisi 52100 ◽  
Aisi 52100 Steel

scholarly journals An Investigation in Analysis of Dry Turning in MQL Method for Predicting Tool Wear and to Improve Surface Roughness

2016 ◽  
Vol 9 (36) ◽  
Author(s):  
G. Ragul ◽  
S. Naveen Kumar ◽  
G. Kalivarathan ◽  
V. Jayakumar ◽  
C. P. Praveen ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Dry Turning

Analysis of secondary adhesion tool wear effects on surface roughness in dry turning process of UNS A92024 aluminium alloy

2017 ◽  
Vol 10 (1) ◽  
pp. 23
Author(s):  
Daniel Garcia Jurado ◽  
Juan Manuel Vázquez Martínez ◽  
Antonio J. Gámez ◽  
M. Batista ◽  
F.J. Puerta ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Aluminium Alloy ◽  
Turning Process ◽  
Dry Turning

Analysis of secondary adhesion tool wear effects on surface roughness in dry turning process of UNS A92024 aluminium alloy

2017 ◽  
Vol 10 (1) ◽  
pp. 23
Author(s):  
M. Marcos ◽  
F.J. Puerta ◽  
M. Batista ◽  
Antonio J. Gámez ◽  
Daniel Garcia Jurado ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Aluminium Alloy ◽  
Turning Process ◽  
Dry Turning

Study on the Cutting Properties about Magnesium Alloy when Dry Turning with Kentanium Cutting Tools

2010 ◽  
Vol 102-104 ◽  
pp. 653-657 ◽  
Author(s):  
Xu Hong Guo ◽  
Li Jun Teng ◽  
Wei Wang ◽  
Ting Ting Chen
Keyword(s):  
Surface Roughness ◽  
Magnesium Alloy ◽  
Tool Wear ◽  
Cutting Force ◽  
Feed Rate ◽  
Cutting Tools ◽  
Cutting Speed ◽  
Cutting Depth ◽  
Dry Turning ◽  
Main Influence

In recent years, the machinability of magnesium alloy is concerned more and more by the public. In this paper, a study on the cutting properties of magnesium alloy AZ91D when dry turning with kentanium cutting tools is presented. It shows the cutting force measured by a data acquisition system which is made up of Kistler9257B piezoelectric crystal sensor dynamometer, Kistler5070A10100 charge amplifier and computer. The effect of cutting parameters on cutting force was studied, and the experimental formula was built. The tool wear and chip characteristics were observed with KYKY-EM3200 electron scanning microscope and EDAX PV9900 alpha ray spectrometer, while the surface roughness of the workpiece was measured with 2205 profilometer. Results showed that the cutting depth was the main influence factor on cutting force, followed by feed rate and cutting speed . The main form of tool wear showed to be diffusive wear and adhesive wear. The feed rate had the main influence on chip form and the workpiece surface roughness, cutting speed was less effective, the cutting depth was the least.

Effect of SiC-Cu Electrode on Material Removal Rate, Tool Wear and Surface Roughness in EDM Process

2020 ◽  
Vol 38 (9A) ◽  
pp. 1406-1413
Author(s):  
Yousif Q. Laibia ◽  
Saad K. Shather
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Material Removal Rate ◽  
Material Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
304 Stainless Steel ◽  
Tool Electrode ◽  
Pulse Time ◽  
Edm Process

Electrical discharge machining (EDM) is one of the most common non-traditional processes for the manufacture of high precision parts and complex shapes. The EDM process depends on the heat energy between the work material and the tool electrode. This study focused on the material removal rate (MRR), the surface roughness, and tool wear in a 304 stainless steel EDM. The composite electrode consisted of copper (Cu) and silicon carbide (SiC). The current effects imposed on the working material, as well as the pulses that change over time during the experiment. When the current used is (8, 5, 3, 2, 1.5) A, the pulse time used is (12, 25) μs and the size of the space used is (1) mm. Optimum surface roughness under a current of 1.5 A and the pulse time of 25 μs with a maximum MRR of 8 A and the pulse duration of 25 μs.

scholarly journals Deep Learning Approach for Vibration Signals Applications

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3929
Author(s):  
Han-Yun Chen ◽  
Ching-Hung Lee
Keyword(s):  
Surface Roughness ◽  
Tool Wear ◽  
Parameters Optimization ◽  
Frequency Spectra ◽  
Vibration Signals ◽  
Bearing Faults ◽  
Time Frequency ◽  
And Performance ◽  
The One ◽  
Short Time

This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach.

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.

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