Effects of Air Jet-assisted Little Quantity Lubrication on Surface Finish and Tool Wear in Face Milling Process of Aluminium 7050-T7451

Monitoring of tool wear in machining process has found its importance to predict tool life, reduce equipment downtime, and tool costs. Traditional visual methods require expert experience and human resources to obtain accurate tool wear information. With the development of charge-coupled device (CCD) image sensor and the deep learning algorithms, it has become possible to use the convolutional neural network (CNN) model to automatically identify the wear types of high-temperature alloy tools in the face milling process. In this paper, the CNN model is developed based on our image dataset. The convolutional automatic encoder (CAE) is used to pre-train the network model, and the model parameters are fine-tuned by back propagation (BP) algorithm combined with stochastic gradient descent (SGD) algorithm. The established ToolWearnet network model has the function of identifying the tool wear types. The experimental results show that the average recognition precision rate of the model can reach 96.20%. At the same time, the automatic detection algorithm of tool wear value is improved by combining the identified tool wear types. In order to verify the feasibility of the method, an experimental system is built on the machine tool. By matching the frame rate of the industrial camera and the machine tool spindle speed, the wear image information of all the inserts can be obtained in the machining gap. The automatic detection method of tool wear value is compared with the result of manual detection by high precision digital optical microscope, the mean absolute percentage error is 4.76%, which effectively verifies the effectiveness and practicality of the method.

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Analysis of tool wear and surface roughness in high-speed milling process of aluminum alloy Al6061

EUREKA Physics and Engineering ◽

10.21303/2461-4262.2021.001824 ◽

2021 ◽

pp. 71-84

Author(s):

Nhu-Tung Nguyen ◽

Dung Hoang Tien ◽

Nguyen Tien Tung ◽

Nguyen Duc Luan

Keyword(s):

Surface Roughness ◽

Aluminum Alloy ◽

Tool Wear ◽

Feed Rate ◽

High Speed ◽

Cutting Speed ◽

Milling Process ◽

Face Milling ◽

Depth Of Cut ◽

High Speed Milling

In this study, the influence of cutting parameters and machining time on the tool wear and surface roughness was investigated in high-speed milling process of Al6061 using face carbide inserts. Taguchi experimental matrix (L9) was chosen to design and conduct the experimental research with three input parameters (feed rate, cutting speed, and axial depth of cut). Tool wear (VB) and surface roughness (Ra) after different machining strokes (after 10, 30, and 50 machining strokes) were selected as the output parameters. In almost cases of high-speed face milling process, the most significant factor that influenced on the tool wear was cutting speed (84.94 % after 10 machining strokes, 52.13 % after 30 machining strokes, and 68.58 % after 50 machining strokes), and the most significant factors that influenced on the surface roughness were depth of cut and feed rate (70.54 % after 10 machining strokes, 43.28 % after 30 machining strokes, and 30.97 % after 50 machining strokes for depth of cut. And 22.01 % after 10 machining strokes, 44.39 % after 30 machining strokes, and 66.58 % after 50 machining strokes for feed rate). Linear regression was the most suitable regression of VB and Ra with the determination coefficients (R2) from 88.00 % to 91.99 % for VB, and from 90.24 % to 96.84 % for Ra. These regression models were successfully verified by comparison between predicted and measured results of VB and Ra. Besides, the relationship of VB, Ra, and different machining strokes was also investigated and evaluated. Tool wear, surface roughness models, and their relationship that were found in this study can be used to improve the surface quality and reduce the tool wear in the high-speed face milling of aluminum alloy Al6061

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The Study of Surface Finish in Face Milling of Stainless Steel: AISI 304

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.650.606 ◽

2013 ◽

Vol 650 ◽

pp. 606-611 ◽

Cited By ~ 4

Author(s):

Songsak Luejanda ◽

Komson Jirapattarasilp

Keyword(s):

Stainless Steel ◽

Surface Finish ◽

Feed Rate ◽

Cutting Speed ◽

Milling Process ◽

Face Milling ◽

Depth Of Cut ◽

Aisi 304 ◽

Steel Aisi ◽

Stainless Steel Aisi

This research was to study the effect of face milling on the surface finish of stainless steel: AISI 304. The experiment was applied on three factors and were consisted of three levels of cutting speed, depth of cut and feed rate. The face milling process was chosen to experiment which used face milling cutter with insert carbide tool. The surface roughness average (Ra) was applied to indicating for surface finish. The experiment results were analyzed by ANOVA. The main factors and factors interaction that affected to surface finish were investigated. Effect of cutting speed, feed rate and depth of cut on surface finish of stainless steel: AISI 304 was discussed.

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